diff --git a/404.html b/404.html
index c291c9b1..21f82365 100644
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-        <a href="https://epiverse-trace.github.io/tutorials/introduction.html" class="external-link">1. Outbreak analytics pipelines</a>
+        <a href="https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html" class="external-link">1. Quantifying transmission</a>
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@@ -166,29 +166,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-        <a href="https://epiverse-trace.github.io/tutorials/read-delays.html" class="external-link">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
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-<!--/div.accordion-item-->
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-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html" class="external-link">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
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-<div class="accordion accordion-flush" id="accordionFlush5">
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-        <a href="https://epiverse-trace.github.io/tutorials/create-forecast.html" class="external-link">4. Create a short-term forecast</a>
+        <a href="https://epiverse-trace.github.io/tutorials/create-forecast.html" class="external-link">2. Create a short-term forecast</a>
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@@ -301,9 +279,9 @@ <h1>Page not found</h1>
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   "url": "https://epiverse-trace.github.io/tutorials/404.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/404.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
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index b70aac61..439b4e90 100644
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
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-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div><!--/div.accordion-header-->
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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@@ -244,7 +226,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
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       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Contributor Code of Conduct</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/CODE_OF_CONDUCT.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -327,9 +309,9 @@ <h1>Contributor Code of Conduct</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/CODE_OF_CONDUCT.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/CODE_OF_CONDUCT.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/LICENSE.html b/LICENSE.html
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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@@ -244,7 +226,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Licenses</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/LICENSE.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -375,9 +357,9 @@ <h1>Licenses</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/LICENSE.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/LICENSE.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
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diff --git a/aio.html b/aio.html
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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-        <a href="read-delays.html">2. Read delays</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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-  <div class="accordion-item">
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-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
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-                </div>
-              </div>
-            </div>
-
-            <hr class="half-width">
-<div class="accordion accordion-flush resources" id="accordionFlush12">
-              <div class="accordion-item">
-                <h2 class="accordion-header" id="flush-headingTwelve">
-                  <button class="accordion-button collapsed" id="resources" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapseTwelve" aria-expanded="false" aria-controls="flush-collapseTwelve">
-                    RESOURCES
-                  </button>
-                </h2>
-                <div id="flush-collapseTwelve" class="accordion-collapse collapse" aria-labelledby="flush-headingTwelve" data-bs-parent="#accordionFlush12">
-                  <div class="accordion-body">
-                    <ul>
-<li>
-                        <a href="key-points.html">Key Points</a>
-                      </li>
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-                        <a href="reference.html#glossary">Glossary</a>
-                      </li>
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-                        <a href="profiles.html">Learner Profiles</a>
-                      </li>
-                      <li><a href="reference.html">Glossary of Terms: Epiverse-TRACE</a></li>
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-<a href="aio.html">See all in one page</a>
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-  <div class="col-xl-8 col-lg-12 primary-content">
-    <main id="main-content" class="main-content"><div class="container lesson-content">
-        
-        
-<section id="aio-introduction"><p>Content from <a href="introduction.html">Outbreak analytics pipelines</a></p>
-<hr>
-<p>Last updated on 2024-02-08 |
-        
-        <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/introduction.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
-<div class="text-end">
-          <button role="button" aria-pressed="false" tabindex="0" id="expand-code" class="pull-right" data-expand="Expand All Solutions " data-collapse="Collapse All Solutions "> Expand All Solutions <i aria-hidden="true" data-feather="plus"></i></button>
-        </div>
-<div class="overview card">
-<h2 class="card-header">Overview</h2>
-<div class="row g-0">
-<div class="col-md-4">
-<div class="card-body">
-<div class="inner">
-<h3 class="card-title">Questions</h3>
-<ul>
-<li>Why use R packages for Outbreak analytics?</li>
-<li>What can we do to analyse our outbreak data?</li>
-<li>How can I start doing Outbreak Analytics with R?</li>
-</ul>
-</div>
-</div>
-</div>
-<div class="col-md-8">
-<div class="card-body">
-<div class="inner bordered">
-<h3 class="card-title">Objectives</h3>
-<ul>
-<li>Explain our vision on the need for outbreak analytics R
-packages.</li>
-<li>Share our strategy to create R packages into an outbreak analytics
-pipeline.</li>
-<li>Define our plan to start your learning path in outbreak analytics
-with R.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="prerequisites" class="callout prereq">
-<div class="callout-square">
-<i class="callout-icon" data-feather="check"></i>
-</div>
-<div id="prerequisites" class="callout-inner">
-<h3 class="callout-title">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
-</h3>
-<div class="callout-content">
-<p>This episode requires you to be familiar with:</p>
-<p><strong>Data science</strong> : Basic programming with R.</p>
-<p><strong>Epidemic theory</strong> : Reproduction number.</p>
-</div>
-</div>
-</div>
-<section id="why-to-use-r-packages-for-outbreak-analytics"><h2 class="section-heading">Why to use R packages for Outbreak analytics?<a class="anchor" aria-label="anchor" href="#why-to-use-r-packages-for-outbreak-analytics"></a>
-</h2>
-<hr class="half-width">
-<p>Outbreaks appear with different diseases and in different contexts,
-but what all of them have in common are the key public health questions
-(<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605" class="external-link">Cori
-et al. 2017</a>).</p>
-<p>Is the epidemic going to take off? Is it under control? How much
-effort will be needed to control it? We can answer them by
-<em>quantifying the transmissibility</em> of the disease. The most used
-parameter for this is the reproduction number (<span class="math inline">\(R\)</span>), the average number of secondary
-infections caused by a typical primary case in the population of
-interest (<a href="http://prism.edu.au/publications/prism-modeling-guideline/" class="external-link">Prism,
-2016</a>). We can intuitively interpret it as:</p>
-<ul>
-<li>if <span class="math inline">\(R&gt;1\)</span>, the epidemic is
-likely to grow,</li>
-<li>if <span class="math inline">\(R&lt;1\)</span>, the epidemic is
-likely to decline.</li>
-</ul>
-<p>We can estimate the reproduction number by initially using two
-<strong>data inputs</strong>: the incidence of reported cases and the <a href="reference.html#generationtime">generation time</a> distribution.
-But to calculate it, we must apply the appropriate mathematical models
-written in code with the required computational methods. That is not
-enough! Following <em>good practices</em>, the code we write should be
-peer-reviewed and contain internal tests to double-check that we are
-getting the estimates we expect. Imagine rewriting all of it during a
-health emergency!</p>
-<p>In R, the fundamental unit of shareable code is the <em>package</em>.
-A package bundles together code, data, documentation, and tests and is
-easy to share with others (<a href="https://r-pkgs.org/introduction.html" class="external-link">Wickham and Bryan,
-2023</a>). We, as epidemiologists, can contribute to their collaborative
-maintenance as a community to perform less error-prone data analysis
-pipelines.</p>
-<div id="questions-to-think-about" class="callout discussion">
-<div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
-</div>
-<div id="questions-to-think-about" class="callout-inner">
-<h3 class="callout-title">Questions to think about<a class="anchor" aria-label="anchor" href="#questions-to-think-about"></a>
-</h3>
-<div class="callout-content">
-<p>Remember your last experience with outbreak data and reflect on these
-questions:</p>
-<ul>
-<li>What data sources did you need to understand the outbreak?</li>
-<li>How did you get access to that data?</li>
-<li>Is that analysis pipeline you followed reusable for the next
-response?</li>
-</ul>
-<p>Reflect on your experiences.</p>
-</div>
-</div>
-</div>
-</section><section id="example-quantify-transmission"><h2 class="section-heading">Example: Quantify transmission<a class="anchor" aria-label="anchor" href="#example-quantify-transmission"></a>
-</h2>
-<hr class="half-width">
-<p>The <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a> package provides a three-step solution to
-<em>quantify the transmissibility</em>. Let’s see how to do this with a
-minimal example. First, load the package:</p>
-<div class="codewrapper sourceCode" id="cb1">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">library</span><span class="op">(</span><span class="va">EpiNow2</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="first-get-your-case-data">First, get your case data<a class="anchor" aria-label="anchor" href="#first-get-your-case-data"></a>
-</h3>
-<p>Case incidence data must be stored in a data frame with the observed
-number of cases per day. We can read an example from the package:</p>
-<div class="codewrapper sourceCode" id="cb2">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">example_confirmed</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>           date confirm
-         &lt;Date&gt;   &lt;num&gt;
-  1: 2020-02-22      14
-  2: 2020-02-23      62
-  3: 2020-02-24      53
-  4: 2020-02-25      97
-  5: 2020-02-26      93
- ---                   
-126: 2020-06-26     296
-127: 2020-06-27     255
-128: 2020-06-28     175
-129: 2020-06-29     174
-130: 2020-06-30     126</code></pre>
-</div>
-</div>
-<div class="section level3">
-<h3 id="then-set-the-generation-time">Then, set the generation time<a class="anchor" aria-label="anchor" href="#then-set-the-generation-time"></a>
-</h3>
-<p>Not all primary cases have the same probability of generating a
-secondary case. The onset and cessation of <a href="reference.html#infectiousness">infectiousness</a> may occur
-gradually. For <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, we can specify it as a
-probability <code>distribution</code> with <code>mean</code>, standard
-deviation <code>sd</code>, and maximum value <code>max</code>:</p>
-<div class="codewrapper sourceCode" id="cb4">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">generation_time</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="fl">3.6</span>,</span>
-<span>  sd <span class="op">=</span> <span class="fl">3.1</span>,</span>
-<span>  max <span class="op">=</span> <span class="fl">20</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-<div class="section level3">
-<h3 id="lets-calculate-the-reproduction-number">Let’s calculate the reproduction number!<a class="anchor" aria-label="anchor" href="#lets-calculate-the-reproduction-number"></a>
-</h3>
-<p>In the <code>epinow()</code> function we can add:</p>
-<ul>
-<li>the <code>reported_cases</code> data frame,</li>
-<li>the <code>generation_time</code> delay distribution, and</li>
-<li>the computation <code>stan</code> parameters for this
-calculation:</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb5">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:18:42] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-Running the chains for more iterations may help. See
-https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
-</div>
-<p>As an output, we get the time-varying (or <a href="reference.html#effectiverepro">effective</a>) reproduction number,
-as well as the cases by date of report and date of infection:</p>
-<div class="codewrapper sourceCode" id="cb7">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="fig/introduction-rendered-unnamed-chunk-5-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div id="is-this-rt-estimation-biased" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="is-this-rt-estimation-biased" class="callout-inner">
-<h3 class="callout-title">Is this<span class="math inline">\(Rt\)</span>estimation biased?<a class="anchor" aria-label="anchor" href="#is-this-rt-estimation-biased"></a>
-</h3>
-<div class="callout-content">
-<p>Review <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">Gostic
-et al., 2020</a> about what additional adjustments this estimation
-requires to avoid false precision in <span class="math inline">\(Rt\)</span>.</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="the-problem"><h2 class="section-heading">The problem!<a class="anchor" aria-label="anchor" href="#the-problem"></a>
-</h2>
-<hr class="half-width">
-<p>However, <em>quantifying the transmissibility</em> during a real-life
-outbreak response is more challenging than this example!</p>
-<p>Usually, we receive outbreak data in non-standard formats, requiring
-specific steps and taking the most time to prepare usable data inputs.
-Some of them are:</p>
-<ul>
-<li>Read delay distributions from the literature</li>
-<li>Read and clean case data</li>
-<li>Validate your line list</li>
-<li>Describe case data</li>
-</ul>
-<p>And this is not the end. After <em>quantifying transmissibility</em>
-we need to answer more key public health questions like: What is the
-attack rate we expect? What would be the impact of a given intervention?
-We can use the reproduction number and other outputs as new inputs for
-complementary tasks. For example:</p>
-<ul>
-<li>Estimate severity</li>
-<li>Create short-term forecast</li>
-<li>Simulate transmission scenarios</li>
-<li>Compare interventions</li>
-</ul>
-<p>So, all these tasks can be interconnected in a pipeline:</p>
-<figure><img src="https://epiverse-trace.github.io/task_pipeline-minimal.svg" alt="" class="figure mx-auto d-block"><figcaption>The outbreak analytics pipeline.</figcaption></figure></section><section id="what-can-we-do"><h2 class="section-heading">What can we do?<a class="anchor" aria-label="anchor" href="#what-can-we-do"></a>
-</h2>
-<hr class="half-width">
-<p>Our strategy is gradually incorporating specialised R packages into
-our traditional analysis pipeline. These packages should fill the gaps
-in these epidemiology-specific tasks in response to outbreaks.</p>
-<p>Epiverse-TRACE’s aim is to provide a software ecosystem for outbreak
-analytics. We support the development of software pieces, make the
-existing ones interoperable for the user experience, and stimulate a
-community of practice.</p>
-<figure><img src="fig/pkgs-hexlogos.png" class="figure mx-auto d-block"></figure></section><section id="how-can-i-start"><h2 class="section-heading">How can I start?<a class="anchor" aria-label="anchor" href="#how-can-i-start"></a>
-</h2>
-<hr class="half-width">
-<p>Our plan for these tutorials is to introduce key solutions from
-packages in all the tasks before and after the <em>Quantify
-transmission</em> task, plus the required theory concepts to interpret
-modelling outputs and make rigorous conclusions.</p>
-<ul>
-<li><p>In the first set of episodes, you will learn how to optimise the
-reading of delay distributions and cleaning of case data to input them
-into the <em>Quantify transmission</em> task. These preliminary tasks
-are the <strong>Early tasks</strong>. These include packages like
-<code>{readepi}</code>, <code>{cleanepi}</code>,
-<a href="https://epiverse-trace.github.io/linelist/" class="external-link">linelist</a>, <code>{epiparameter}</code>, and
-<code>{episoap}</code>.</p></li>
-<li><p>Then, we will get deeper into the packages and required theory to
-<em>Quantify transmission</em> and perform more real-time analysis tasks
-next to it. These are the <strong>Middle tasks</strong>. This includes
-<a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, <a href="https://github.com/epiverse-trace/cfr" class="external-link">cfr</a>, <code>{epichains}</code>,
-and <code>{superspreading}</code>.</p></li>
-<li><p>Lastly, we will use <em>Quantify transmission</em> data outputs
-to compare it to other indicators and simulate epidemic scenarios as
-part of the <strong>Late tasks</strong>. This includes
-<a href="https://github.com/epiverse-trace/finalsize" class="external-link">finalsize</a>, <code>{epidemics}</code>, and
-<code>{scenarios}</code>.</p></li>
-</ul>
-<div id="keypoints1" class="callout keypoints">
-<div class="callout-square">
-<i class="callout-icon" data-feather="key"></i>
-</div>
-<div class="callout-inner">
-<h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="#keypoints1"></a>
-</h3>
-<div class="callout-content">
-<ul>
-<li>Our vision is to have pipelines of R packages for outbreak
-analytics.</li>
-<li>Our strategy is to create interconnected tasks to get relevant
-outputs for public health questions.</li>
-<li>We plan to introduce package solutions and theory bits for each of
-the tasks in the outbreak analytics pipeline.</li>
-</ul>
-</div>
-</div>
-</div>
-<!-- 
-Place links that you need to refer to multiple times across pages here. Delete
-any links that you are not going to use. 
- -->
-</section></section><section id="aio-read-delays"><p>Content from <a href="read-delays.html">Read delays</a></p>
-<hr>
-<p>Last updated on 2024-02-08 |
-        
-        <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/read-delays.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
-<div class="text-end">
-          <button role="button" aria-pressed="false" tabindex="0" id="expand-code" class="pull-right" data-expand="Expand All Solutions " data-collapse="Collapse All Solutions "> Expand All Solutions <i aria-hidden="true" data-feather="plus"></i></button>
-        </div>
-<div class="overview card">
-<h2 class="card-header">Overview</h2>
-<div class="row g-0">
-<div class="col-md-4">
-<div class="card-body">
-<div class="inner">
-<h3 class="card-title">Questions</h3>
-<ul>
-<li>How to get delay distributions from a systematic review?</li>
-<li>How to connect reused delays with my existing analysis
-pipeline?</li>
-<li>When should delays be reused from a systematic review?</li>
-</ul>
-</div>
-</div>
-</div>
-<div class="col-md-8">
-<div class="card-body">
-<div class="inner bordered">
-<h3 class="card-title">Objectives</h3>
-<ul>
-<li>Get delays from a systematic review with
-<code>{epiparameter}</code>.</li>
-<li>Get statistical summaries and distribution parameters of delay
-distributions.</li>
-<li>Use distribution functions from delay distributions.</li>
-<li>Convert a continuous to a discrete delay distribution.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="prerequisites" class="callout prereq">
-<div class="callout-square">
-<i class="callout-icon" data-feather="check"></i>
-</div>
-<div id="prerequisites" class="callout-inner">
-<h3 class="callout-title">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
-</h3>
-<div class="callout-content">
-<p>This episode requires you to be familiar with:</p>
-<p><strong>Data science</strong> : Basic programming with R.</p>
-<p><strong>Epidemic theory</strong> : Epidemiological parameters. Time
-periods.</p>
-</div>
-</div>
-</div>
-<section id="introduction"><h2 class="section-heading">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<hr class="half-width">
-<p>The <a href="reference.html#naturalhistory">natural history</a> of an
-infectious disease shows that its development has a regularity from
-stage to stage. The time periods from an infectious disease inform about
-the timing of transmission and interventions.</p>
-<figure><img src="fig/time-periods.jpg" alt="" class="figure mx-auto d-block"><figcaption>Definition of key time periods. From <a href="https://www.sciencedirect.com/science/article/pii/S2468042721000038" class="external-link">Xiang
-et al, 2021</a></figcaption></figure><div id="definitions" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="definitions" class="callout-inner">
-<h3 class="callout-title">Definitions<a class="anchor" aria-label="anchor" href="#definitions"></a>
-</h3>
-<div class="callout-content">
-<p>Look at the <a href="reference.html">glossary</a> for the definitions
-of all the time periods of the figure above!</p>
-</div>
-</div>
-</div>
-<p>However, early in an epidemic, modelling efforts can be delayed by
-the lack of a centralized resource that summarises input parameters for
-the disease of interest (<a href="https://mrc-ide.github.io/epireview/" class="external-link">Nash et al., 2023</a>).
-Projects like <code>{epiparameter}</code> and <code>{epireview}</code>
-are building online catalogues following systematic review protocols
-that can help build models faster for coming outbreaks and epidemics
-from known pathogens and unknown ones related to known families of
-viruses.</p>
-<!-- Early models for COVID-19 used parameters from other coronaviruses. https://www.thelancet.com/article/S1473-3099(20)30144-4/fulltext -->
-<p>To exemplify how to use <code>{epiparameter}</code> in your analysis
-pipeline, our goal in this episode will be to replace the
-<code>generation_time</code> input that we can use for
-<code>EpiNow2::epinow()</code>.</p>
-<div class="codewrapper sourceCode" id="cb1">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>To do this replacement, instead of plug-in numeric values to
-<code>EpiNow2::dist_spec()</code> to manually specify the delay
-distribution parameters, we are going to collect them from the library
-of epidemiological parameters provided by
-<code>{epiparameter}</code>:</p>
-<div class="codewrapper sourceCode" id="cb2">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">generation_time</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="fl">3.6</span>,</span>
-<span>  sd <span class="op">=</span> <span class="fl">3.1</span>,</span>
-<span>  max <span class="op">=</span> <span class="fl">20</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>Let’s explore how we can access this and other time delays using
-<code>{epiparameter}</code>. We’ll use the pipe <code>%&gt;%</code> to
-connect some of their functions, so let’s also call to the
-<a href="https://tidyverse.tidyverse.org" class="external-link">tidyverse</a> package:</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">library</span><span class="op">(</span><span class="va">epiparameter</span><span class="op">)</span></span>
-<span><span class="kw">library</span><span class="op">(</span><span class="va">EpiNow2</span><span class="op">)</span></span>
-<span><span class="kw">library</span><span class="op">(</span><span class="va">tidyverse</span><span class="op">)</span></span></code></pre>
-</div>
-</section><section id="find-a-generation-time"><h2 class="section-heading">Find a Generation time<a class="anchor" aria-label="anchor" href="#find-a-generation-time"></a>
-</h2>
-<hr class="half-width">
-<p>The generation time, jointly with the <span class="math inline">\(R\)</span>, can inform about the speed of spread
-and its feasibility of control. Given a <span class="math inline">\(R&gt;1\)</span>, with a shorter generation time,
-cases can appear more quickly.</p>
-<figure><img src="fig/reproduction-generation-time.png" alt="" class="figure mx-auto d-block"><figcaption>Video from the MRC Centre for Global Infectious
-Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group
-with Dr Anne Cori (27-07-2023) at <a href="https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL" class="external-link uri">https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL</a></figcaption></figure><p>In calculating the effective reproduction number (<span class="math inline">\(R_{t}\)</span>), the <em>generation time</em>
-distribution is often approximated by the <a href="reference.html#serialinterval">serial interval</a> distribution.
-This frequent approximation is because it is easier to observe and
-measure the onset of symptoms than the onset of infectiousness.</p>
-<figure><img src="fig/serial-interval-observed.jpeg" alt="" class="figure mx-auto d-block"><figcaption>A schematic of the relationship of different time
-periods of transmission between an infector and an infectee in a
-transmission pair. Exposure window is defined as the time interval
-having viral exposure, and transmission window is defined as the time
-interval for onward transmission with respect to the infection time (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>).</figcaption></figure><p>However, using the <em>serial interval</em> as an approximation of
-the <em>generation time</em> is primarily valid for diseases in which
-infectiousness starts after symptom onset (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>). In cases where infectiousness starts before
-symptom onset, the serial intervals can have negative values, which is
-the case of a pre-symptomatic transmission (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2" class="external-link">Nishiura
-et al. (2020)</a>).</p>
-<p>Additionally, even if the <em>generation time</em> and <em>serial
-interval</em> have the same mean, their variance usually differs,
-propagating bias to the <span class="math inline">\(R_{t}\)</span>
-estimation. <span class="math inline">\(R_{t}\)</span> estimates are
-sensitive not only to the mean generation time but also to the variance
-and form of the generation interval distribution <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">(Gostic
-et al., 2020)</a>.</p>
-<div id="from-time-periods-to-probability-distributions." class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="from-time-periods-to-probability-distributions." class="callout-inner">
-<h3 class="callout-title">From time periods to probability
-distributions.<a class="anchor" aria-label="anchor" href="#from-time-periods-to-probability-distributions."></a>
-</h3>
-<div class="callout-content">
-<p>When we calculate the <em>serial interval</em>, we see that not all
-case pairs have the same time length. We will observe this variability
-for any case pair and individual time period, including the <a href="reference.html#incubation">incubation period</a> and <a href="reference.html#infectiousness">infectious period</a>.</p>
-<figure><img src="fig/serial-interval-pairs.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial intervals of possible case pairs in (a)
-COVID-19 and (b) MERS-CoV. Pairs represent a presumed infector and their
-presumed infectee plotted by date of symptom onset (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><p>To summarize these data from individual and pair time periods, we can
-find the <strong>statistical distributions</strong> that best fit the
-data (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200806" class="external-link">McFarland
-et al., 2023</a>).</p>
-<!-- add a reference about good practices to estimate distributions -->
-<figure><img src="fig/seria-interval-fitted-distributions.jpg" alt="" class="figure mx-auto d-block"><figcaption>Fitted serial interval distribution for (a) COVID-19
-and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia.
-We fitted three commonly used distributions, Lognormal, Gamma, and
-Weibull distributions, respectively (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><p>Statistical distributions are summarized in terms of their
-<strong>summary statistics</strong> like the <em>location</em> (mean and
-percentiles) and <em>spread</em> (variance or standard deviation) of the
-distribution, or with their <strong>distribution parameters</strong>
-that inform about the <em>form</em> (shape and rate/scale) of the
-distribution. These estimated values can be reported with their
-<strong>uncertainty</strong> (95% confidence intervals).</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">Gamma</th>
-<th align="left">mean</th>
-<th align="left">shape</th>
-<th align="left">rate/scale</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.13(13.9–14.7)</td>
-<td align="left">6.31(4.88–8.52)</td>
-<td align="left">0.43(0.33–0.60)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.1(5.0–5.5)</td>
-<td align="left">2.77(2.09–3.88)</td>
-<td align="left">0.53(0.38–0.76)</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<thead><tr class="header">
-<th align="left">Weibull</th>
-<th align="left">mean</th>
-<th align="left">shape</th>
-<th align="left">rate/scale</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.2(13.3–15.2)</td>
-<td align="left">3.07(2.64–3.63)</td>
-<td align="left">16.1(15.0–17.1)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.2(4.6–5.9)</td>
-<td align="left">1.74(1.46–2.11)</td>
-<td align="left">5.83(5.08–6.67)</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Serial interval estimates using Gamma, Weibull, and Log normal
-distributions. 95% confidence intervals for the shape and scale (logmean
-and sd for Log normal) parameters are shown in brackets (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3" class="external-link">Althobaity
-et al., 2022</a>).</caption>
-<thead><tr class="header">
-<th align="left">Log normal</th>
-<th align="left">mean</th>
-<th align="left">mean-log</th>
-<th align="left">sd-log</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.08(13.1–15.2)</td>
-<td align="left">2.58(2.50–2.68)</td>
-<td align="left">0.44(0.39–0.5)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.2(4.2–6.5)</td>
-<td align="left">1.45(1.31–1.61)</td>
-<td align="left">0.63(0.54–0.74)</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-<div id="serial-interval" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="serial-interval" class="callout-inner">
-<h3 class="callout-title">Serial interval<a class="anchor" aria-label="anchor" href="#serial-interval"></a>
-</h3>
-<div class="callout-content">
-<p>Assume that COVID-19 and SARS have similar reproduction number values
-and that the serial interval approximates the generation time.</p>
-<p>Given the Serial interval of both infections in the figure below:</p>
-<ul>
-<li>Which one would be harder to control?</li>
-<li>Why do you conclude that?</li>
-</ul>
-<figure><img src="fig/serial-interval-covid-sars.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial interval of novel coronavirus (COVID-19)
-infections overlaid with a published distribution of SARS. (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-et al, 2020</a>)</figcaption></figure>
-</div>
-</div>
-</div>
-<div id="accordionHint1" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
-  <h4 class="accordion-header" id="headingHint1">Give me a hint</h4>
-</button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
-<div class="accordion-body">
-<p>The peak of each curve can inform you about the location of the mean
-of each distribution. The larger the mean, the larger the serial
-interval.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution1" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
-  <h4 class="accordion-header" id="headingSolution1">Show me the solution</h4>
-</button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
-<div class="accordion-body">
-<p>Which one would be harder to control?</p>
-<ul>
-<li>COVID-19</li>
-</ul>
-<p>Why do you conclude that?</p>
-<ul>
-<li>COVID-19 has the lowest mean serial interval. The approximate mean
-value for the serial interval of COVID-19 is around four days, and SARS
-is about seven days. Thus, COVID-19 will likely have newer generations
-in less time than SARS, assuming similar reproduction numbers.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-
-</section><section id="extract-epidemiological-parameters"><h2 class="section-heading">Extract epidemiological parameters<a class="anchor" aria-label="anchor" href="#extract-epidemiological-parameters"></a>
-</h2>
-<hr class="half-width">
-<p>First, let’s assume that the data set <code>example_confirmed</code>
-has COVID-19 observed cases. So, we need to find a reported generation
-time for COVID-19 or any other useful parameter for this aim.</p>
-<p>Let’s start by looking at how many parameters we have in the
-epidemiological distributions database (<code>epidist_db</code>) for the
-<code>disease</code> named <code>covid</code>-19:</p>
-<div class="codewrapper sourceCode" id="cb4">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 27 results that match the criteria (22 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>List of &lt;epidist&gt; objects
-  Number of entries in library: 27
-  Number of studies in library: 10
-  Number of diseases: 1
-  Number of delay distributions: 27
-  Number of offspring distributions: 0</code></pre>
-</div>
-<p>From the <code>{epiparameter}</code> package, we can use the
-<code>epidist_db()</code> function to ask for any <code>disease</code>
-and also for a specific epidemiological distribution
-(<code>epi_dist</code>).</p>
-<p>Let’s ask now how many parameters we have in the epidemiological
-distributions database (<code>epidist_db</code>) with the generation
-time using the string <code>generation</code>:</p>
-<div class="codewrapper sourceCode" id="cb7">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"generation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 1 results that match the criteria (1 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: Influenza
-Pathogen: Influenza-A-H1N1
-Epi Distribution: generation time
-Study: Lessler J, Reich N, Cummings D, New York City Department of Health and
-Mental Hygiene Swine Influenza Investigation Team (2009). "Outbreak of
-2009 Pandemic Influenza A (H1N1) at a New York City School." _The New
-England Journal of Medicine_. doi:10.1056/NEJMoa0906089
-&lt;https://doi.org/10.1056/NEJMoa0906089&gt;.
-Distribution: weibull
-Parameters:
-  shape: 2.360
-  scale: 3.180</code></pre>
-</div>
-<p>Currently, in the library of epidemiological parameters, we have one
-<code>generation</code> time entry for Influenza. Considering the
-abovementioned considerations, we can look at the <code>serial</code>
-intervals for <code>COVID</code>-19.</p>
-<div class="codewrapper sourceCode" id="cb10">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"COVID"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[[1]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021).
-"Serial interval and incubation period of COVID-19: a systematic review
-and meta-analysis." _BMC Infectious Diseases_.
-doi:10.1186/s12879-021-05950-x
-&lt;https://doi.org/10.1186/s12879-021-05950-x&gt;.
-Parameters: &lt;no parameters&gt;
-
-[[2]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568
-
-[[3]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: weibull
-Parameters:
-  shape: 2.203
-  scale: 5.420
-
-[[4]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). "Estimation of
-incubation period and serial interval of COVID-19: analysis of 178
-cases and 131 transmission chains in Hubei province, China."
-_Epidemiology and Infection_. doi:10.1017/S0950268820001338
-&lt;https://doi.org/10.1017/S0950268820001338&gt;.
-Distribution: norm
-Parameters:
-  mean: 4.600
-  sd: 4.400
-
-attr(,"class")
-[1] "multi_epidist"</code></pre>
-</div>
-<div id="case-insensitive" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="case-insensitive" class="callout-inner">
-<h3 class="callout-title">CASE-INSENSITIVE<a class="anchor" aria-label="anchor" href="#case-insensitive"></a>
-</h3>
-<div class="callout-content">
-<p><code>epidist_db</code> is <a href="https://dillionmegida.com/p/case-sensitivity-vs-case-insensitivity/#case-insensitivity" class="external-link">case-insensitive</a>.
-This means that you can use strings with letters in upper or lower case
-indistinctly.</p>
-</div>
-</div>
-</div>
-<p>We get more than one epidemiological delay. To summarize this view
-and get the column names from the underlying parameter dataset, we can
-add the <code>epiparameter::list_distributions()</code> function to the
-previous code using the pipe <code>%&gt;%</code>:</p>
-<div class="codewrapper sourceCode" id="cb13">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<div id="ebolas-incubation-periods" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-incubation-periods" class="callout-inner">
-<h3 class="callout-title">Ebola’s incubation periods<a class="anchor" aria-label="anchor" href="#ebolas-incubation-periods"></a>
-</h3>
-<div class="callout-content">
-<p>Take 5 minutes:</p>
-<ul>
-<li><p>How many delay distributions are for the Ebola disease?</p></li>
-<li><p>How many delay distributions are for the incubation period of
-Ebola?</p></li>
-<li><p>Explore the library and find the disease with the delay
-distribution of your interest! Do you recognize the paper?</p></li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint2" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
-  <h4 class="accordion-header" id="headingHint2">Give me a hint</h4>
-</button>
-<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
-<div class="accordion-body">
-<p>The <code>{epiparameter}</code> combo of <code>epidist_db()</code>
-plus <code>list_distributions()</code> list all the entries by:</p>
-<ul>
-<li>disease,</li>
-<li>epidemiological distribution,</li>
-<li>the type of the probability distribution,</li>
-<li>author of the study, and</li>
-<li>year of study.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution2" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
-  <h4 class="accordion-header" id="headingSolution2">Show me the solution</h4>
-</button>
-<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb16">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># 16 delays distributions</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># 5 delay distributions are for the incubation period</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>Now, from the output of <code>epiparameter::epidist_db()</code>, What
-is an <a href="reference.html#offspringdist">offspring
-distribution</a>?</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="select-a-single-distribution"><h2 class="section-heading">Select a single distribution<a class="anchor" aria-label="anchor" href="#select-a-single-distribution"></a>
-</h2>
-<hr class="half-width">
-<p>The <code>epiparameter::epidist_db()</code> function works as a
-filtering or subset function. Let’s use the <code>author</code> argument
-to filter <code>Hiroshi Nishiura</code> parameters:</p>
-<div class="codewrapper sourceCode" id="cb17">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Hiroshi"</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 2 results that match the criteria (2 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval             lnorm Hiroshi .... 2020
-2 COVID-19  serial interval           weibull Hiroshi .... 2020</code></pre>
-</div>
-<p>We still get more than one epidemiological parameter. We can set the
-<code>single_epidist</code> argument to <code>TRUE</code> to only
-one:</p>
-<div class="codewrapper sourceCode" id="cb20">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Hiroshi"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Using Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.. 
-To retrieve the short citation use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<div id="how-does-single_epidist-works" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="how-does-single_epidist-works" class="callout-inner">
-<h3 class="callout-title">How does<code>single_epidist</code>works?<a class="anchor" aria-label="anchor" href="#how-does-single_epidist-works"></a>
-</h3>
-<div class="callout-content">
-<p>Looking at the help documentation for
-<code>?epiparameter::epidist_db()</code>:</p>
-<ul>
-<li>If multiple entries match the arguments supplied and
-<code>single_epidist = TRUE</code>,</li>
-<li>Then, the parameterised <code>⁠&lt;epidist&gt;</code>⁠ with the
-largest sample size will be returned.</li>
-<li>If multiple entries are equal after this sorting, the first entry
-will be returned.</li>
-</ul>
-<p>What does a parametrised <code>&lt;epidist&gt;</code> is? Look at
-<code>?is_parameterised</code>.</p>
-</div>
-</div>
-</div>
-<p>Now, we have an epidemiological parameter we can reuse! We can
-replace the numbers we plug into <code>EpiNow2::dist_spec()</code>.</p>
-<p>Let’s assign this <code>&lt;epidist&gt;</code> class object to the
-<code>covid_serialint</code> object.</p>
-<div class="codewrapper sourceCode" id="cb23">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>    author <span class="op">=</span> <span class="st">"Nishiura"</span>,</span>
-<span>    single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Using Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.. 
-To retrieve the short citation use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb25">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<div id="ebolas-incubation-period" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-incubation-period" class="callout-inner">
-<h3 class="callout-title">Ebola’s incubation period<a class="anchor" aria-label="anchor" href="#ebolas-incubation-period"></a>
-</h3>
-<div class="callout-content">
-<p>Take 2 minutes:</p>
-<ul>
-<li>What type of distribution has the incubation period of Ebola with
-the highest sample size?</li>
-<li>How would you access to the sample size of the other studies in the
-<code>&lt;multi_epidist&gt;</code> class object?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint3" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint3" aria-expanded="false" aria-controls="collapseHint3">
-  <h4 class="accordion-header" id="headingHint3">Give me a hint</h4>
-</button>
-<div id="collapseHint3" class="accordion-collapse collapse" data-bs-parent="#accordionHint3" aria-labelledby="headingHint3">
-<div class="accordion-body">
-<p>The <code>{epiparameter}</code> combo of <code>epidist_db()</code>
-plus <code>list_distributions()</code> list all the entries by:</p>
-<ul>
-<li>disease,</li>
-<li>epidemiological distribution,</li>
-<li>the type of the probability distribution,</li>
-<li>author of the study, and</li>
-<li>year of study.</li>
-</ul>
-<p>This is a <code>&lt;multi_epidist&gt;</code> class object:</p>
-<div class="codewrapper sourceCode" id="cb27">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution3" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
-  <h4 class="accordion-header" id="headingSolution3">Show me the solution</h4>
-</button>
-<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb28">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the distribution with the highest sample size has a gamma distribution</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>To access the <code>sample_size</code>, review an <a href="https://github.com/epiverse-trace/epiparameter/issues/227" class="external-link">issue
-reported in the GitHub repository</a> of the <code>{epiparameter}</code>
-package.</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="extract-the-summary-statistics"><h2 class="section-heading">Extract the summary statistics<a class="anchor" aria-label="anchor" href="#extract-the-summary-statistics"></a>
-</h2>
-<hr class="half-width">
-<p>We can get the <code>mean</code> and standard deviation
-(<code>sd</code>) from this <code>&lt;epidist&gt;</code> diving into the
-<code>summary_stats</code> object:</p>
-<div class="codewrapper sourceCode" id="cb29">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># get the mean</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 4.7</code></pre>
-</div>
-<div id="how-to-get-the-sd-and-other-nested-elements" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="how-to-get-the-sd-and-other-nested-elements" class="callout-inner">
-<h3 class="callout-title">How to get the<code>sd</code>and other
-nested elements?<a class="anchor" aria-label="anchor" href="#how-to-get-the-sd-and-other-nested-elements"></a>
-</h3>
-<div class="callout-content">
-<p>Take 1 minute to:</p>
-<ol style="list-style-type: decimal">
-<li><p>Get the <code>sd</code> of the epidemiological
-distribution.</p></li>
-<li><p>Find the <code>sample_size</code> used in the study.</p></li>
-<li><p>Explore all the other nested elements within the
-<code>&lt;epidist&gt;</code> object.</p></li>
-</ol>
-<p>Share about:</p>
-<ul>
-<li>What elements do you find useful for your analysis?</li>
-<li>What other elements would you like to see in this object? How?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint4" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint4" aria-expanded="false" aria-controls="collapseHint4">
-  <h4 class="accordion-header" id="headingHint4">Give me a hint</h4>
-</button>
-<div id="collapseHint4" class="accordion-collapse collapse" data-bs-parent="#accordionHint4" aria-labelledby="headingHint4">
-<div class="accordion-body">
-<p>Use the <code>$</code> operator plus the <kbd>tab</kbd> keyboard
-button to explore them as an expandable list:</p>
-<div class="codewrapper sourceCode" id="cb31">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>covid_serialint<span class="sc">$</span></span></code></pre>
-</div>
-<p>Use the <code>str()</code> to display the structure of the
-<code>&lt;epidist&gt;</code> R object.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
-  <h4 class="accordion-header" id="headingSolution4">Show me the solution</h4>
-</button>
-<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb32">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># get the sd</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span></span>
-<span></span>
-<span><span class="co"># get the sample_size</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">sample_size</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="intructor">
-<p>An interesting element is the <code>method_assess</code> nested
-entry, which refers to the methods used by the study authors to assess
-for bias while estimating the serial interval distribution.</p>
-<div class="codewrapper sourceCode" id="cb33">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">method_assess</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>$censored
-[1] TRUE
-
-$right_truncated
-[1] TRUE
-
-$phase_bias_adjusted
-[1] FALSE</code></pre>
-</div>
-<p>We will explore these concepts at the end!</p>
-</div>
-<div id="ebolas-severity-parameter" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-severity-parameter" class="callout-inner">
-<h3 class="callout-title">Ebola’s severity parameter<a class="anchor" aria-label="anchor" href="#ebolas-severity-parameter"></a>
-</h3>
-<div class="callout-content">
-<p>A severity parameter like the duration of hospitalization could add
-to the information needed about the bed capacity in response to an
-outbreak (<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371" class="external-link">Cori
-et al., 2017</a>).</p>
-<p>For Ebola:</p>
-<ul>
-<li>what is a reported point estimate and uncertainty of the mean
-duration of health-care and case isolation?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint5" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint5" aria-expanded="false" aria-controls="collapseHint5">
-  <h4 class="accordion-header" id="headingHint5">Give me a hint</h4>
-</button>
-<div id="collapseHint5" class="accordion-collapse collapse" data-bs-parent="#accordionHint5" aria-labelledby="headingHint5">
-<div class="accordion-body">
-<p>An informative delay measures the time from symptom onset to recovery
-or death.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
-  <h4 class="accordion-header" id="headingSolution5">Show me the solution</h4>
-</button>
-<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb35">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># one way to get the list of all the available parameters</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"all"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">as_tibble</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">distinct</span><span class="op">(</span><span class="va">epi_distribution</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_severity</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"onset to discharge"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># point estimate</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span></span>
-<span><span class="co"># 95% confidence intervals</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean_ci</span></span>
-<span><span class="co"># limits of the confidence intervals</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean_ci_limits</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="continuous-distributions"><h2 class="section-heading">Continuous distributions<a class="anchor" aria-label="anchor" href="#continuous-distributions"></a>
-</h2>
-<hr class="half-width">
-<p>The following output has four entries with different content in the
-<strong>probability distribution</strong>
-(<code>prob_distribution</code>) column:</p>
-<div class="codewrapper sourceCode" id="cb36">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span>  <span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb38">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<p>Entries with a missing value (<code>&lt;NA&gt;</code>) in the
-<code>prob_distribution</code> column are <em>non-parameterised</em>
-entries. They have summary statistics but no probability distribution.
-Compare these two outputs:</p>
-<div class="codewrapper sourceCode" id="cb40">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">summary_stats</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">prob_dist</span></span></code></pre>
-</div>
-<div id="accordionSpoiler1" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
-  <h3 class="accordion-header" id="headingSpoiler1">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>Parameterised entries have an Inference method</h3>
-</button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
-<div class="accordion-body">
-<p>As detailed in <code>?is_parameterised</code>, a parameterised
-distribution is the entry that has a probability distribution associated
-with it provided by an <code>inference_method</code> as shown in
-<code>metadata</code>:</p>
-<div class="codewrapper sourceCode" id="cb41">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">4</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<p>In the <code>epiparameter::list_distributions()</code> output, we can
-also find different types of probability distributions (e.g.,
-Log-normal, Weibull, Normal).</p>
-<div class="codewrapper sourceCode" id="cb42">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<p>In <code>{epiparameter}</code>, you will mostly find
-<strong>continuous</strong> distributions like these. You can visualize
-any of them with the <code>plot()</code> function and access to:</p>
-<ul>
-<li>the <em>Probability Density Function (PDF)</em> and</li>
-<li>the <em>Cumulative Distribution Function (CDF)</em>.</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb44">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="fig/read-delays-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>With the <code>day_range</code> argument, you can change the length
-or number of days in the <code>x</code> axis. Explore what it look
-like:</p>
-<div class="codewrapper sourceCode" id="cb45">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="the-distribution-zoo" class="callout discussion">
-<div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
-</div>
-<div id="the-distribution-zoo" class="callout-inner">
-<h3 class="callout-title">The distribution Zoo<a class="anchor" aria-label="anchor" href="#the-distribution-zoo"></a>
-</h3>
-<div class="callout-content">
-<p>Explore this shinyapp called <strong>The Distribution
-Zoo</strong>!</p>
-<p>Follow these steps to reproduce the form of the
-<code>covid_serialint</code> distribution:</p>
-<ol style="list-style-type: decimal">
-<li>Access to <a href="https://ben18785.shinyapps.io/distribution-zoo/" class="external-link uri">https://ben18785.shinyapps.io/distribution-zoo/</a> shinyapp
-website,</li>
-<li>Go to the left panel,</li>
-<li>Keep the <em>Category of distribution</em>:
-<code>Continuous Univariate</code>,</li>
-<li>Select a new <em>Type of distribution</em>:
-<code>Log-Normal</code>,</li>
-<li>Move the <strong>sliders</strong>, i.e. the graphical control
-element that allows you to adjust a value by moving a handle along a
-horizontal track or bar to the <code>covid_serialint</code>
-parameters.</li>
-</ol>
-<p>Replicate these with the <code>distribution</code> object and all its
-list elements: <code>2</code>, <code>3</code>, and <code>4</code>.
-Explore how the shape of a distribution changes when its parameters
-change.</p>
-<p>Share about:</p>
-<ul>
-<li>What other features of the website do you find helpful?</li>
-</ul>
-</div>
-</div>
-</div>
-<!-- A slider, in the context of user interfaces and graphical user interfaces (GUIs), is a graphical control element that allows users to adjust a value by moving a handle along a track or bar. Conceptually, it provides a way to select a numeric value within a specified range by visually sliding or dragging a pointer (the handle) along a continuous axis. -->
-</section><section id="distribution-functions"><h2 class="section-heading">Distribution functions<a class="anchor" aria-label="anchor" href="#distribution-functions"></a>
-</h2>
-<hr class="half-width">
-<p>In R, all the statistical distributions have functions to access
-the:</p>
-<ul>
-<li>Probability <strong>Density</strong> function (PDF),</li>
-<li>Cumulative Distribution function (<strong>CDF</strong>),</li>
-<li>
-<strong>Quantile</strong> function, and</li>
-<li>
-<strong>Random</strong> values from the given distribution.</li>
-</ul>
-<div id="accordionSpoiler2" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler2" aria-expanded="false" aria-controls="collapseSpoiler2">
-  <h3 class="accordion-header" id="headingSpoiler2">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>Functions for the Normal distribution</h3>
-</button>
-<div id="collapseSpoiler2" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler2" aria-labelledby="headingSpoiler2">
-<div class="accordion-body">
-<p>If you need it, read in detail about the <a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">R
-probability functions for the normal distribution</a>, each of its
-definitions and identify in which part of a distribution they are
-located!</p>
-<figure><img src="fig/fig5a-normaldistribution.png" alt="" class="figure mx-auto d-block"><figcaption>The four probability functions for the normal
-distribution (<a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">Jack
-Weiss, 2012</a>)</figcaption></figure>
-</div>
-</div>
-</div>
-</div>
-<p>If you look at <code>?stats::Distributions</code>, each type of
-distribution has a unique set of functions. However,
-<code>{epiparameter}</code> gives you the same four functions to access
-each of the values above for any <code>&lt;epidist&gt;</code> object you
-want!</p>
-<div class="codewrapper sourceCode" id="cb46">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># plot this to have a visual reference</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb47">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the density value at quantile value of 10 (days)</span></span>
-<span><span class="fu">density</span><span class="op">(</span><span class="va">covid_serialint</span>, at <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 0.01911607</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb49">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the cumulative probability at quantile value of 10 (days)</span></span>
-<span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 0.9466605</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb51">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the quantile value (day) at a cumulative probability of 60%</span></span>
-<span><span class="fu">quantile</span><span class="op">(</span><span class="va">covid_serialint</span>, p <span class="op">=</span> <span class="fl">0.6</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 4.618906</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb53">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># generate 10 random values (days) given</span></span>
-<span><span class="co"># the distribution family and its parameters</span></span>
-<span><span class="fu">generate</span><span class="op">(</span><span class="va">covid_serialint</span>, times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code> [1] 2.889422 3.306235 3.308088 4.208431 2.248277 2.692921 1.221014 9.998734
- [9] 3.758726 9.042027</code></pre>
-</div>
-
-<div id="window-for-contact-tracing-and-the-serial-interval" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="window-for-contact-tracing-and-the-serial-interval" class="callout-inner">
-<h3 class="callout-title">Window for contact tracing and the Serial
-interval<a class="anchor" aria-label="anchor" href="#window-for-contact-tracing-and-the-serial-interval"></a>
-</h3>
-<div class="callout-content">
-<p>The <strong>serial interval</strong> is important in the optimization
-of contact tracing since it provides a time window for the containment
-of a disease spread (<a href="https://academic.oup.com/aje/article/158/11/1039/162725" class="external-link">Fine,
-2003</a>). Depending on the serial interval, we can evaluate the need to
-expand the number of days pre-onset to consider in the contact tracing
-to include more backwards contacts (<a href="https://assets.publishing.service.gov.uk/media/61e9ab3f8fa8f50597fb3078/S0523_Oxford_-_Backwards_contact_tracing.pdf" class="external-link">Davis
-et al., 2020</a>).</p>
-<p>With the COVID-19 serial interval (<code>covid_serialint</code>)
-calculate:</p>
-<ul>
-<li>How much more of the backward cases could be captured if the contact
-tracing method considered contacts up to 6 days pre-onset compared to 2
-days pre-onset?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint6" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint6" aria-expanded="false" aria-controls="collapseHint6">
-  <h4 class="accordion-header" id="headingHint6">Give me a hint</h4>
-</button>
-<div id="collapseHint6" class="accordion-collapse collapse" data-bs-parent="#accordionHint6" aria-labelledby="headingHint6">
-<div class="accordion-body">
-<p>In Figure 5 from the <a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">R
-probability functions for the normal distribution</a>, the shadowed
-section represents a cumulative probability of <code>0.997</code> for
-the quantile value at <code>x = 2</code>.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
-  <h4 class="accordion-header" id="headingSolution6">Show me the solution</h4>
-</button>
-<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb55">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb56">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
-<span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">6</span><span class="op">)</span></span></code></pre>
-</div>
-<p>Given the COVID-19 serial interval:</p>
-<ul>
-<li><p>A contact tracing method considering contacts up to 2 days
-pre-onset will capture around 11.1% of backward cases.</p></li>
-<li><p>If this period is extended to 6 days pre-onset, this could
-include 76.2% of backward contacts.</p></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution7" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
-  <h4 class="accordion-header" id="headingSolution7">What if</h4>
-</button>
-<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
-<div class="accordion-body">
-<p>If we exchange the question between days and cumulative probability
-to:</p>
-<ul>
-<li>When considering secondary cases, how many days following the
-symptom onset of primary cases can we expect 55% of symptom onset to
-occur?</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb57">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">quantile</span><span class="op">(</span><span class="va">covid_serialint</span>, p <span class="op">=</span> <span class="fl">0.55</span><span class="op">)</span></span></code></pre>
-</div>
-<p>An interpretation could be:</p>
-<ul>
-<li>The 55% percent of the symptom onset of secondary cases will happen
-after 4.2 days after the symptom onset of primary cases.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</section><section id="discretize-a-continuous-distribution"><h2 class="section-heading">Discretize a continuous distribution<a class="anchor" aria-label="anchor" href="#discretize-a-continuous-distribution"></a>
-</h2>
-<hr class="half-width">
-<p>We are getting closer to the end! <code>EpiNow2::dist_spec()</code>
-still needs a maximum value (<code>max</code>).</p>
-<p>One way to do this is to get the quantile value for the
-distribution’s 99.9th percentile or <code>0.999</code> cumulative
-probability. For this, we need access to the set of distribution
-functions for our <code>&lt;epidist&gt;</code> object.</p>
-<p>We can use the set of distribution functions for a
-<em>continuous</em> distribution (as above). However, these values will
-be <em>continuous</em> numbers. We can <strong>discretize</strong> the
-continuous distribution stored in our <code>&lt;epidist&gt;</code>
-object to get discrete values from a continuous distribution.</p>
-<p>When we <code>epiparameter::discretise()</code> the continuous
-distribution we get a <strong>discrete</strong>(-ized) distribution:</p>
-<div class="codewrapper sourceCode" id="cb58">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint_discrete</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_serialint_discrete</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: discrete lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<p>We identify this change in the <code>Distribution:</code> output line
-of the <code>&lt;epidist&gt;</code> object. Take a double check to this
-line:</p>
-<pre><code>Distribution: discrete lnorm</code></pre>
-<p>While for a <strong>continuous</strong> distribution, we plot the
-<em>Probability Density Function (PDF)</em>, for a
-<strong>discrete</strong> distribution, we plot the <em>Probability Mass
-Function (PMF)</em>:</p>
-<div class="codewrapper sourceCode" id="cb61">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># continuous</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># discrete</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint_discrete</span><span class="op">)</span></span></code></pre>
-</div>
-<p>To finally get a <code>max</code> value, let’s access the quantile
-value of the 99.9th percentile or <code>0.999</code> probability of the
-distribution with the <code>prob_dist$q</code> notation, similarly to
-how we access the <code>summary_stats</code> values.</p>
-<div class="codewrapper sourceCode" id="cb62">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint_discrete_max</span> <span class="op">&lt;-</span></span>
-<span>  <span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="lenght-of-quarantine-and-incubation-period" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="lenght-of-quarantine-and-incubation-period" class="callout-inner">
-<h3 class="callout-title">Lenght of quarantine and Incubation
-period<a class="anchor" aria-label="anchor" href="#lenght-of-quarantine-and-incubation-period"></a>
-</h3>
-<div class="callout-content">
-<p>The <strong>incubation period</strong> distribution is a useful delay
-to assess the length of active monitoring or quarantine (<a href="https://www.acpjournals.org/doi/10.7326/M20-0504" class="external-link">Lauer et al.,
-2020</a>). Similarly, delays from symptom onset to recovery (or death)
-will determine the required duration of health-care and case isolation
-(<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371" class="external-link">Cori
-et al., 2017</a>).</p>
-<p>Calculate:</p>
-<ul>
-<li>Within what exact time frame do 99% of individuals who develop
-COVID-19 symptoms exhibit them after infection?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint7" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint7" aria-expanded="false" aria-controls="collapseHint7">
-  <h4 class="accordion-header" id="headingHint7">Give me a hint</h4>
-</button>
-<div id="collapseHint7" class="accordion-collapse collapse" data-bs-parent="#accordionHint7" aria-labelledby="headingHint7">
-<div class="accordion-body">
-<p>What delay distribution measures the time between infection and the
-onset of symptoms?</p>
-<p>The probability function for <code>&lt;epidist&gt;</code>
-<strong>discrete</strong> distributions differ from the
-<em>continuous</em> ones!</p>
-<div class="codewrapper sourceCode" id="cb63">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># plot to have a visual reference</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint_discrete</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># density value at quantile value 10 (day)</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">d</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># cumulative probability at quantile value 10 (day)</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">cdf</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># In what quantile value (days) do we have the 60% cumulative probability?</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.6</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># generate random values</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">r</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution8" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
-  <h4 class="accordion-header" id="headingSolution8">Show me the solution</h4>
-</button>
-<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb64">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_incubation</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>    single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.99</span><span class="op">)</span></span></code></pre>
-</div>
-<p>99% of those who develop COVID-19 symptoms will do so within 16 days
-of infection.</p>
-<p>Now, <em>Is this result expected in epidemiological terms?</em></p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution9" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
-  <h4 class="accordion-header" id="headingSolution9">how to create a distribution plot?</h4>
-</button>
-<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
-<div class="accordion-body">
-<p>From a maximum value with <code>$prob_dist$q()</code>, we can create
-a sequence of quantile values as a numeric vector and map density values
-for each:</p>
-<div class="codewrapper sourceCode" id="cb65">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># create a discrete distribution visualization</span></span>
-<span><span class="co"># from a maximum value from the distribution</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.999</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># generate quantile values</span></span>
-<span>  <span class="co"># as a sequence for each natural number</span></span>
-<span>  <span class="fu">seq</span><span class="op">(</span><span class="fl">1L</span>, to <span class="op">=</span> <span class="va">.</span>, by <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># coerce numeric vector to data frame</span></span>
-<span>  <span class="fu">as_tibble_col</span><span class="op">(</span>column_name <span class="op">=</span> <span class="st">"quantile_values"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">mutate</span><span class="op">(</span></span>
-<span>    <span class="co"># map density values</span></span>
-<span>    <span class="co"># for each quantile in the density function</span></span>
-<span>    density_values <span class="op">=</span></span>
-<span>      <span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">d</span><span class="op">(</span><span class="va">quantile_values</span><span class="op">)</span></span>
-<span>  <span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># create plot</span></span>
-<span>  <span class="fu">ggplot</span><span class="op">(</span></span>
-<span>    <span class="fu">aes</span><span class="op">(</span></span>
-<span>      x <span class="op">=</span> <span class="va">quantile_values</span>,</span>
-<span>      y <span class="op">=</span> <span class="va">density_values</span></span>
-<span>    <span class="op">)</span></span>
-<span>  <span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="plug-in-epiparameter-to-epinow2"><h2 class="section-heading">Plug-in <code>{epiparameter}</code> to <code>{EpiNow2}</code><a class="anchor" aria-label="anchor" href="#plug-in-epiparameter-to-epinow2"></a>
-</h2>
-<hr class="half-width">
-<p>Now we can plug everything into the <code>EpiNow2::dist_spec()</code>
-function!</p>
-<div class="codewrapper sourceCode" id="cb66">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">serial_interval_covid</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">covid_serialint_discrete_max</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_covid</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>
-  Fixed distribution with PMF [0.18 0.11 0.08 0.066 0.057 0.05 0.045 0.041 0.037 0.034 0.032 0.03 0.028 0.027 0.025 0.024 0.023 0.022 0.021 0.02 0.019 0.019 0.018]</code></pre>
-</div>
-<div id="warning" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="warning" class="callout-inner">
-<h3 class="callout-title">Warning<a class="anchor" aria-label="anchor" href="#warning"></a>
-</h3>
-<div class="callout-content">
-<p>Using the serial interval instead of the generation time is an
-alternative that can propagate bias in your estimates, even more so in
-diseases with reported pre-symptomatic transmission.</p>
-</div>
-</div>
-</div>
-<p>Let’s replace the <code>generation_time</code> input we used for
-<code>EpiNow2::epinow()</code>.</p>
-<div class="codewrapper sourceCode" id="cb68">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="ebolas-effective-reproduction-number" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-effective-reproduction-number" class="callout-inner">
-<h3 class="callout-title">Ebola’s effective reproduction number<a class="anchor" aria-label="anchor" href="#ebolas-effective-reproduction-number"></a>
-</h3>
-<div class="callout-content">
-<p>Download and read the <a href="data/ebola_cases.csv">Ebola
-dataset</a>:</p>
-<ul>
-<li>Reuse one epidemiological parameter to estimate the effective
-reproduction number for the Ebola dataset.</li>
-<li>Why did you choose that parameter?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint8" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint8" aria-expanded="false" aria-controls="collapseHint8">
-  <h4 class="accordion-header" id="headingHint8">Give me a hint</h4>
-</button>
-<div id="collapseHint8" class="accordion-collapse collapse" data-bs-parent="#accordionHint8" aria-labelledby="headingHint8">
-<div class="accordion-body">
-<p>To calculate the <span class="math inline">\(R_t\)</span>, we
-need:</p>
-<ul>
-<li>data set with confirmed cases per day and</li>
-<li>one key delay distribution</li>
-</ul>
-<p>Key functions we applied in this episode are:</p>
-<ul>
-<li><code>epidist_db()</code></li>
-<li><code>list_distributions()</code></li>
-<li><code>discretise()</code></li>
-<li>probability functions for continuous and discrete distributions</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution10" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution10" aria-expanded="false" aria-controls="collapseSolution10">
-  <h4 class="accordion-header" id="headingSolution10">Show me the solution</h4>
-</button>
-<div id="collapseSolution10" class="accordion-collapse collapse" data-bs-parent="#accordionSolution10" aria-labelledby="headingSolution10">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb69">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># read data</span></span>
-<span><span class="co"># e.g.: if path to file is data/raw-data/ebola_cases.csv then:</span></span>
-<span><span class="va">ebola_confirmed</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">read_csv</span><span class="op">(</span><span class="fu">here</span><span class="fu">::</span><span class="fu">here</span><span class="op">(</span><span class="st">"data"</span>, <span class="st">"raw-data"</span>, <span class="st">"ebola_cases.csv"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># list distributions</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"ebola"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution</span></span>
-<span><span class="va">ebola_serial</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_serial_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_serial</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># name of the type of distribution</span></span>
-<span><span class="co"># only for the discretised distribution</span></span>
-<span><span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="va">name</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">ebola_confirmed</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_ebola</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="adjusting-for-reporting-delays"><h2 class="section-heading">Adjusting for reporting delays<a class="anchor" aria-label="anchor" href="#adjusting-for-reporting-delays"></a>
-</h2>
-<hr class="half-width">
-<p>Estimating <span class="math inline">\(R_t\)</span> requires data on
-the daily number of new infections. Due to lags in the development of
-detectable viral loads, symptom onset, seeking care, and reporting,
-these numbers are not readily available. All observations reflect
-transmission events from some time in the past. In other words, if <span class="math inline">\(d\)</span> is the delay from infection to
-observation, then observations at time <span class="math inline">\(t\)</span> inform <span class="math inline">\(R_{t−d}\)</span>, not <span class="math inline">\(R_t\)</span>. <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">(Gostic
-et al., 2020)</a></p>
-<figure><img src="fig/disease-reporting.jpg" alt="" class="figure mx-auto d-block"><figcaption>Timeline for chain of disease reporting, the
-Netherlands. Lab, laboratory; PHA, public health authority. From <a href="https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article" class="external-link">Marinović
-et al., 2015</a></figcaption></figure><p>The <strong>delay distribution</strong> could be inferred jointly
-with the underlying times of infection or estimated as the sum of the
-<strong><a href="reference.html#incubation">incubation
-period</a></strong> distribution and the distribution of delays from
-symptom onset to observation from line list data <strong>(<a href="reference.html#reportingdelay">reporting delay</a>)</strong>.</p>
-<p>For <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, we can specify these two complementary
-delay distributions in the <code>delays</code> argument.</p>
-<figure><img src="fig/rt-adjusting-delays.png" alt="" class="figure mx-auto d-block"><figcaption>Rt is a measure of transmission at time t.
-Observations after time t must be adjusted. ICU, intensive care unit.
-From <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">Gostic
-et al., 2020</a></figcaption></figure><div id="reuse-an-incubation-period-for-covid-19" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="reuse-an-incubation-period-for-covid-19" class="callout-inner">
-<h3 class="callout-title">Reuse an Incubation period for COVID-19<a class="anchor" aria-label="anchor" href="#reuse-an-incubation-period-for-covid-19"></a>
-</h3>
-<div class="callout-content">
-<p>Use <code>{epiparameter}</code> to:</p>
-<ul>
-<li>Find an incubation period for COVID-19.</li>
-<li>Add our last <code>epinow()</code> code chunk using the
-<code>delays</code> argument and the <code>delay_opts()</code> helper
-function.</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint9" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint9" aria-expanded="false" aria-controls="collapseHint9">
-  <h4 class="accordion-header" id="headingHint9">Give me a hint</h4>
-</button>
-<div id="collapseHint9" class="accordion-collapse collapse" data-bs-parent="#accordionHint9" aria-labelledby="headingHint9">
-<div class="accordion-body">
-<p>The <code>delays</code> argument and the <code>delay_opts()</code>
-helper function are analogous to the <code>generation_time</code>
-argument and the <code>generation_time_opts()</code> helper
-function.</p>
-<div class="codewrapper sourceCode" id="cb70">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_time_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution11" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution11" aria-expanded="false" aria-controls="collapseSolution11">
-  <h4 class="accordion-header" id="headingSolution11">Show me the solution</h4>
-</button>
-<div id="collapseSolution11" class="accordion-collapse collapse" data-bs-parent="#accordionSolution11" aria-labelledby="headingSolution11">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb71">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_incubation</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Natalie"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">incubation_time_covid</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="va">covid_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>  sd <span class="op">=</span> <span class="va">covid_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>  max <span class="op">=</span> <span class="va">covid_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_time_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution12" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution12" aria-expanded="false" aria-controls="collapseSolution12">
-  <h4 class="accordion-header" id="headingSolution12">How much has it changed?</h4>
-</button>
-<div id="collapseSolution12" class="accordion-collapse collapse" data-bs-parent="#accordionSolution12" aria-labelledby="headingSolution12">
-<div class="accordion-body">
-<p>After adding the incubation period, discuss:</p>
-<ul>
-<li>Does the retrospective trend of forecast change?</li>
-<li>Has the uncertainty changed?</li>
-<li>How would you explain or interpret any of these changes?</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays" class="callout-inner">
-<h3 class="callout-title">Ebola’s effective reproduction number was
-adjusted by reporting delays<a class="anchor" aria-label="anchor" href="#ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays"></a>
-</h3>
-<div class="callout-content">
-<p>Using the same <a href="data/ebola_cases.csv">Ebola dataset</a>:</p>
-<ul>
-<li>Reuse one additional epidemiological parameter for the
-<code>delays</code> argument in <code>EpiNow2::epinow()</code>.</li>
-<li>Estimate the effective reproduction number using
-<code>EpiNow2::epinow()</code>.</li>
-<li>Why did you choose that parameter?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint10" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint10" aria-expanded="false" aria-controls="collapseHint10">
-  <h4 class="accordion-header" id="headingHint10">Give me a hint</h4>
-</button>
-<div id="collapseHint10" class="accordion-collapse collapse" data-bs-parent="#accordionHint10" aria-labelledby="headingHint10">
-<div class="accordion-body">
-<p>We can use two complementary delay distributions to estimate the
-<span class="math inline">\(R_t\)</span> at time <span class="math inline">\(t\)</span>.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution13" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution13" aria-expanded="false" aria-controls="collapseSolution13">
-  <h4 class="accordion-header" id="headingSolution13">Show me the solution</h4>
-</button>
-<div id="collapseSolution13" class="accordion-collapse collapse" data-bs-parent="#accordionSolution13" aria-labelledby="headingSolution13">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb72">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># read data</span></span>
-<span><span class="co"># e.g.: if path to file is data/raw-data/ebola_cases.csv then:</span></span>
-<span><span class="va">ebola_confirmed</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">read_csv</span><span class="op">(</span><span class="fu">here</span><span class="fu">::</span><span class="fu">here</span><span class="op">(</span><span class="st">"data"</span>, <span class="st">"raw-data"</span>, <span class="st">"ebola_cases.csv"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># list distributions</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"ebola"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution for the generation time</span></span>
-<span><span class="va">ebola_serial</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_serial_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_serial</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution for delay of the incubation period</span></span>
-<span><span class="va">ebola_incubation</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">incubation_period_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">ebola_confirmed</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_ebola</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_period_ebola</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="extract-parameters" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="extract-parameters" class="callout-inner">
-<h3 class="callout-title">Extract parameters<a class="anchor" aria-label="anchor" href="#extract-parameters"></a>
-</h3>
-<div class="callout-content">
-<p>Use the <code>influenza_england_1978_school</code> from the
-<a href="https://github.com/reconhub/outbreaks" class="external-link">outbreaks</a> package to calculate the effective reproduction
-number.</p>
-</div>
-</div>
-</div>
-<div id="accordionHint11" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint11" aria-expanded="false" aria-controls="collapseHint11">
-  <h4 class="accordion-header" id="headingHint11">Give me a hint</h4>
-</button>
-<div id="collapseHint11" class="accordion-collapse collapse" data-bs-parent="#accordionHint11" aria-labelledby="headingHint11">
-<div class="accordion-body">
-<p>How to get the mean and standard deviation from a generation time
-with median and quantiles as summary statistics?</p>
-<ul>
-<li>Look at how to extract parameters from <code>{epiparameter}</code>
-vignette on <a href="https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html" class="external-link">parameter
-extraction and conversion</a>
-</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution14" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution14" aria-expanded="false" aria-controls="collapseSolution14">
-  <h4 class="accordion-header" id="headingSolution14">Show me the solution</h4>
-</button>
-<div id="collapseSolution14" class="accordion-collapse collapse" data-bs-parent="#accordionSolution14" aria-labelledby="headingSolution14">
-<div class="accordion-body">
-<!-- https://github.com/epiverse-trace/epiparameter/issues/114 -->
-<div class="codewrapper sourceCode" id="cb73">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># What parameters are available for Influenza?</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"influenza"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">as_tibble</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">count</span><span class="op">(</span><span class="va">epi_distribution</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_generation</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"generation"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_generation_discrete</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">discretise</span><span class="op">(</span><span class="va">influenza_generation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># problem</span></span>
-<span><span class="co"># the summary statistics do not have mean and sd</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">median</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span></span>
-<span></span>
-<span><span class="co"># solution</span></span>
-<span><span class="co"># extract parameters from percentiles</span></span>
-<span><span class="va">influenza_extracted</span> <span class="op">&lt;-</span> <span class="fu">extract_param</span><span class="op">(</span></span>
-<span>  type <span class="op">=</span> <span class="st">"percentiles"</span>,</span>
-<span>  values <span class="op">=</span> <span class="fu">c</span><span class="op">(</span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
-<span>             <span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lnorm"</span>,</span>
-<span>  percentiles <span class="op">=</span> <span class="fu">c</span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.95</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_extracted</span></span>
-<span></span>
-<span><span class="va">generation_time_influenza</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">influenza_extracted</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">influenza_extracted</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">influenza_generation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_cleaned</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">outbreaks</span><span class="fu">::</span><span class="va">influenza_england_1978_school</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">select</span><span class="op">(</span><span class="va">date</span>, confirm <span class="op">=</span> <span class="va">in_bed</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">influenza_cleaned</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time_influenza</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="when-to-reuse-when-to-estimate"><h2 class="section-heading">When to reuse? When to estimate?<a class="anchor" aria-label="anchor" href="#when-to-reuse-when-to-estimate"></a>
-</h2>
-<hr class="half-width">
-<p>In the early stage of an outbreak, we can rely on reusing parameters
-for known pathogens to unknown ones, like for the <a href="https://en.wikipedia.org/wiki/Disease_X" class="external-link">Disease X</a>, a pathogen
-currently unknown to cause human disease and potentially cause a serious
-international epidemic (<a href="https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts" class="external-link">WHO,
-2018</a>).</p>
-<p>But when data from <strong>lines list paired with contact
-tracing</strong> is available, we can estimate the key delay
-distributions that best fit our data. These will help us to inform,
-compare and update any previous estimate about questions like:</p>
-<ul>
-<li>How long should contacts be followed?</li>
-<li>What is the required duration of contact tracing?</li>
-<li>How long should cases be isolated to reduce transmission?</li>
-</ul>
-<p>However, the methods to accurately estimate delays like the
-generation interval from contact tracing data involve adjusting for
-biases like <strong><a href="reference.html#censoring">censoring</a></strong>, <strong><a href="reference.html#truncation">right truncation</a></strong> and
-<strong><a href="reference.html#phasebias">epidemic phase
-bias</a></strong>. <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">(Gostic
-et al., 2020)</a></p>
-<p>We can identify what entries in the <code>{epiparameter}</code>
-library assessed for these biases in their methodology with the
-<code>method_assess</code> nested entry:</p>
-<div class="codewrapper sourceCode" id="cb74">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">method_assess</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>$censored
-[1] TRUE
-
-$right_truncated
-[1] TRUE
+                </div>
+              </div>
+            </div>
 
-$phase_bias_adjusted
-[1] FALSE</code></pre>
-</div>
-<div id="how-to-estimate-delay-distributions-for-disease-x" class="callout testimonial">
-<div class="callout-square">
-<i class="callout-icon" data-feather="heart"></i>
-</div>
-<div id="how-to-estimate-delay-distributions-for-disease-x" class="callout-inner">
-<h3 class="callout-title">How to estimate delay distributions for
-Disease X?<a class="anchor" aria-label="anchor" href="#how-to-estimate-delay-distributions-for-disease-x"></a>
-</h3>
-<div class="callout-content">
-<p>Refer to this excellent tutorial on estimating the serial interval
-and incubation period of Disease X accounting for <em>censoring</em>
-using Bayesian inference with packages like <a href="https://mc-stan.org/rstan/" class="external-link">rstan</a> and
-<a href="https://cran.r-project.org/package=coarseDataTools" class="external-link">coarseDataTools</a>.</p>
-<ul>
-<li>Tutorial in English: <a href="https://rpubs.com/tracelac/diseaseX" class="external-link uri">https://rpubs.com/tracelac/diseaseX</a>
-<!-- to request -->
-</li>
-<li>Tutorial en Español: <a href="https://epiverse-trace.github.io/epimodelac/EnfermedadX.html" class="external-link uri">https://epiverse-trace.github.io/epimodelac/EnfermedadX.html</a>
-</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionSpoiler3" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler3" aria-expanded="false" aria-controls="collapseSpoiler3">
-  <h3 class="accordion-header" id="headingSpoiler3">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>How to assess for pre-symptomatic infection?</h3>
-</button>
-<div id="collapseSpoiler3" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler3" aria-labelledby="headingSpoiler3">
-<div class="accordion-body">
-<p>The lengths of the Serial interval and Incubation period determine
-the type of disease transmission.</p>
-<figure><img src="fig/incubation-period-serial-interval.jpg" alt="" class="figure mx-auto d-block"><figcaption>The relationship between the incubation period and
-serial interval. From <a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-2020</a></figcaption></figure><p>Estimating the proportion of pre-symptomatic infections, or the
-extent to which infectiousness precedes symptom onset will determine the
-effectiveness of contact tracing and the feasibility of controlling an
-outbreak (<a href="https://www.pnas.org/doi/full/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a> and <a href="https://www.thelancet.com/article/S2214-109X(20)30074-7/fulltext" class="external-link">Hellewell
-et al., 2020</a>).</p>
-<!-- The proportion of pre-symptomatic transmission contributes to the effective reproduction number and expected new cases. -->
-<figure><img src="fig/reproduction-number-pre-symptomatic.png" alt="" class="figure mx-auto d-block"><figcaption>Parameter estimates. Plausible ranges for the key
-parameters R0 and θ (read the main text for sources) for four viral
-infections of public concern are shown as shaded regions. The size of
-the shaded area reflects the uncertainties in the parameter estimates.
-<a href="https://www.pnas.org/doi/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a></figcaption></figure><p>Meta-analysis on the proportion of pre-symptomatic and asymptomatic
-transmission in SARS-CoV-2 found limitations of the evidence given high
-heterogeneity and high risk of selection and information bias between
-studies (<a href="https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003" class="external-link">Buitrago-Garcia
-et al., 2022</a>). This is a call to action to improve the Outbreak
-Analytic pipelines to use and reuse in the early phase of an
-outbreak.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="what-type-of-transmission" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="what-type-of-transmission" class="callout-inner">
-<h3 class="callout-title">What type of transmission?<a class="anchor" aria-label="anchor" href="#what-type-of-transmission"></a>
-</h3>
-<div class="callout-content">
-<p>Compare the serial interval and incubation period of Influenza and
-MERS:</p>
-<ul>
-<li>What type of transmission has Influenza?</li>
-<li>What type of transmission has MERS?</li>
-<li>Do these results correlate with the available evidence?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint12" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint12" aria-expanded="false" aria-controls="collapseHint12">
-  <h4 class="accordion-header" id="headingHint12">Give me a hint</h4>
-</button>
-<div id="collapseHint12" class="accordion-collapse collapse" data-bs-parent="#accordionHint12" aria-labelledby="headingHint12">
-<div class="accordion-body">
-<p>For types of transmission, we refer to infections with symptomatic or
-pre-symptomatic transmission.</p>
-<p>Key functions:</p>
-<ul>
-<li><code>epidist_db()</code></li>
-<li><code>epidist$summary_stats$</code></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution15" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution15" aria-expanded="false" aria-controls="collapseSolution15">
-  <h4 class="accordion-header" id="headingSolution15">Show me the solution</h4>
-</button>
-<div id="collapseSolution15" class="accordion-collapse collapse" data-bs-parent="#accordionSolution15" aria-labelledby="headingSolution15">
-<div class="accordion-body">
-<p>In this solution we use <code>purrr::pluck()</code> to extract
-elements within the <code>summary_stats</code> object which is of class
-<code>list</code>.</p>
-<div class="codewrapper sourceCode" id="cb76">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># pre-symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"mers"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"median"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"mers"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb77">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># pre-symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Stephen"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Nishiura"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
+            <hr class="half-width">
+<div class="accordion accordion-flush resources" id="accordionFlush12">
+              <div class="accordion-item">
+                <h2 class="accordion-header" id="flush-headingTwelve">
+                  <button class="accordion-button collapsed" id="resources" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapseTwelve" aria-expanded="false" aria-controls="flush-collapseTwelve">
+                    RESOURCES
+                  </button>
+                </h2>
+                <div id="flush-collapseTwelve" class="accordion-collapse collapse" aria-labelledby="flush-headingTwelve" data-bs-parent="#accordionFlush12">
+                  <div class="accordion-body">
+                    <ul>
+<li>
+                        <a href="key-points.html">Key Points</a>
+                      </li>
+                      <li>
+                        <a href="reference.html#glossary">Glossary</a>
+                      </li>
+                      <li>
+                        <a href="profiles.html">Learner Profiles</a>
+                      </li>
+                      <li><a href="reference.html">Glossary of Terms: Epiverse-TRACE</a></li>
+                    </ul>
 </div>
-<!--
-## Concept map
+                </div>
+              </div>
+            </div>
+            <hr class="half-width resources">
+<a href="aio.html">See all in one page</a>
+            
 
-update it from last epiparameter test 
--->
-<div id="keypoints1" class="callout keypoints">
-<div class="callout-square">
-<i class="callout-icon" data-feather="key"></i>
-</div>
-<div class="callout-inner">
-<h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="#keypoints1"></a>
-</h3>
-<div class="callout-content">
-<ul>
-<li>Use <code>{epiparameter}</code> to access the systematic review
-catalogue of epidemiological delay distributions.</li>
-<li>Use <code>epidist_db()</code> to select single delay
-distributions.</li>
-<li>Use <code>list_distributions()</code> for an overview of multiple
-delay distributions.</li>
-<li>Use <code>discretise()</code> to convert continuous to discrete
-delay distributions.</li>
-<li>Use <code>{epiparameter}</code> probability functions for any delay
-distributions.</li>
-</ul>
-</div>
-</div>
+            <hr class="d-none d-sm-block d-md-none">
+<div class="d-grid gap-1">
+            
+            </div>
+          </div>
+<!-- /div.accordion -->
+        </div>
+<!-- /div.sidebar-inner -->
+      </nav>
 </div>
-<!-- 
-Place links that you need to refer to multiple times across pages here. Delete
-any links that you are not going to use. 
- -->
-</section></section><section id="aio-quantify-transmissibility"><p>Content from <a href="quantify-transmissibility.html">Quantifying transmission</a></p>
+<!-- /div.sidebar -->
+  </div>
+<!-- /div.sidebar-col -->
+<!-- END:   inst/pkgdown/templates/navbar.html-->
+
+        <!-- START: inst/pkgdown/templates/content-extra.html -->
+  <div class="col-xl-8 col-lg-12 primary-content">
+    <main id="main-content" class="main-content"><div class="container lesson-content">
+        
+        
+<section id="aio-quantify-transmissibility"><p>Content from <a href="quantify-transmissibility.html">Quantifying transmission</a></p>
 <hr>
-<p>Last updated on 2024-02-08 |
+<p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/quantify-transmissibility.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <div class="text-end">
@@ -3367,11 +742,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:24:53] epinow: There were 12 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 13:39:13] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 10:24:53] epinow: Examine the pairs() plot to diagnose sampling problems
- - </code></pre>
+WARN [2024-02-12 13:39:13] epinow: Examine the pairs() plot to diagnose sampling problems
+ - 
+WARN [2024-02-12 13:39:15] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
+Running the chains for more iterations may help. See
+https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
 </div>
 <div class="section level3">
 <h3 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a>
@@ -3413,24 +791,24 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date     7232 (4056 -- 12590)
+1: New confirmed cases by infection date     7340 (4136 -- 12899)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.57 -- 1.3)
-4:                        Rate of growth -0.015 (-0.064 -- 0.039)
-5:          Doubling/halving time (days)          -47 (18 -- -11)</code></pre>
+3:            Effective reproduction no.        0.9 (0.58 -- 1.4)
+4:                        Rate of growth -0.013 (-0.062 -- 0.042)
+5:          Doubling/halving time (days)          -54 (17 -- -11)</code></pre>
 </div>
 <p>As these estimates are based on partial data, they have a wide
 uncertainty interval.</p>
 <ul>
 <li><p>From the summary of our analysis we see that the expected change
 in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4056 – 12590).</p></li>
+cases 7340 (4136 – 12899).</p></li>
 <li><p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 0.89 (0.57 – 1.3).</p></li>
-<li><p>The exponential growth rate of case numbers is -0.015 (-0.064 –
-0.039).</p></li>
+data) is 0.9 (0.58 – 1.4).</p></li>
+<li><p>The exponential growth rate of case numbers is -0.013 (-0.062 –
+0.042).</p></li>
 <li><p>The doubling time (the time taken for case numbers to double) is
--47 (18 – -11).</p></li>
+-54 (17 – -11).</p></li>
 </ul>
 <div id="expected-change-in-daily-cases" class="callout callout">
 <div class="callout-square">
@@ -3537,17 +915,17 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>INFO [2024-02-08 10:24:58] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-02-08 10:24:58] Reporting estimates using data up to: 2020-04-28
-INFO [2024-02-08 10:24:58] No target directory specified so returning output
-INFO [2024-02-08 10:24:58] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-02-08 10:24:58] Regions excluded: none
-INFO [2024-02-08 11:08:54] Completed regional estimates
-INFO [2024-02-08 11:08:54] Regions with estimates: 13
-INFO [2024-02-08 11:08:54] Regions with runtime errors: 0
-INFO [2024-02-08 11:08:54] Producing summary
-INFO [2024-02-08 11:08:54] No summary directory specified so returning summary output
-INFO [2024-02-08 11:08:54] No target directory specified so returning timings</code></pre>
+<pre class="output" tabindex="0"><code>INFO [2024-02-12 13:39:18] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-02-12 13:39:18] Reporting estimates using data up to: 2020-04-28
+INFO [2024-02-12 13:39:18] No target directory specified so returning output
+INFO [2024-02-12 13:39:18] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-02-12 13:39:18] Regions excluded: none
+INFO [2024-02-12 14:27:58] Completed regional estimates
+INFO [2024-02-12 14:27:58] Regions with estimates: 13
+INFO [2024-02-12 14:27:58] Regions with runtime errors: 0
+INFO [2024-02-12 14:27:58] Producing summary
+INFO [2024-02-12 14:27:58] No summary directory specified so returning summary output
+INFO [2024-02-12 14:27:58] No target directory specified so returning timings</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3560,49 +938,49 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                      Region New confirmed cases by infection date
                       &lt;char&gt;                                &lt;char&gt;
- 1:            East Midlands                      340 (211 -- 551)
- 2:          East of England                      533 (332 -- 847)
- 3:                  England                   3548 (2264 -- 5694)
- 4:                   London                      292 (193 -- 460)
- 5:               North East                      251 (141 -- 430)
- 6:               North West                      566 (327 -- 870)
- 7:         Northern Ireland                         45 (23 -- 86)
- 8:                 Scotland                      289 (160 -- 522)
- 9:               South East                      599 (360 -- 996)
-10:               South West                      423 (294 -- 623)
-11:                    Wales                        95 (64 -- 136)
-12:            West Midlands                      270 (141 -- 488)
-13: Yorkshire and The Humber                      478 (284 -- 790)
+ 1:            East Midlands                      345 (208 -- 547)
+ 2:          East of England                      535 (324 -- 843)
+ 3:                  England                   3570 (2204 -- 5553)
+ 4:                   London                      298 (193 -- 464)
+ 5:               North East                      258 (149 -- 425)
+ 6:               North West                      554 (319 -- 885)
+ 7:         Northern Ireland                         43 (23 -- 85)
+ 8:                 Scotland                      286 (158 -- 544)
+ 9:               South East                      582 (357 -- 962)
+10:               South West                      417 (294 -- 608)
+11:                    Wales                        95 (65 -- 137)
+12:            West Midlands                      268 (141 -- 494)
+13: Yorkshire and The Humber                      472 (283 -- 798)
     Expected change in daily cases Effective reproduction no.
                             &lt;fctr&gt;                     &lt;char&gt;
- 1:              Likely increasing          1.2 (0.85 -- 1.6)
- 2:              Likely increasing          1.2 (0.84 -- 1.6)
- 3:              Likely decreasing         0.91 (0.65 -- 1.3)
- 4:              Likely decreasing         0.78 (0.56 -- 1.1)
- 5:              Likely decreasing         0.91 (0.59 -- 1.3)
- 6:              Likely decreasing         0.87 (0.57 -- 1.2)
- 7:              Likely decreasing         0.65 (0.39 -- 1.1)
- 8:              Likely decreasing          0.92 (0.6 -- 1.4)
- 9:                         Stable         0.99 (0.68 -- 1.4)
+ 1:              Likely increasing          1.2 (0.84 -- 1.6)
+ 2:              Likely increasing          1.2 (0.82 -- 1.6)
+ 3:              Likely decreasing         0.92 (0.64 -- 1.3)
+ 4:              Likely decreasing         0.79 (0.56 -- 1.1)
+ 5:              Likely decreasing         0.92 (0.61 -- 1.3)
+ 6:              Likely decreasing         0.86 (0.57 -- 1.2)
+ 7:              Likely decreasing         0.64 (0.39 -- 1.1)
+ 8:              Likely decreasing         0.91 (0.58 -- 1.4)
+ 9:                         Stable         0.98 (0.68 -- 1.4)
 10:                     Increasing           1.4 (1.1 -- 1.8)
-11:                     Decreasing        0.57 (0.41 -- 0.75)
+11:                     Decreasing        0.57 (0.42 -- 0.75)
 12:              Likely decreasing          0.7 (0.42 -- 1.1)
 13:                         Stable            1 (0.69 -- 1.4)
                Rate of growth Doubling/halving time (days)
                        &lt;char&gt;                       &lt;char&gt;
- 1:    0.022 (-0.02 -- 0.069)               31 (10 -- -34)
- 2:   0.022 (-0.022 -- 0.066)               32 (11 -- -31)
- 3:  -0.011 (-0.051 -- 0.033)              -61 (21 -- -14)
- 4:   -0.03 (-0.066 -- 0.011)              -23 (61 -- -11)
- 5:   -0.012 (-0.06 -- 0.038)              -58 (18 -- -11)
- 6:  -0.017 (-0.063 -- 0.024)              -40 (29 -- -11)
- 7:  -0.05 (-0.098 -- 0.0087)               -14 (80 -- -7)
- 8:  -0.011 (-0.059 -- 0.043)              -62 (16 -- -12)
- 9: -0.00073 (-0.046 -- 0.05)             -940 (14 -- -15)
-10:    0.048 (0.012 -- 0.087)                 14 (8 -- 57)
-11: -0.064 (-0.094 -- -0.034)            -11 (-20 -- -7.3)
-12:  -0.042 (-0.092 -- 0.012)             -17 (57 -- -7.5)
-13:  0.0026 (-0.044 -- 0.052)              260 (13 -- -16)</code></pre>
+ 1:   0.024 (-0.022 -- 0.069)               29 (10 -- -31)
+ 2:   0.021 (-0.025 -- 0.065)               32 (11 -- -28)
+ 3:  -0.011 (-0.052 -- 0.031)              -63 (22 -- -13)
+ 4:  -0.029 (-0.066 -- 0.013)              -24 (55 -- -11)
+ 5:  -0.011 (-0.057 -- 0.038)              -63 (18 -- -12)
+ 6:  -0.019 (-0.065 -- 0.023)              -37 (31 -- -11)
+ 7: -0.052 (-0.099 -- 0.0082)               -13 (85 -- -7)
+ 8:   -0.012 (-0.062 -- 0.05)              -60 (14 -- -11)
+ 9: -0.0029 (-0.046 -- 0.046)             -240 (15 -- -15)
+10:    0.046 (0.013 -- 0.084)               15 (8.2 -- 54)
+11: -0.065 (-0.093 -- -0.035)            -11 (-20 -- -7.4)
+12:  -0.043 (-0.092 -- 0.015)             -16 (48 -- -7.5)
+13:  0.0019 (-0.045 -- 0.051)              370 (14 -- -15)</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3641,7 +1019,7 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
  -->
 </section></section><section id="aio-create-forecast"><p>Content from <a href="create-forecast.html">Create a short-term forecast</a></p>
 <hr>
-<p>Last updated on 2024-02-08 |
+<p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/create-forecast.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <div class="text-end">
@@ -3730,10 +1108,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:15:14] epinow: There were 5 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:34:58] epinow: There were 2 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:15:14] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:34:58] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <p>We can visualise the estimates of the effective reproduction number
@@ -3830,10 +1208,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:22:06] epinow: There were 4 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:41:30] epinow: There were 10 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:22:06] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:41:30] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb8">
@@ -3847,11 +1225,11 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date   18000 (10430 -- 33164)
+1: New confirmed cases by infection date   17828 (10162 -- 32312)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.59 -- 1.4)
-4:                        Rate of growth -0.014 (-0.061 -- 0.043)
-5:          Doubling/halving time (days)          -48 (16 -- -11)</code></pre>
+3:            Effective reproduction no.       0.88 (0.57 -- 1.3)
+4:                        Rate of growth -0.015 (-0.064 -- 0.042)
+5:          Doubling/halving time (days)          -45 (17 -- -11)</code></pre>
 </div>
 <p>The estimates of transmission measures such as the effective
 reproduction number and rate of growth are similar (or the same in
@@ -4038,7 +1416,7 @@ <h3 class="callout-title">Challenge<a class="anchor" aria-label="anchor" href="#
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
 <div class="accordion-body">
 <p>Ensure the data is in the correct format :</p>
 <ul>
@@ -4057,7 +1435,7 @@ <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1">SOLUTION</h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
 <div class="accordion-body">
 <p>To estimate the effective reproduction number and growth rate, we
 will use the function <code>epinow()</code>.</p>
@@ -4113,10 +1491,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:24:26] epinow: There were 14 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:43:44] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:24:26] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:43:44] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb20">
@@ -4130,15 +1508,15 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                estimate
                                   &lt;char&gt;                  &lt;char&gt;
-1: New confirmed cases by infection date         103 (49 -- 263)
+1: New confirmed cases by infection date         101 (49 -- 262)
 2:        Expected change in daily cases              Increasing
-3:            Effective reproduction no.        1.7 (1.1 -- 3.1)
-4:                        Rate of growth 0.044 (0.0062 -- 0.095)
-5:          Doubling/halving time (days)         16 (7.3 -- 110)</code></pre>
+3:            Effective reproduction no.          1.7 (1.1 -- 3)
+4:                        Rate of growth 0.042 (0.0048 -- 0.092)
+5:          Doubling/halving time (days)         17 (7.5 -- 140)</code></pre>
 </div>
 <p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 1.7 (1.1 – 3.1). The exponential growth rate of case numbers is
-0.044 (0.0062 – 0.095).</p>
+data) is 1.7 (1.1 – 3). The exponential growth rate of case numbers is
+0.042 (0.0048 – 0.092).</p>
 <p>Visualize the estimates:</p>
 <div class="codewrapper sourceCode" id="cb22">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -4227,9 +1605,9 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/aio.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/aio.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/create-forecast.html b/create-forecast.html
index f03f7174..f064478a 100644
--- a/create-forecast.html
+++ b/create-forecast.html
@@ -92,11 +92,11 @@
   Real-time analysis and forecasting for outbreak analytics with R
 </div>
 
-<aside class="col-md-12 lesson-progress"><div style="width: 47%" class="percentage">
-    47%
+<aside class="col-md-12 lesson-progress"><div style="width: 33%" class="percentage">
+    33%
   </div>
   <div class="progress incubator">
-    <div class="progress-bar incubator" role="progressbar" style="width: 47%" aria-valuenow="47" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
+    <div class="progress-bar incubator" role="progressbar" style="width: 33%" aria-valuenow="33" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
     </div>
   </div>
 </aside><div class="container">
@@ -153,25 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush3">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -183,7 +165,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
       <button class="accordion-button" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapsecurrent" aria-expanded="true" aria-controls="flush-collapsecurrent">
         <span class="visually-hidden">Current Chapter</span>
         <span class="current-chapter">
-        4. Create a short-term forecast
+        2. Create a short-term forecast
         </span>
       </button>
     </div><!--/div.accordion-header-->
@@ -259,7 +241,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Create a short-term forecast</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/create-forecast.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -346,10 +328,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:15:14] epinow: There were 5 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:34:58] epinow: There were 2 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:15:14] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:34:58] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <p>We can visualise the estimates of the effective reproduction number
@@ -443,10 +425,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:22:06] epinow: There were 4 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:41:30] epinow: There were 10 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:22:06] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:41:30] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb8">
@@ -460,11 +442,11 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date   18000 (10430 -- 33164)
+1: New confirmed cases by infection date   17828 (10162 -- 32312)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.59 -- 1.4)
-4:                        Rate of growth -0.014 (-0.061 -- 0.043)
-5:          Doubling/halving time (days)          -48 (16 -- -11)</code></pre>
+3:            Effective reproduction no.       0.88 (0.57 -- 1.3)
+4:                        Rate of growth -0.015 (-0.064 -- 0.042)
+5:          Doubling/halving time (days)          -45 (17 -- -11)</code></pre>
 </div>
 <p>The estimates of transmission measures such as the effective
 reproduction number and rate of growth are similar (or the same in
@@ -641,7 +623,7 @@ <h3 class="callout-title">Challenge<a class="anchor" aria-label="anchor" href="#
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
 <div class="accordion-body">
 <p>Ensure the data is in the correct format :</p>
 <ul><li>
@@ -658,7 +640,7 @@ <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1">SOLUTION</h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
 <div class="accordion-body">
 <p>To estimate the effective reproduction number and growth rate, we
 will use the function <code>epinow()</code>.</p>
@@ -714,10 +696,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:24:26] epinow: There were 14 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:43:44] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:24:26] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:43:44] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb20">
@@ -731,15 +713,15 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                estimate
                                   &lt;char&gt;                  &lt;char&gt;
-1: New confirmed cases by infection date         103 (49 -- 263)
+1: New confirmed cases by infection date         101 (49 -- 262)
 2:        Expected change in daily cases              Increasing
-3:            Effective reproduction no.        1.7 (1.1 -- 3.1)
-4:                        Rate of growth 0.044 (0.0062 -- 0.095)
-5:          Doubling/halving time (days)         16 (7.3 -- 110)</code></pre>
+3:            Effective reproduction no.          1.7 (1.1 -- 3)
+4:                        Rate of growth 0.042 (0.0048 -- 0.092)
+5:          Doubling/halving time (days)         17 (7.5 -- 140)</code></pre>
 </div>
 <p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 1.7 (1.1 – 3.1). The exponential growth rate of case numbers is
-0.044 (0.0062 – 0.095).</p>
+data) is 1.7 (1.1 – 3). The exponential growth rate of case numbers is
+0.042 (0.0048 – 0.092).</p>
 <p>Visualize the estimates:</p>
 <div class="codewrapper sourceCode" id="cb22">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -835,9 +817,9 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/create-forecast.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/create-forecast.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
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diff --git a/images.html b/images.html
index 0e71af7d..f18cd071 100644
--- a/images.html
+++ b/images.html
@@ -187,7 +187,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -199,31 +199,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -286,82 +262,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
     <main id="main-content" class="main-content"><div class="container lesson-content">
         
         
-<section id="introduction"><h2 class="section-heading"><a href="introduction.html">Outbreak analytics pipelines</a></h2>
-<hr class="half-width">
-<h3 id="introduction-figure-1">Figure 1</h3>
-<figure><img src="fig/introduction-rendered-unnamed-chunk-5-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="introduction-figure-2">Figure 2</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="https://epiverse-trace.github.io/task_pipeline-minimal.svg" alt="" class="figure mx-auto d-block"><figcaption>The outbreak analytics pipeline.</figcaption></figure><hr>
-<h3 id="introduction-figure-3">Figure 3</h3>
-<figure><img src="fig/pkgs-hexlogos.png" class="figure mx-auto d-block"></figure><hr></section><section id="read-delays"><h2 class="section-heading"><a href="read-delays.html">Read delays</a></h2>
-<hr class="half-width">
-<h3 id="read-delays-figure-1">Figure 1</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/time-periods.jpg" alt="" class="figure mx-auto d-block"><figcaption>Definition of key time periods. From <a href="https://www.sciencedirect.com/science/article/pii/S2468042721000038" class="external-link">Xiang
-et al, 2021</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-2">Figure 2</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/reproduction-generation-time.png" alt="" class="figure mx-auto d-block"><figcaption>Video from the MRC Centre for Global Infectious
-Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group
-with Dr Anne Cori (27-07-2023) at <a href="https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL" class="external-link uri">https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-3">Figure 3</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/serial-interval-observed.jpeg" alt="" class="figure mx-auto d-block"><figcaption>A schematic of the relationship of different time
-periods of transmission between an infector and an infectee in a
-transmission pair. Exposure window is defined as the time interval
-having viral exposure, and transmission window is defined as the time
-interval for onward transmission with respect to the infection time (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-4">Figure 4</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/serial-interval-pairs.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial intervals of possible case pairs in (a)
-COVID-19 and (b) MERS-CoV. Pairs represent a presumed infector and their
-presumed infectee plotted by date of symptom onset (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-5">Figure 5</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/seria-interval-fitted-distributions.jpg" alt="" class="figure mx-auto d-block"><figcaption>Fitted serial interval distribution for (a) COVID-19
-and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia.
-We fitted three commonly used distributions, Lognormal, Gamma, and
-Weibull distributions, respectively (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-6">Figure 6</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/serial-interval-covid-sars.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial interval of novel coronavirus (COVID-19)
-infections overlaid with a published distribution of SARS. (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-et al, 2020</a>)</figcaption></figure><hr>
-<h3 id="read-delays-figure-7">Figure 7</h3>
-<figure><img src="fig/read-delays-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="read-delays-figure-8">Figure 8</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/fig5a-normaldistribution.png" alt="" class="figure mx-auto d-block"><figcaption>The four probability functions for the normal
-distribution (<a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">Jack
-Weiss, 2012</a>)</figcaption></figure><hr>
-<h3 id="read-delays-figure-9">Figure 9</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/disease-reporting.jpg" alt="" class="figure mx-auto d-block"><figcaption>Timeline for chain of disease reporting, the
-Netherlands. Lab, laboratory; PHA, public health authority. From <a href="https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article" class="external-link">Marinović
-et al., 2015</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-10">Figure 10</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/rt-adjusting-delays.png" alt="" class="figure mx-auto d-block"><figcaption>Rt is a measure of transmission at time t.
-Observations after time t must be adjusted. ICU, intensive care unit.
-From <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">Gostic
-et al., 2020</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-11">Figure 11</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/incubation-period-serial-interval.jpg" alt="" class="figure mx-auto d-block"><figcaption>The relationship between the incubation period and
-serial interval. From <a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-2020</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-12">Figure 12</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="fig/reproduction-number-pre-symptomatic.png" alt="" class="figure mx-auto d-block"><figcaption>Parameter estimates. Plausible ranges for the key
-parameters R0 and θ (read the main text for sources) for four viral
-infections of public concern are shown as shaded regions. The size of
-the shaded area reflects the uncertainties in the parameter estimates.
-<a href="https://www.pnas.org/doi/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a></figcaption></figure><hr></section><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
+<section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
 <hr class="half-width">
 <h3 id="quantify-transmissibility-figure-1">Figure 1</h3>
 <figure><img src="fig/quantify-transmissibility-rendered-unnamed-chunk-4-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
@@ -426,9 +327,9 @@ <h3 id="create-forecast-figure-3">Figure 3</h3>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/images.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/images.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/index.html b/index.html
index fb56d7d8..402d94e8 100644
--- a/index.html
+++ b/index.html
@@ -166,7 +166,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -175,25 +175,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -241,11 +223,11 @@ <h2 class="accordion-header" id="flush-headingTwelve">
         <!-- START: inst/pkgdown/templates/content-syllabus.html -->
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     <nav class="lesson-content mx-md-4" aria-label="Next Chapter"><!-- content for small screens --><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link float-end" href="introduction.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="quantify-transmissibility.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link float-end" href="introduction.html" rel="next">Next: Outbreak analytics... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="quantify-transmissibility.html" rel="next">Next: Quantifying... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <hr></nav><main id="main-content" class="main-content"><h1 class="schedule-heading">Summary and Setup</h1>
       
@@ -413,11 +395,11 @@ <h3 id="download-the-data">Download the data<a class="anchor" aria-label="anchor
 any links that you are not going to use. 
  -->
 </section></section></main><nav class="bottom-pagination mx-md-4" aria-label="Next Chapter"><!-- content for small screens --><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link float-end" href="introduction.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="quantify-transmissibility.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link float-end" href="introduction.html" rel="next">Next: Outbreak analytics... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="quantify-transmissibility.html" rel="next">Next: Quantifying... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
     </nav></div>
 <!-- END  : inst/pkgdown/templates/content-syllabus.html -->
@@ -460,9 +442,9 @@ <h3 id="download-the-data">Download the data<a class="anchor" aria-label="anchor
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/index.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/index.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor-notes.html b/instructor-notes.html
index 07cb40da..184251df 100644
--- a/instructor-notes.html
+++ b/instructor-notes.html
@@ -187,7 +187,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -199,31 +199,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
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-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -341,9 +317,9 @@ <h1>Instructor Notes</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor-notes.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor-notes.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/404.html b/instructor/404.html
index f372fc29..35680e43 100644
--- a/instructor/404.html
+++ b/instructor/404.html
@@ -155,7 +155,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="https://epiverse-trace.github.io/tutorials/introduction.html" class="external-link">1. Outbreak analytics pipelines</a>
+        <a href="https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html" class="external-link">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -166,29 +166,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="https://epiverse-trace.github.io/tutorials/read-delays.html" class="external-link">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
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-</div>
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html" class="external-link">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
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-</div>
-
-<div class="accordion accordion-flush" id="accordionFlush5">
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-    <div class="accordion-header" id="flush-heading5">
-        <a href="https://epiverse-trace.github.io/tutorials/create-forecast.html" class="external-link">4. Create a short-term forecast</a>
+        <a href="https://epiverse-trace.github.io/tutorials/create-forecast.html" class="external-link">2. Create a short-term forecast</a>
     </div>
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@@ -301,9 +279,9 @@ <h1>Page not found</h1>
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   "url": "https://epiverse-trace.github.io/tutorials/instructor/404.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/404.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/CODE_OF_CONDUCT.html b/instructor/CODE_OF_CONDUCT.html
index 85be48c9..6addf77c 100644
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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@@ -162,25 +162,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
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-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
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-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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@@ -244,7 +226,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Contributor Code of Conduct</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/CODE_OF_CONDUCT.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -329,9 +311,9 @@ <h1>Contributor Code of Conduct</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/CODE_OF_CONDUCT.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/CODE_OF_CONDUCT.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index 41e27603..7d3d7b7c 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -153,7 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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@@ -162,25 +162,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
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-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
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-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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@@ -244,7 +226,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Licenses</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/LICENSE.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -377,9 +359,9 @@ <h1>Licenses</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/LICENSE.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/LICENSE.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
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diff --git a/instructor/aio.html b/instructor/aio.html
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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-        <a href="read-delays.html">2. Read delays</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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@@ -209,2724 +209,64 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
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-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
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-
-                </div>
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-
-            <hr class="half-width">
-<div class="accordion accordion-flush resources" id="accordionFlush12">
-              <div class="accordion-item">
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-                    RESOURCES
-                  </button>
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-                <div id="flush-collapseTwelve" class="accordion-collapse collapse" aria-labelledby="flush-headingTwelve" data-bs-parent="#accordionFlush12">
-                  <div class="accordion-body">
-                    <ul>
-<li>
-                        <a href="../instructor/key-points.html">Key Points</a>
-                      </li>
-                      <li>
-                        <a href="../instructor/instructor-notes.html">Instructor Notes</a>
-                      </li>
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-                        <a href="../instructor/images.html">Extract All Images</a>
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-<li><a class="dropdown-item" href="reference.html">Glossary of Terms: Epiverse-TRACE</a></li>
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-            <hr class="half-width resources">
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-  <div class="col-xl-8 col-lg-12 primary-content">
-    <main id="main-content" class="main-content"><div class="container lesson-content">
-        
-        
-<section id="aio-introduction"><p>Content from <a href="introduction.html">Outbreak analytics pipelines</a></p>
-<hr>
-<p>Last updated on 2024-02-08 |
-        
-        <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/introduction.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
-<p>Estimated time: <i aria-hidden="true" data-feather="clock"></i> 12 minutes</p>
-<div class="text-end">
-          <button role="button" aria-pressed="false" tabindex="0" id="expand-code" class="pull-right" data-expand="Expand All Solutions " data-collapse="Collapse All Solutions "> Expand All Solutions <i aria-hidden="true" data-feather="plus"></i></button>
-        </div>
-<div class="overview card">
-<h2 class="card-header">Overview</h2>
-<div class="row g-0">
-<div class="col-md-4">
-<div class="card-body">
-<div class="inner">
-<h3 class="card-title">Questions</h3>
-<ul>
-<li>Why use R packages for Outbreak analytics?</li>
-<li>What can we do to analyse our outbreak data?</li>
-<li>How can I start doing Outbreak Analytics with R?</li>
-</ul>
-</div>
-</div>
-</div>
-<div class="col-md-8">
-<div class="card-body">
-<div class="inner bordered">
-<h3 class="card-title">Objectives</h3>
-<ul>
-<li>Explain our vision on the need for outbreak analytics R
-packages.</li>
-<li>Share our strategy to create R packages into an outbreak analytics
-pipeline.</li>
-<li>Define our plan to start your learning path in outbreak analytics
-with R.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="prerequisites" class="callout prereq">
-<div class="callout-square">
-<i class="callout-icon" data-feather="check"></i>
-</div>
-<div id="prerequisites" class="callout-inner">
-<h3 class="callout-title">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
-</h3>
-<div class="callout-content">
-<p>This episode requires you to be familiar with:</p>
-<p><strong>Data science</strong> : Basic programming with R.</p>
-<p><strong>Epidemic theory</strong> : Reproduction number.</p>
-</div>
-</div>
-</div>
-<section id="why-to-use-r-packages-for-outbreak-analytics"><h2 class="section-heading">Why to use R packages for Outbreak analytics?<a class="anchor" aria-label="anchor" href="#why-to-use-r-packages-for-outbreak-analytics"></a>
-</h2>
-<hr class="half-width">
-<p>Outbreaks appear with different diseases and in different contexts,
-but what all of them have in common are the key public health questions
-(<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605" class="external-link">Cori
-et al. 2017</a>).</p>
-<p>Is the epidemic going to take off? Is it under control? How much
-effort will be needed to control it? We can answer them by
-<em>quantifying the transmissibility</em> of the disease. The most used
-parameter for this is the reproduction number (<span class="math inline">\(R\)</span>), the average number of secondary
-infections caused by a typical primary case in the population of
-interest (<a href="http://prism.edu.au/publications/prism-modeling-guideline/" class="external-link">Prism,
-2016</a>). We can intuitively interpret it as:</p>
-<ul>
-<li>if <span class="math inline">\(R&gt;1\)</span>, the epidemic is
-likely to grow,</li>
-<li>if <span class="math inline">\(R&lt;1\)</span>, the epidemic is
-likely to decline.</li>
-</ul>
-<p>We can estimate the reproduction number by initially using two
-<strong>data inputs</strong>: the incidence of reported cases and the <a href="reference.html#generationtime">generation time</a> distribution.
-But to calculate it, we must apply the appropriate mathematical models
-written in code with the required computational methods. That is not
-enough! Following <em>good practices</em>, the code we write should be
-peer-reviewed and contain internal tests to double-check that we are
-getting the estimates we expect. Imagine rewriting all of it during a
-health emergency!</p>
-<p>In R, the fundamental unit of shareable code is the <em>package</em>.
-A package bundles together code, data, documentation, and tests and is
-easy to share with others (<a href="https://r-pkgs.org/introduction.html" class="external-link">Wickham and Bryan,
-2023</a>). We, as epidemiologists, can contribute to their collaborative
-maintenance as a community to perform less error-prone data analysis
-pipelines.</p>
-<div id="questions-to-think-about" class="callout discussion">
-<div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
-</div>
-<div id="questions-to-think-about" class="callout-inner">
-<h3 class="callout-title">Questions to think about<a class="anchor" aria-label="anchor" href="#questions-to-think-about"></a>
-</h3>
-<div class="callout-content">
-<p>Remember your last experience with outbreak data and reflect on these
-questions:</p>
-<ul>
-<li>What data sources did you need to understand the outbreak?</li>
-<li>How did you get access to that data?</li>
-<li>Is that analysis pipeline you followed reusable for the next
-response?</li>
-</ul>
-<p>Reflect on your experiences.</p>
-</div>
-</div>
-</div>
-</section><section id="example-quantify-transmission"><h2 class="section-heading">Example: Quantify transmission<a class="anchor" aria-label="anchor" href="#example-quantify-transmission"></a>
-</h2>
-<hr class="half-width">
-<p>The <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a> package provides a three-step solution to
-<em>quantify the transmissibility</em>. Let’s see how to do this with a
-minimal example. First, load the package:</p>
-<div class="codewrapper sourceCode" id="cb1">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">library</span><span class="op">(</span><span class="va">EpiNow2</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="first-get-your-case-data">First, get your case data<a class="anchor" aria-label="anchor" href="#first-get-your-case-data"></a>
-</h3>
-<p>Case incidence data must be stored in a data frame with the observed
-number of cases per day. We can read an example from the package:</p>
-<div class="codewrapper sourceCode" id="cb2">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">example_confirmed</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>           date confirm
-         &lt;Date&gt;   &lt;num&gt;
-  1: 2020-02-22      14
-  2: 2020-02-23      62
-  3: 2020-02-24      53
-  4: 2020-02-25      97
-  5: 2020-02-26      93
- ---                   
-126: 2020-06-26     296
-127: 2020-06-27     255
-128: 2020-06-28     175
-129: 2020-06-29     174
-130: 2020-06-30     126</code></pre>
-</div>
-</div>
-<div class="section level3">
-<h3 id="then-set-the-generation-time">Then, set the generation time<a class="anchor" aria-label="anchor" href="#then-set-the-generation-time"></a>
-</h3>
-<p>Not all primary cases have the same probability of generating a
-secondary case. The onset and cessation of <a href="reference.html#infectiousness">infectiousness</a> may occur
-gradually. For <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, we can specify it as a
-probability <code>distribution</code> with <code>mean</code>, standard
-deviation <code>sd</code>, and maximum value <code>max</code>:</p>
-<div class="codewrapper sourceCode" id="cb4">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">generation_time</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="fl">3.6</span>,</span>
-<span>  sd <span class="op">=</span> <span class="fl">3.1</span>,</span>
-<span>  max <span class="op">=</span> <span class="fl">20</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-<div class="section level3">
-<h3 id="lets-calculate-the-reproduction-number">Let’s calculate the reproduction number!<a class="anchor" aria-label="anchor" href="#lets-calculate-the-reproduction-number"></a>
-</h3>
-<p>In the <code>epinow()</code> function we can add:</p>
-<ul>
-<li>the <code>reported_cases</code> data frame,</li>
-<li>the <code>generation_time</code> delay distribution, and</li>
-<li>the computation <code>stan</code> parameters for this
-calculation:</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb5">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:18:42] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-Running the chains for more iterations may help. See
-https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
-</div>
-<p>As an output, we get the time-varying (or <a href="reference.html#effectiverepro">effective</a>) reproduction number,
-as well as the cases by date of report and date of infection:</p>
-<div class="codewrapper sourceCode" id="cb7">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="../fig/introduction-rendered-unnamed-chunk-5-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div id="is-this-rt-estimation-biased" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="is-this-rt-estimation-biased" class="callout-inner">
-<h3 class="callout-title">Is this<span class="math inline">\(Rt\)</span>estimation biased?<a class="anchor" aria-label="anchor" href="#is-this-rt-estimation-biased"></a>
-</h3>
-<div class="callout-content">
-<p>Review <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">Gostic
-et al., 2020</a> about what additional adjustments this estimation
-requires to avoid false precision in <span class="math inline">\(Rt\)</span>.</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="the-problem"><h2 class="section-heading">The problem!<a class="anchor" aria-label="anchor" href="#the-problem"></a>
-</h2>
-<hr class="half-width">
-<p>However, <em>quantifying the transmissibility</em> during a real-life
-outbreak response is more challenging than this example!</p>
-<p>Usually, we receive outbreak data in non-standard formats, requiring
-specific steps and taking the most time to prepare usable data inputs.
-Some of them are:</p>
-<ul>
-<li>Read delay distributions from the literature</li>
-<li>Read and clean case data</li>
-<li>Validate your line list</li>
-<li>Describe case data</li>
-</ul>
-<p>And this is not the end. After <em>quantifying transmissibility</em>
-we need to answer more key public health questions like: What is the
-attack rate we expect? What would be the impact of a given intervention?
-We can use the reproduction number and other outputs as new inputs for
-complementary tasks. For example:</p>
-<ul>
-<li>Estimate severity</li>
-<li>Create short-term forecast</li>
-<li>Simulate transmission scenarios</li>
-<li>Compare interventions</li>
-</ul>
-<p>So, all these tasks can be interconnected in a pipeline:</p>
-<figure><img src="https://epiverse-trace.github.io/task_pipeline-minimal.svg" alt="" class="figure mx-auto d-block"><figcaption>The outbreak analytics pipeline.</figcaption></figure></section><section id="what-can-we-do"><h2 class="section-heading">What can we do?<a class="anchor" aria-label="anchor" href="#what-can-we-do"></a>
-</h2>
-<hr class="half-width">
-<p>Our strategy is gradually incorporating specialised R packages into
-our traditional analysis pipeline. These packages should fill the gaps
-in these epidemiology-specific tasks in response to outbreaks.</p>
-<p>Epiverse-TRACE’s aim is to provide a software ecosystem for outbreak
-analytics. We support the development of software pieces, make the
-existing ones interoperable for the user experience, and stimulate a
-community of practice.</p>
-<figure><img src="../fig/pkgs-hexlogos.png" class="figure mx-auto d-block"></figure></section><section id="how-can-i-start"><h2 class="section-heading">How can I start?<a class="anchor" aria-label="anchor" href="#how-can-i-start"></a>
-</h2>
-<hr class="half-width">
-<p>Our plan for these tutorials is to introduce key solutions from
-packages in all the tasks before and after the <em>Quantify
-transmission</em> task, plus the required theory concepts to interpret
-modelling outputs and make rigorous conclusions.</p>
-<ul>
-<li><p>In the first set of episodes, you will learn how to optimise the
-reading of delay distributions and cleaning of case data to input them
-into the <em>Quantify transmission</em> task. These preliminary tasks
-are the <strong>Early tasks</strong>. These include packages like
-<code>{readepi}</code>, <code>{cleanepi}</code>,
-<a href="https://epiverse-trace.github.io/linelist/" class="external-link">linelist</a>, <code>{epiparameter}</code>, and
-<code>{episoap}</code>.</p></li>
-<li><p>Then, we will get deeper into the packages and required theory to
-<em>Quantify transmission</em> and perform more real-time analysis tasks
-next to it. These are the <strong>Middle tasks</strong>. This includes
-<a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, <a href="https://github.com/epiverse-trace/cfr" class="external-link">cfr</a>, <code>{epichains}</code>,
-and <code>{superspreading}</code>.</p></li>
-<li><p>Lastly, we will use <em>Quantify transmission</em> data outputs
-to compare it to other indicators and simulate epidemic scenarios as
-part of the <strong>Late tasks</strong>. This includes
-<a href="https://github.com/epiverse-trace/finalsize" class="external-link">finalsize</a>, <code>{epidemics}</code>, and
-<code>{scenarios}</code>.</p></li>
-</ul>
-<div id="keypoints1" class="callout keypoints">
-<div class="callout-square">
-<i class="callout-icon" data-feather="key"></i>
-</div>
-<div class="callout-inner">
-<h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="#keypoints1"></a>
-</h3>
-<div class="callout-content">
-<ul>
-<li>Our vision is to have pipelines of R packages for outbreak
-analytics.</li>
-<li>Our strategy is to create interconnected tasks to get relevant
-outputs for public health questions.</li>
-<li>We plan to introduce package solutions and theory bits for each of
-the tasks in the outbreak analytics pipeline.</li>
-</ul>
-</div>
-</div>
-</div>
-<!-- 
-Place links that you need to refer to multiple times across pages here. Delete
-any links that you are not going to use. 
- -->
-</section></section><section id="aio-read-delays"><p>Content from <a href="read-delays.html">Read delays</a></p>
-<hr>
-<p>Last updated on 2024-02-08 |
-        
-        <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/read-delays.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
-<p>Estimated time: <i aria-hidden="true" data-feather="clock"></i> 12 minutes</p>
-<div class="text-end">
-          <button role="button" aria-pressed="false" tabindex="0" id="expand-code" class="pull-right" data-expand="Expand All Solutions " data-collapse="Collapse All Solutions "> Expand All Solutions <i aria-hidden="true" data-feather="plus"></i></button>
-        </div>
-<div class="overview card">
-<h2 class="card-header">Overview</h2>
-<div class="row g-0">
-<div class="col-md-4">
-<div class="card-body">
-<div class="inner">
-<h3 class="card-title">Questions</h3>
-<ul>
-<li>How to get delay distributions from a systematic review?</li>
-<li>How to connect reused delays with my existing analysis
-pipeline?</li>
-<li>When should delays be reused from a systematic review?</li>
-</ul>
-</div>
-</div>
-</div>
-<div class="col-md-8">
-<div class="card-body">
-<div class="inner bordered">
-<h3 class="card-title">Objectives</h3>
-<ul>
-<li>Get delays from a systematic review with
-<code>{epiparameter}</code>.</li>
-<li>Get statistical summaries and distribution parameters of delay
-distributions.</li>
-<li>Use distribution functions from delay distributions.</li>
-<li>Convert a continuous to a discrete delay distribution.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="prerequisites" class="callout prereq">
-<div class="callout-square">
-<i class="callout-icon" data-feather="check"></i>
-</div>
-<div id="prerequisites" class="callout-inner">
-<h3 class="callout-title">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
-</h3>
-<div class="callout-content">
-<p>This episode requires you to be familiar with:</p>
-<p><strong>Data science</strong> : Basic programming with R.</p>
-<p><strong>Epidemic theory</strong> : Epidemiological parameters. Time
-periods.</p>
-</div>
-</div>
-</div>
-<section id="introduction"><h2 class="section-heading">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<hr class="half-width">
-<p>The <a href="reference.html#naturalhistory">natural history</a> of an
-infectious disease shows that its development has a regularity from
-stage to stage. The time periods from an infectious disease inform about
-the timing of transmission and interventions.</p>
-<figure><img src="../fig/time-periods.jpg" alt="" class="figure mx-auto d-block"><figcaption>Definition of key time periods. From <a href="https://www.sciencedirect.com/science/article/pii/S2468042721000038" class="external-link">Xiang
-et al, 2021</a></figcaption></figure><div id="definitions" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="definitions" class="callout-inner">
-<h3 class="callout-title">Definitions<a class="anchor" aria-label="anchor" href="#definitions"></a>
-</h3>
-<div class="callout-content">
-<p>Look at the <a href="reference.html">glossary</a> for the definitions
-of all the time periods of the figure above!</p>
-</div>
-</div>
-</div>
-<p>However, early in an epidemic, modelling efforts can be delayed by
-the lack of a centralized resource that summarises input parameters for
-the disease of interest (<a href="https://mrc-ide.github.io/epireview/" class="external-link">Nash et al., 2023</a>).
-Projects like <code>{epiparameter}</code> and <code>{epireview}</code>
-are building online catalogues following systematic review protocols
-that can help build models faster for coming outbreaks and epidemics
-from known pathogens and unknown ones related to known families of
-viruses.</p>
-<!-- Early models for COVID-19 used parameters from other coronaviruses. https://www.thelancet.com/article/S1473-3099(20)30144-4/fulltext -->
-<p>To exemplify how to use <code>{epiparameter}</code> in your analysis
-pipeline, our goal in this episode will be to replace the
-<code>generation_time</code> input that we can use for
-<code>EpiNow2::epinow()</code>.</p>
-<div class="codewrapper sourceCode" id="cb1">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>To do this replacement, instead of plug-in numeric values to
-<code>EpiNow2::dist_spec()</code> to manually specify the delay
-distribution parameters, we are going to collect them from the library
-of epidemiological parameters provided by
-<code>{epiparameter}</code>:</p>
-<div class="codewrapper sourceCode" id="cb2">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">generation_time</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="fl">3.6</span>,</span>
-<span>  sd <span class="op">=</span> <span class="fl">3.1</span>,</span>
-<span>  max <span class="op">=</span> <span class="fl">20</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>Let’s explore how we can access this and other time delays using
-<code>{epiparameter}</code>. We’ll use the pipe <code>%&gt;%</code> to
-connect some of their functions, so let’s also call to the
-<a href="https://tidyverse.tidyverse.org" class="external-link">tidyverse</a> package:</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">library</span><span class="op">(</span><span class="va">epiparameter</span><span class="op">)</span></span>
-<span><span class="kw">library</span><span class="op">(</span><span class="va">EpiNow2</span><span class="op">)</span></span>
-<span><span class="kw">library</span><span class="op">(</span><span class="va">tidyverse</span><span class="op">)</span></span></code></pre>
-</div>
-</section><section id="find-a-generation-time"><h2 class="section-heading">Find a Generation time<a class="anchor" aria-label="anchor" href="#find-a-generation-time"></a>
-</h2>
-<hr class="half-width">
-<p>The generation time, jointly with the <span class="math inline">\(R\)</span>, can inform about the speed of spread
-and its feasibility of control. Given a <span class="math inline">\(R&gt;1\)</span>, with a shorter generation time,
-cases can appear more quickly.</p>
-<figure><img src="../fig/reproduction-generation-time.png" alt="" class="figure mx-auto d-block"><figcaption>Video from the MRC Centre for Global Infectious
-Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group
-with Dr Anne Cori (27-07-2023) at <a href="https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL" class="external-link uri">https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL</a></figcaption></figure><p>In calculating the effective reproduction number (<span class="math inline">\(R_{t}\)</span>), the <em>generation time</em>
-distribution is often approximated by the <a href="reference.html#serialinterval">serial interval</a> distribution.
-This frequent approximation is because it is easier to observe and
-measure the onset of symptoms than the onset of infectiousness.</p>
-<figure><img src="../fig/serial-interval-observed.jpeg" alt="" class="figure mx-auto d-block"><figcaption>A schematic of the relationship of different time
-periods of transmission between an infector and an infectee in a
-transmission pair. Exposure window is defined as the time interval
-having viral exposure, and transmission window is defined as the time
-interval for onward transmission with respect to the infection time (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>).</figcaption></figure><p>However, using the <em>serial interval</em> as an approximation of
-the <em>generation time</em> is primarily valid for diseases in which
-infectiousness starts after symptom onset (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>). In cases where infectiousness starts before
-symptom onset, the serial intervals can have negative values, which is
-the case of a pre-symptomatic transmission (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2" class="external-link">Nishiura
-et al. (2020)</a>).</p>
-<p>Additionally, even if the <em>generation time</em> and <em>serial
-interval</em> have the same mean, their variance usually differs,
-propagating bias to the <span class="math inline">\(R_{t}\)</span>
-estimation. <span class="math inline">\(R_{t}\)</span> estimates are
-sensitive not only to the mean generation time but also to the variance
-and form of the generation interval distribution <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">(Gostic
-et al., 2020)</a>.</p>
-<div id="from-time-periods-to-probability-distributions." class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="from-time-periods-to-probability-distributions." class="callout-inner">
-<h3 class="callout-title">From time periods to probability
-distributions.<a class="anchor" aria-label="anchor" href="#from-time-periods-to-probability-distributions."></a>
-</h3>
-<div class="callout-content">
-<p>When we calculate the <em>serial interval</em>, we see that not all
-case pairs have the same time length. We will observe this variability
-for any case pair and individual time period, including the <a href="reference.html#incubation">incubation period</a> and <a href="reference.html#infectiousness">infectious period</a>.</p>
-<figure><img src="../fig/serial-interval-pairs.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial intervals of possible case pairs in (a)
-COVID-19 and (b) MERS-CoV. Pairs represent a presumed infector and their
-presumed infectee plotted by date of symptom onset (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><p>To summarize these data from individual and pair time periods, we can
-find the <strong>statistical distributions</strong> that best fit the
-data (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200806" class="external-link">McFarland
-et al., 2023</a>).</p>
-<!-- add a reference about good practices to estimate distributions -->
-<figure><img src="../fig/seria-interval-fitted-distributions.jpg" alt="" class="figure mx-auto d-block"><figcaption>Fitted serial interval distribution for (a) COVID-19
-and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia.
-We fitted three commonly used distributions, Lognormal, Gamma, and
-Weibull distributions, respectively (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><p>Statistical distributions are summarized in terms of their
-<strong>summary statistics</strong> like the <em>location</em> (mean and
-percentiles) and <em>spread</em> (variance or standard deviation) of the
-distribution, or with their <strong>distribution parameters</strong>
-that inform about the <em>form</em> (shape and rate/scale) of the
-distribution. These estimated values can be reported with their
-<strong>uncertainty</strong> (95% confidence intervals).</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">Gamma</th>
-<th align="left">mean</th>
-<th align="left">shape</th>
-<th align="left">rate/scale</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.13(13.9–14.7)</td>
-<td align="left">6.31(4.88–8.52)</td>
-<td align="left">0.43(0.33–0.60)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.1(5.0–5.5)</td>
-<td align="left">2.77(2.09–3.88)</td>
-<td align="left">0.53(0.38–0.76)</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<thead><tr class="header">
-<th align="left">Weibull</th>
-<th align="left">mean</th>
-<th align="left">shape</th>
-<th align="left">rate/scale</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.2(13.3–15.2)</td>
-<td align="left">3.07(2.64–3.63)</td>
-<td align="left">16.1(15.0–17.1)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.2(4.6–5.9)</td>
-<td align="left">1.74(1.46–2.11)</td>
-<td align="left">5.83(5.08–6.67)</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Serial interval estimates using Gamma, Weibull, and Log normal
-distributions. 95% confidence intervals for the shape and scale (logmean
-and sd for Log normal) parameters are shown in brackets (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3" class="external-link">Althobaity
-et al., 2022</a>).</caption>
-<thead><tr class="header">
-<th align="left">Log normal</th>
-<th align="left">mean</th>
-<th align="left">mean-log</th>
-<th align="left">sd-log</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">MERS-CoV</td>
-<td align="left">14.08(13.1–15.2)</td>
-<td align="left">2.58(2.50–2.68)</td>
-<td align="left">0.44(0.39–0.5)</td>
-</tr>
-<tr class="even">
-<td align="left">COVID-19</td>
-<td align="left">5.2(4.2–6.5)</td>
-<td align="left">1.45(1.31–1.61)</td>
-<td align="left">0.63(0.54–0.74)</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-<div id="serial-interval" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="serial-interval" class="callout-inner">
-<h3 class="callout-title">Serial interval<a class="anchor" aria-label="anchor" href="#serial-interval"></a>
-</h3>
-<div class="callout-content">
-<p>Assume that COVID-19 and SARS have similar reproduction number values
-and that the serial interval approximates the generation time.</p>
-<p>Given the Serial interval of both infections in the figure below:</p>
-<ul>
-<li>Which one would be harder to control?</li>
-<li>Why do you conclude that?</li>
-</ul>
-<figure><img src="../fig/serial-interval-covid-sars.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial interval of novel coronavirus (COVID-19)
-infections overlaid with a published distribution of SARS. (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-et al, 2020</a>)</figcaption></figure>
-</div>
-</div>
-</div>
-<div id="accordionHint1" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
-  <h4 class="accordion-header" id="headingHint1">Give me a hint</h4>
-</button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
-<div class="accordion-body">
-<p>The peak of each curve can inform you about the location of the mean
-of each distribution. The larger the mean, the larger the serial
-interval.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution1" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
-  <h4 class="accordion-header" id="headingSolution1">Show me the solution</h4>
-</button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
-<div class="accordion-body">
-<p>Which one would be harder to control?</p>
-<ul>
-<li>COVID-19</li>
-</ul>
-<p>Why do you conclude that?</p>
-<ul>
-<li>COVID-19 has the lowest mean serial interval. The approximate mean
-value for the serial interval of COVID-19 is around four days, and SARS
-is about seven days. Thus, COVID-19 will likely have newer generations
-in less time than SARS, assuming similar reproduction numbers.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionInstructor1" class="accordion instructor-note accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button instructor-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseInstructor1" aria-expanded="false" aria-controls="collapseInstructor1">
-  <h3 class="accordion-header" id="headingInstructor1">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="edit-2"></i></div>Instructor Note</h3>
-</button>
-<div id="collapseInstructor1" class="accordion-collapse collapse" data-bs-parent="#accordionInstructor1" aria-labelledby="headingInstructor1">
-<div class="accordion-body">
-<p>The objective of the assessment above is to assess the interpretation
-of a larger or shorter generation time.</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="extract-epidemiological-parameters"><h2 class="section-heading">Extract epidemiological parameters<a class="anchor" aria-label="anchor" href="#extract-epidemiological-parameters"></a>
-</h2>
-<hr class="half-width">
-<p>First, let’s assume that the data set <code>example_confirmed</code>
-has COVID-19 observed cases. So, we need to find a reported generation
-time for COVID-19 or any other useful parameter for this aim.</p>
-<p>Let’s start by looking at how many parameters we have in the
-epidemiological distributions database (<code>epidist_db</code>) for the
-<code>disease</code> named <code>covid</code>-19:</p>
-<div class="codewrapper sourceCode" id="cb4">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 27 results that match the criteria (22 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>List of &lt;epidist&gt; objects
-  Number of entries in library: 27
-  Number of studies in library: 10
-  Number of diseases: 1
-  Number of delay distributions: 27
-  Number of offspring distributions: 0</code></pre>
-</div>
-<p>From the <code>{epiparameter}</code> package, we can use the
-<code>epidist_db()</code> function to ask for any <code>disease</code>
-and also for a specific epidemiological distribution
-(<code>epi_dist</code>).</p>
-<p>Let’s ask now how many parameters we have in the epidemiological
-distributions database (<code>epidist_db</code>) with the generation
-time using the string <code>generation</code>:</p>
-<div class="codewrapper sourceCode" id="cb7">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"generation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 1 results that match the criteria (1 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: Influenza
-Pathogen: Influenza-A-H1N1
-Epi Distribution: generation time
-Study: Lessler J, Reich N, Cummings D, New York City Department of Health and
-Mental Hygiene Swine Influenza Investigation Team (2009). "Outbreak of
-2009 Pandemic Influenza A (H1N1) at a New York City School." _The New
-England Journal of Medicine_. doi:10.1056/NEJMoa0906089
-&lt;https://doi.org/10.1056/NEJMoa0906089&gt;.
-Distribution: weibull
-Parameters:
-  shape: 2.360
-  scale: 3.180</code></pre>
-</div>
-<p>Currently, in the library of epidemiological parameters, we have one
-<code>generation</code> time entry for Influenza. Considering the
-abovementioned considerations, we can look at the <code>serial</code>
-intervals for <code>COVID</code>-19.</p>
-<div class="codewrapper sourceCode" id="cb10">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"COVID"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[[1]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021).
-"Serial interval and incubation period of COVID-19: a systematic review
-and meta-analysis." _BMC Infectious Diseases_.
-doi:10.1186/s12879-021-05950-x
-&lt;https://doi.org/10.1186/s12879-021-05950-x&gt;.
-Parameters: &lt;no parameters&gt;
-
-[[2]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568
-
-[[3]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: weibull
-Parameters:
-  shape: 2.203
-  scale: 5.420
-
-[[4]]
-Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). "Estimation of
-incubation period and serial interval of COVID-19: analysis of 178
-cases and 131 transmission chains in Hubei province, China."
-_Epidemiology and Infection_. doi:10.1017/S0950268820001338
-&lt;https://doi.org/10.1017/S0950268820001338&gt;.
-Distribution: norm
-Parameters:
-  mean: 4.600
-  sd: 4.400
-
-attr(,"class")
-[1] "multi_epidist"</code></pre>
-</div>
-<div id="case-insensitive" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="case-insensitive" class="callout-inner">
-<h3 class="callout-title">CASE-INSENSITIVE<a class="anchor" aria-label="anchor" href="#case-insensitive"></a>
-</h3>
-<div class="callout-content">
-<p><code>epidist_db</code> is <a href="https://dillionmegida.com/p/case-sensitivity-vs-case-insensitivity/#case-insensitivity" class="external-link">case-insensitive</a>.
-This means that you can use strings with letters in upper or lower case
-indistinctly.</p>
-</div>
-</div>
-</div>
-<p>We get more than one epidemiological delay. To summarize this view
-and get the column names from the underlying parameter dataset, we can
-add the <code>epiparameter::list_distributions()</code> function to the
-previous code using the pipe <code>%&gt;%</code>:</p>
-<div class="codewrapper sourceCode" id="cb13">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<div id="ebolas-incubation-periods" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-incubation-periods" class="callout-inner">
-<h3 class="callout-title">Ebola’s incubation periods<a class="anchor" aria-label="anchor" href="#ebolas-incubation-periods"></a>
-</h3>
-<div class="callout-content">
-<p>Take 5 minutes:</p>
-<ul>
-<li><p>How many delay distributions are for the Ebola disease?</p></li>
-<li><p>How many delay distributions are for the incubation period of
-Ebola?</p></li>
-<li><p>Explore the library and find the disease with the delay
-distribution of your interest! Do you recognize the paper?</p></li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint2" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
-  <h4 class="accordion-header" id="headingHint2">Give me a hint</h4>
-</button>
-<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
-<div class="accordion-body">
-<p>The <code>{epiparameter}</code> combo of <code>epidist_db()</code>
-plus <code>list_distributions()</code> list all the entries by:</p>
-<ul>
-<li>disease,</li>
-<li>epidemiological distribution,</li>
-<li>the type of the probability distribution,</li>
-<li>author of the study, and</li>
-<li>year of study.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution2" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
-  <h4 class="accordion-header" id="headingSolution2">Show me the solution</h4>
-</button>
-<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb16">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># 16 delays distributions</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># 5 delay distributions are for the incubation period</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>Now, from the output of <code>epiparameter::epidist_db()</code>, What
-is an <a href="reference.html#offspringdist">offspring
-distribution</a>?</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="select-a-single-distribution"><h2 class="section-heading">Select a single distribution<a class="anchor" aria-label="anchor" href="#select-a-single-distribution"></a>
-</h2>
-<hr class="half-width">
-<p>The <code>epiparameter::epidist_db()</code> function works as a
-filtering or subset function. Let’s use the <code>author</code> argument
-to filter <code>Hiroshi Nishiura</code> parameters:</p>
-<div class="codewrapper sourceCode" id="cb17">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Hiroshi"</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 2 results that match the criteria (2 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval             lnorm Hiroshi .... 2020
-2 COVID-19  serial interval           weibull Hiroshi .... 2020</code></pre>
-</div>
-<p>We still get more than one epidemiological parameter. We can set the
-<code>single_epidist</code> argument to <code>TRUE</code> to only
-one:</p>
-<div class="codewrapper sourceCode" id="cb20">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Hiroshi"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Using Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.. 
-To retrieve the short citation use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<div id="how-does-single_epidist-works" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="how-does-single_epidist-works" class="callout-inner">
-<h3 class="callout-title">How does<code>single_epidist</code>works?<a class="anchor" aria-label="anchor" href="#how-does-single_epidist-works"></a>
-</h3>
-<div class="callout-content">
-<p>Looking at the help documentation for
-<code>?epiparameter::epidist_db()</code>:</p>
-<ul>
-<li>If multiple entries match the arguments supplied and
-<code>single_epidist = TRUE</code>,</li>
-<li>Then, the parameterised <code>⁠&lt;epidist&gt;</code>⁠ with the
-largest sample size will be returned.</li>
-<li>If multiple entries are equal after this sorting, the first entry
-will be returned.</li>
-</ul>
-<p>What does a parametrised <code>&lt;epidist&gt;</code> is? Look at
-<code>?is_parameterised</code>.</p>
-</div>
-</div>
-</div>
-<p>Now, we have an epidemiological parameter we can reuse! We can
-replace the numbers we plug into <code>EpiNow2::dist_spec()</code>.</p>
-<p>Let’s assign this <code>&lt;epidist&gt;</code> class object to the
-<code>covid_serialint</code> object.</p>
-<div class="codewrapper sourceCode" id="cb23">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>    author <span class="op">=</span> <span class="st">"Nishiura"</span>,</span>
-<span>    single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Using Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.. 
-To retrieve the short citation use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb25">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<div id="ebolas-incubation-period" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-incubation-period" class="callout-inner">
-<h3 class="callout-title">Ebola’s incubation period<a class="anchor" aria-label="anchor" href="#ebolas-incubation-period"></a>
-</h3>
-<div class="callout-content">
-<p>Take 2 minutes:</p>
-<ul>
-<li>What type of distribution has the incubation period of Ebola with
-the highest sample size?</li>
-<li>How would you access to the sample size of the other studies in the
-<code>&lt;multi_epidist&gt;</code> class object?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint3" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint3" aria-expanded="false" aria-controls="collapseHint3">
-  <h4 class="accordion-header" id="headingHint3">Give me a hint</h4>
-</button>
-<div id="collapseHint3" class="accordion-collapse collapse" data-bs-parent="#accordionHint3" aria-labelledby="headingHint3">
-<div class="accordion-body">
-<p>The <code>{epiparameter}</code> combo of <code>epidist_db()</code>
-plus <code>list_distributions()</code> list all the entries by:</p>
-<ul>
-<li>disease,</li>
-<li>epidemiological distribution,</li>
-<li>the type of the probability distribution,</li>
-<li>author of the study, and</li>
-<li>year of study.</li>
-</ul>
-<p>This is a <code>&lt;multi_epidist&gt;</code> class object:</p>
-<div class="codewrapper sourceCode" id="cb27">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution3" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
-  <h4 class="accordion-header" id="headingSolution3">Show me the solution</h4>
-</button>
-<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb28">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the distribution with the highest sample size has a gamma distribution</span></span>
-<span><span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-<p>To access the <code>sample_size</code>, review an <a href="https://github.com/epiverse-trace/epiparameter/issues/227" class="external-link">issue
-reported in the GitHub repository</a> of the <code>{epiparameter}</code>
-package.</p>
-</div>
-</div>
-</div>
-</div>
-</section><section id="extract-the-summary-statistics"><h2 class="section-heading">Extract the summary statistics<a class="anchor" aria-label="anchor" href="#extract-the-summary-statistics"></a>
-</h2>
-<hr class="half-width">
-<p>We can get the <code>mean</code> and standard deviation
-(<code>sd</code>) from this <code>&lt;epidist&gt;</code> diving into the
-<code>summary_stats</code> object:</p>
-<div class="codewrapper sourceCode" id="cb29">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># get the mean</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 4.7</code></pre>
-</div>
-<div id="how-to-get-the-sd-and-other-nested-elements" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="how-to-get-the-sd-and-other-nested-elements" class="callout-inner">
-<h3 class="callout-title">How to get the<code>sd</code>and other
-nested elements?<a class="anchor" aria-label="anchor" href="#how-to-get-the-sd-and-other-nested-elements"></a>
-</h3>
-<div class="callout-content">
-<p>Take 1 minute to:</p>
-<ol style="list-style-type: decimal">
-<li><p>Get the <code>sd</code> of the epidemiological
-distribution.</p></li>
-<li><p>Find the <code>sample_size</code> used in the study.</p></li>
-<li><p>Explore all the other nested elements within the
-<code>&lt;epidist&gt;</code> object.</p></li>
-</ol>
-<p>Share about:</p>
-<ul>
-<li>What elements do you find useful for your analysis?</li>
-<li>What other elements would you like to see in this object? How?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint4" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint4" aria-expanded="false" aria-controls="collapseHint4">
-  <h4 class="accordion-header" id="headingHint4">Give me a hint</h4>
-</button>
-<div id="collapseHint4" class="accordion-collapse collapse" data-bs-parent="#accordionHint4" aria-labelledby="headingHint4">
-<div class="accordion-body">
-<p>Use the <code>$</code> operator plus the <kbd>tab</kbd> keyboard
-button to explore them as an expandable list:</p>
-<div class="codewrapper sourceCode" id="cb31">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>covid_serialint<span class="sc">$</span></span></code></pre>
-</div>
-<p>Use the <code>str()</code> to display the structure of the
-<code>&lt;epidist&gt;</code> R object.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
-  <h4 class="accordion-header" id="headingSolution4">Show me the solution</h4>
-</button>
-<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb32">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># get the sd</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span></span>
-<span></span>
-<span><span class="co"># get the sample_size</span></span>
-<span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">sample_size</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="intructor">
-<p>An interesting element is the <code>method_assess</code> nested
-entry, which refers to the methods used by the study authors to assess
-for bias while estimating the serial interval distribution.</p>
-<div class="codewrapper sourceCode" id="cb33">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">method_assess</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>$censored
-[1] TRUE
-
-$right_truncated
-[1] TRUE
-
-$phase_bias_adjusted
-[1] FALSE</code></pre>
-</div>
-<p>We will explore these concepts at the end!</p>
-</div>
-<div id="ebolas-severity-parameter" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-severity-parameter" class="callout-inner">
-<h3 class="callout-title">Ebola’s severity parameter<a class="anchor" aria-label="anchor" href="#ebolas-severity-parameter"></a>
-</h3>
-<div class="callout-content">
-<p>A severity parameter like the duration of hospitalization could add
-to the information needed about the bed capacity in response to an
-outbreak (<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371" class="external-link">Cori
-et al., 2017</a>).</p>
-<p>For Ebola:</p>
-<ul>
-<li>what is a reported point estimate and uncertainty of the mean
-duration of health-care and case isolation?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint5" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint5" aria-expanded="false" aria-controls="collapseHint5">
-  <h4 class="accordion-header" id="headingHint5">Give me a hint</h4>
-</button>
-<div id="collapseHint5" class="accordion-collapse collapse" data-bs-parent="#accordionHint5" aria-labelledby="headingHint5">
-<div class="accordion-body">
-<p>An informative delay measures the time from symptom onset to recovery
-or death.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
-  <h4 class="accordion-header" id="headingSolution5">Show me the solution</h4>
-</button>
-<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb35">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># one way to get the list of all the available parameters</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"all"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">as_tibble</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">distinct</span><span class="op">(</span><span class="va">epi_distribution</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_severity</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"onset to discharge"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># point estimate</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span></span>
-<span><span class="co"># 95% confidence intervals</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean_ci</span></span>
-<span><span class="co"># limits of the confidence intervals</span></span>
-<span><span class="va">ebola_severity</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean_ci_limits</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="continuous-distributions"><h2 class="section-heading">Continuous distributions<a class="anchor" aria-label="anchor" href="#continuous-distributions"></a>
-</h2>
-<hr class="half-width">
-<p>The following output has four entries with different content in the
-<strong>probability distribution</strong>
-(<code>prob_distribution</code>) column:</p>
-<div class="codewrapper sourceCode" id="cb36">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"serial"</span></span>
-<span>  <span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Returning 4 results that match the criteria (3 are parameterised). 
-Use subset to filter by entry variables or single_epidist to return a single entry. 
-To retrieve the short citation for each use the 'get_citation' function</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb38">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<p>Entries with a missing value (<code>&lt;NA&gt;</code>) in the
-<code>prob_distribution</code> column are <em>non-parameterised</em>
-entries. They have summary statistics but no probability distribution.
-Compare these two outputs:</p>
-<div class="codewrapper sourceCode" id="cb40">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">summary_stats</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">prob_dist</span></span></code></pre>
-</div>
-<div id="accordionSpoiler1" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
-  <h3 class="accordion-header" id="headingSpoiler1">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>Parameterised entries have an Inference method</h3>
-</button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
-<div class="accordion-body">
-<p>As detailed in <code>?is_parameterised</code>, a parameterised
-distribution is the entry that has a probability distribution associated
-with it provided by an <code>inference_method</code> as shown in
-<code>metadata</code>:</p>
-<div class="codewrapper sourceCode" id="cb41">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span>
-<span><span class="va">distribution</span><span class="op">[[</span><span class="fl">4</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">inference_method</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<p>In the <code>epiparameter::list_distributions()</code> output, we can
-also find different types of probability distributions (e.g.,
-Log-normal, Weibull, Normal).</p>
-<div class="codewrapper sourceCode" id="cb42">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">distribution</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>   disease epi_distribution prob_distribution       author year
-1 COVID-19  serial interval              &lt;NA&gt; Muluneh .... 2021
-2 COVID-19  serial interval             lnorm Hiroshi .... 2020
-3 COVID-19  serial interval           weibull Hiroshi .... 2020
-4 COVID-19  serial interval              norm Lin Yang.... 2020</code></pre>
-</div>
-<p>In <code>{epiparameter}</code>, you will mostly find
-<strong>continuous</strong> distributions like these. You can visualize
-any of them with the <code>plot()</code> function and access to:</p>
-<ul>
-<li>the <em>Probability Density Function (PDF)</em> and</li>
-<li>the <em>Cumulative Distribution Function (CDF)</em>.</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb44">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="../fig/read-delays-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>With the <code>day_range</code> argument, you can change the length
-or number of days in the <code>x</code> axis. Explore what it look
-like:</p>
-<div class="codewrapper sourceCode" id="cb45">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">distribution</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="the-distribution-zoo" class="callout discussion">
-<div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
-</div>
-<div id="the-distribution-zoo" class="callout-inner">
-<h3 class="callout-title">The distribution Zoo<a class="anchor" aria-label="anchor" href="#the-distribution-zoo"></a>
-</h3>
-<div class="callout-content">
-<p>Explore this shinyapp called <strong>The Distribution
-Zoo</strong>!</p>
-<p>Follow these steps to reproduce the form of the
-<code>covid_serialint</code> distribution:</p>
-<ol style="list-style-type: decimal">
-<li>Access to <a href="https://ben18785.shinyapps.io/distribution-zoo/" class="external-link uri">https://ben18785.shinyapps.io/distribution-zoo/</a> shinyapp
-website,</li>
-<li>Go to the left panel,</li>
-<li>Keep the <em>Category of distribution</em>:
-<code>Continuous Univariate</code>,</li>
-<li>Select a new <em>Type of distribution</em>:
-<code>Log-Normal</code>,</li>
-<li>Move the <strong>sliders</strong>, i.e. the graphical control
-element that allows you to adjust a value by moving a handle along a
-horizontal track or bar to the <code>covid_serialint</code>
-parameters.</li>
-</ol>
-<p>Replicate these with the <code>distribution</code> object and all its
-list elements: <code>2</code>, <code>3</code>, and <code>4</code>.
-Explore how the shape of a distribution changes when its parameters
-change.</p>
-<p>Share about:</p>
-<ul>
-<li>What other features of the website do you find helpful?</li>
-</ul>
-</div>
-</div>
-</div>
-<!-- A slider, in the context of user interfaces and graphical user interfaces (GUIs), is a graphical control element that allows users to adjust a value by moving a handle along a track or bar. Conceptually, it provides a way to select a numeric value within a specified range by visually sliding or dragging a pointer (the handle) along a continuous axis. -->
-</section><section id="distribution-functions"><h2 class="section-heading">Distribution functions<a class="anchor" aria-label="anchor" href="#distribution-functions"></a>
-</h2>
-<hr class="half-width">
-<p>In R, all the statistical distributions have functions to access
-the:</p>
-<ul>
-<li>Probability <strong>Density</strong> function (PDF),</li>
-<li>Cumulative Distribution function (<strong>CDF</strong>),</li>
-<li>
-<strong>Quantile</strong> function, and</li>
-<li>
-<strong>Random</strong> values from the given distribution.</li>
-</ul>
-<div id="accordionSpoiler2" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler2" aria-expanded="false" aria-controls="collapseSpoiler2">
-  <h3 class="accordion-header" id="headingSpoiler2">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>Functions for the Normal distribution</h3>
-</button>
-<div id="collapseSpoiler2" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler2" aria-labelledby="headingSpoiler2">
-<div class="accordion-body">
-<p>If you need it, read in detail about the <a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">R
-probability functions for the normal distribution</a>, each of its
-definitions and identify in which part of a distribution they are
-located!</p>
-<figure><img src="../fig/fig5a-normaldistribution.png" alt="" class="figure mx-auto d-block"><figcaption>The four probability functions for the normal
-distribution (<a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">Jack
-Weiss, 2012</a>)</figcaption></figure>
-</div>
-</div>
-</div>
-</div>
-<p>If you look at <code>?stats::Distributions</code>, each type of
-distribution has a unique set of functions. However,
-<code>{epiparameter}</code> gives you the same four functions to access
-each of the values above for any <code>&lt;epidist&gt;</code> object you
-want!</p>
-<div class="codewrapper sourceCode" id="cb46">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># plot this to have a visual reference</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb47">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the density value at quantile value of 10 (days)</span></span>
-<span><span class="fu">density</span><span class="op">(</span><span class="va">covid_serialint</span>, at <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 0.01911607</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb49">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the cumulative probability at quantile value of 10 (days)</span></span>
-<span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 0.9466605</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb51">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># the quantile value (day) at a cumulative probability of 60%</span></span>
-<span><span class="fu">quantile</span><span class="op">(</span><span class="va">covid_serialint</span>, p <span class="op">=</span> <span class="fl">0.6</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>[1] 4.618906</code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb53">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># generate 10 random values (days) given</span></span>
-<span><span class="co"># the distribution family and its parameters</span></span>
-<span><span class="fu">generate</span><span class="op">(</span><span class="va">covid_serialint</span>, times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code> [1] 2.889422 3.306235 3.308088 4.208431 2.248277 2.692921 1.221014 9.998734
- [9] 3.758726 9.042027</code></pre>
-</div>
-<div id="accordionInstructor2" class="accordion instructor-note accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button instructor-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseInstructor2" aria-expanded="false" aria-controls="collapseInstructor2">
-  <h3 class="accordion-header" id="headingInstructor2">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="edit-2"></i></div>Instructor Note</h3>
-</button>
-<div id="collapseInstructor2" class="accordion-collapse collapse" data-bs-parent="#accordionInstructor2" aria-labelledby="headingInstructor2">
-<div class="accordion-body">
-<p>Access to the reference documentation (Help files) for these
-functions is accessible with the three double-colon notation:
-<code>epiparameter:::</code></p>
-<ul>
-<li><code>?epiparameter:::density.epidist()</code></li>
-<li><code>?epiparameter:::cdf.epidist()</code></li>
-<li><code>?epiparameter:::quantile.epidist()</code></li>
-<li><code>?epiparameter:::generate.epidist()</code></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="window-for-contact-tracing-and-the-serial-interval" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="window-for-contact-tracing-and-the-serial-interval" class="callout-inner">
-<h3 class="callout-title">Window for contact tracing and the Serial
-interval<a class="anchor" aria-label="anchor" href="#window-for-contact-tracing-and-the-serial-interval"></a>
-</h3>
-<div class="callout-content">
-<p>The <strong>serial interval</strong> is important in the optimization
-of contact tracing since it provides a time window for the containment
-of a disease spread (<a href="https://academic.oup.com/aje/article/158/11/1039/162725" class="external-link">Fine,
-2003</a>). Depending on the serial interval, we can evaluate the need to
-expand the number of days pre-onset to consider in the contact tracing
-to include more backwards contacts (<a href="https://assets.publishing.service.gov.uk/media/61e9ab3f8fa8f50597fb3078/S0523_Oxford_-_Backwards_contact_tracing.pdf" class="external-link">Davis
-et al., 2020</a>).</p>
-<p>With the COVID-19 serial interval (<code>covid_serialint</code>)
-calculate:</p>
-<ul>
-<li>How much more of the backward cases could be captured if the contact
-tracing method considered contacts up to 6 days pre-onset compared to 2
-days pre-onset?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint6" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint6" aria-expanded="false" aria-controls="collapseHint6">
-  <h4 class="accordion-header" id="headingHint6">Give me a hint</h4>
-</button>
-<div id="collapseHint6" class="accordion-collapse collapse" data-bs-parent="#accordionHint6" aria-labelledby="headingHint6">
-<div class="accordion-body">
-<p>In Figure 5 from the <a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">R
-probability functions for the normal distribution</a>, the shadowed
-section represents a cumulative probability of <code>0.997</code> for
-the quantile value at <code>x = 2</code>.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
-  <h4 class="accordion-header" id="headingSolution6">Show me the solution</h4>
-</button>
-<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb55">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb56">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
-<span><span class="fu">cdf</span><span class="op">(</span><span class="va">covid_serialint</span>, q <span class="op">=</span> <span class="fl">6</span><span class="op">)</span></span></code></pre>
-</div>
-<p>Given the COVID-19 serial interval:</p>
-<ul>
-<li><p>A contact tracing method considering contacts up to 2 days
-pre-onset will capture around 11.1% of backward cases.</p></li>
-<li><p>If this period is extended to 6 days pre-onset, this could
-include 76.2% of backward contacts.</p></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution7" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
-  <h4 class="accordion-header" id="headingSolution7">What if</h4>
-</button>
-<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
-<div class="accordion-body">
-<p>If we exchange the question between days and cumulative probability
-to:</p>
-<ul>
-<li>When considering secondary cases, how many days following the
-symptom onset of primary cases can we expect 55% of symptom onset to
-occur?</li>
-</ul>
-<div class="codewrapper sourceCode" id="cb57">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">quantile</span><span class="op">(</span><span class="va">covid_serialint</span>, p <span class="op">=</span> <span class="fl">0.55</span><span class="op">)</span></span></code></pre>
-</div>
-<p>An interpretation could be:</p>
-<ul>
-<li>The 55% percent of the symptom onset of secondary cases will happen
-after 4.2 days after the symptom onset of primary cases.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-</section><section id="discretize-a-continuous-distribution"><h2 class="section-heading">Discretize a continuous distribution<a class="anchor" aria-label="anchor" href="#discretize-a-continuous-distribution"></a>
-</h2>
-<hr class="half-width">
-<p>We are getting closer to the end! <code>EpiNow2::dist_spec()</code>
-still needs a maximum value (<code>max</code>).</p>
-<p>One way to do this is to get the quantile value for the
-distribution’s 99.9th percentile or <code>0.999</code> cumulative
-probability. For this, we need access to the set of distribution
-functions for our <code>&lt;epidist&gt;</code> object.</p>
-<p>We can use the set of distribution functions for a
-<em>continuous</em> distribution (as above). However, these values will
-be <em>continuous</em> numbers. We can <strong>discretize</strong> the
-continuous distribution stored in our <code>&lt;epidist&gt;</code>
-object to get discrete values from a continuous distribution.</p>
-<p>When we <code>epiparameter::discretise()</code> the continuous
-distribution we get a <strong>discrete</strong>(-ized) distribution:</p>
-<div class="codewrapper sourceCode" id="cb58">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint_discrete</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_serialint_discrete</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>Disease: COVID-19
-Pathogen: SARS-CoV-2
-Epi Distribution: serial interval
-Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
-coronavirus (COVID-19) infections." _International Journal of
-Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
-&lt;https://doi.org/10.1016/j.ijid.2020.02.060&gt;.
-Distribution: discrete lnorm
-Parameters:
-  meanlog: 1.386
-  sdlog: 0.568</code></pre>
-</div>
-<p>We identify this change in the <code>Distribution:</code> output line
-of the <code>&lt;epidist&gt;</code> object. Take a double check to this
-line:</p>
-<pre><code>Distribution: discrete lnorm</code></pre>
-<p>While for a <strong>continuous</strong> distribution, we plot the
-<em>Probability Density Function (PDF)</em>, for a
-<strong>discrete</strong> distribution, we plot the <em>Probability Mass
-Function (PMF)</em>:</p>
-<div class="codewrapper sourceCode" id="cb61">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># continuous</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># discrete</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint_discrete</span><span class="op">)</span></span></code></pre>
-</div>
-<p>To finally get a <code>max</code> value, let’s access the quantile
-value of the 99.9th percentile or <code>0.999</code> probability of the
-distribution with the <code>prob_dist$q</code> notation, similarly to
-how we access the <code>summary_stats</code> values.</p>
-<div class="codewrapper sourceCode" id="cb62">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint_discrete_max</span> <span class="op">&lt;-</span></span>
-<span>  <span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="lenght-of-quarantine-and-incubation-period" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="lenght-of-quarantine-and-incubation-period" class="callout-inner">
-<h3 class="callout-title">Lenght of quarantine and Incubation
-period<a class="anchor" aria-label="anchor" href="#lenght-of-quarantine-and-incubation-period"></a>
-</h3>
-<div class="callout-content">
-<p>The <strong>incubation period</strong> distribution is a useful delay
-to assess the length of active monitoring or quarantine (<a href="https://www.acpjournals.org/doi/10.7326/M20-0504" class="external-link">Lauer et al.,
-2020</a>). Similarly, delays from symptom onset to recovery (or death)
-will determine the required duration of health-care and case isolation
-(<a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371" class="external-link">Cori
-et al., 2017</a>).</p>
-<p>Calculate:</p>
-<ul>
-<li>Within what exact time frame do 99% of individuals who develop
-COVID-19 symptoms exhibit them after infection?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint7" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint7" aria-expanded="false" aria-controls="collapseHint7">
-  <h4 class="accordion-header" id="headingHint7">Give me a hint</h4>
-</button>
-<div id="collapseHint7" class="accordion-collapse collapse" data-bs-parent="#accordionHint7" aria-labelledby="headingHint7">
-<div class="accordion-body">
-<p>What delay distribution measures the time between infection and the
-onset of symptoms?</p>
-<p>The probability function for <code>&lt;epidist&gt;</code>
-<strong>discrete</strong> distributions differ from the
-<em>continuous</em> ones!</p>
-<div class="codewrapper sourceCode" id="cb63">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># plot to have a visual reference</span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">covid_serialint_discrete</span>, day_range <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">20</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># density value at quantile value 10 (day)</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">d</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># cumulative probability at quantile value 10 (day)</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">cdf</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># In what quantile value (days) do we have the 60% cumulative probability?</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.6</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># generate random values</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">r</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution8" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
-  <h4 class="accordion-header" id="headingSolution8">Show me the solution</h4>
-</button>
-<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb64">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_incubation</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>    single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.99</span><span class="op">)</span></span></code></pre>
-</div>
-<p>99% of those who develop COVID-19 symptoms will do so within 16 days
-of infection.</p>
-<p>Now, <em>Is this result expected in epidemiological terms?</em></p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution9" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
-  <h4 class="accordion-header" id="headingSolution9">how to create a distribution plot?</h4>
-</button>
-<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
-<div class="accordion-body">
-<p>From a maximum value with <code>$prob_dist$q()</code>, we can create
-a sequence of quantile values as a numeric vector and map density values
-for each:</p>
-<div class="codewrapper sourceCode" id="cb65">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># create a discrete distribution visualization</span></span>
-<span><span class="co"># from a maximum value from the distribution</span></span>
-<span><span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span><span class="fl">0.999</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># generate quantile values</span></span>
-<span>  <span class="co"># as a sequence for each natural number</span></span>
-<span>  <span class="fu">seq</span><span class="op">(</span><span class="fl">1L</span>, to <span class="op">=</span> <span class="va">.</span>, by <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># coerce numeric vector to data frame</span></span>
-<span>  <span class="fu">as_tibble_col</span><span class="op">(</span>column_name <span class="op">=</span> <span class="st">"quantile_values"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">mutate</span><span class="op">(</span></span>
-<span>    <span class="co"># map density values</span></span>
-<span>    <span class="co"># for each quantile in the density function</span></span>
-<span>    density_values <span class="op">=</span></span>
-<span>      <span class="va">covid_serialint_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">d</span><span class="op">(</span><span class="va">quantile_values</span><span class="op">)</span></span>
-<span>  <span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="co"># create plot</span></span>
-<span>  <span class="fu">ggplot</span><span class="op">(</span></span>
-<span>    <span class="fu">aes</span><span class="op">(</span></span>
-<span>      x <span class="op">=</span> <span class="va">quantile_values</span>,</span>
-<span>      y <span class="op">=</span> <span class="va">density_values</span></span>
-<span>    <span class="op">)</span></span>
-<span>  <span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="plug-in-epiparameter-to-epinow2"><h2 class="section-heading">Plug-in <code>{epiparameter}</code> to <code>{EpiNow2}</code><a class="anchor" aria-label="anchor" href="#plug-in-epiparameter-to-epinow2"></a>
-</h2>
-<hr class="half-width">
-<p>Now we can plug everything into the <code>EpiNow2::dist_spec()</code>
-function!</p>
-<div class="codewrapper sourceCode" id="cb66">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">serial_interval_covid</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">covid_serialint</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">covid_serialint_discrete_max</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_covid</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>
-  Fixed distribution with PMF [0.18 0.11 0.08 0.066 0.057 0.05 0.045 0.041 0.037 0.034 0.032 0.03 0.028 0.027 0.025 0.024 0.023 0.022 0.021 0.02 0.019 0.019 0.018]</code></pre>
-</div>
-<div id="warning" class="callout callout">
-<div class="callout-square">
-<i class="callout-icon" data-feather="bell"></i>
-</div>
-<div id="warning" class="callout-inner">
-<h3 class="callout-title">Warning<a class="anchor" aria-label="anchor" href="#warning"></a>
-</h3>
-<div class="callout-content">
-<p>Using the serial interval instead of the generation time is an
-alternative that can propagate bias in your estimates, even more so in
-diseases with reported pre-symptomatic transmission.</p>
-</div>
-</div>
-</div>
-<p>Let’s replace the <code>generation_time</code> input we used for
-<code>EpiNow2::epinow()</code>.</p>
-<div class="codewrapper sourceCode" id="cb68">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-<div id="ebolas-effective-reproduction-number" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-effective-reproduction-number" class="callout-inner">
-<h3 class="callout-title">Ebola’s effective reproduction number<a class="anchor" aria-label="anchor" href="#ebolas-effective-reproduction-number"></a>
-</h3>
-<div class="callout-content">
-<p>Download and read the <a href="../data/ebola_cases.csv">Ebola
-dataset</a>:</p>
-<ul>
-<li>Reuse one epidemiological parameter to estimate the effective
-reproduction number for the Ebola dataset.</li>
-<li>Why did you choose that parameter?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint8" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint8" aria-expanded="false" aria-controls="collapseHint8">
-  <h4 class="accordion-header" id="headingHint8">Give me a hint</h4>
-</button>
-<div id="collapseHint8" class="accordion-collapse collapse" data-bs-parent="#accordionHint8" aria-labelledby="headingHint8">
-<div class="accordion-body">
-<p>To calculate the <span class="math inline">\(R_t\)</span>, we
-need:</p>
-<ul>
-<li>data set with confirmed cases per day and</li>
-<li>one key delay distribution</li>
-</ul>
-<p>Key functions we applied in this episode are:</p>
-<ul>
-<li><code>epidist_db()</code></li>
-<li><code>list_distributions()</code></li>
-<li><code>discretise()</code></li>
-<li>probability functions for continuous and discrete distributions</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution10" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution10" aria-expanded="false" aria-controls="collapseSolution10">
-  <h4 class="accordion-header" id="headingSolution10">Show me the solution</h4>
-</button>
-<div id="collapseSolution10" class="accordion-collapse collapse" data-bs-parent="#accordionSolution10" aria-labelledby="headingSolution10">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb69">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># read data</span></span>
-<span><span class="co"># e.g.: if path to file is data/raw-data/ebola_cases.csv then:</span></span>
-<span><span class="va">ebola_confirmed</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">read_csv</span><span class="op">(</span><span class="fu">here</span><span class="fu">::</span><span class="fu">here</span><span class="op">(</span><span class="st">"data"</span>, <span class="st">"raw-data"</span>, <span class="st">"ebola_cases.csv"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># list distributions</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"ebola"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution</span></span>
-<span><span class="va">ebola_serial</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_serial_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_serial</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># name of the type of distribution</span></span>
-<span><span class="co"># only for the discretised distribution</span></span>
-<span><span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="va">name</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">ebola_confirmed</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_ebola</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="adjusting-for-reporting-delays"><h2 class="section-heading">Adjusting for reporting delays<a class="anchor" aria-label="anchor" href="#adjusting-for-reporting-delays"></a>
-</h2>
-<hr class="half-width">
-<p>Estimating <span class="math inline">\(R_t\)</span> requires data on
-the daily number of new infections. Due to lags in the development of
-detectable viral loads, symptom onset, seeking care, and reporting,
-these numbers are not readily available. All observations reflect
-transmission events from some time in the past. In other words, if <span class="math inline">\(d\)</span> is the delay from infection to
-observation, then observations at time <span class="math inline">\(t\)</span> inform <span class="math inline">\(R_{t−d}\)</span>, not <span class="math inline">\(R_t\)</span>. <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">(Gostic
-et al., 2020)</a></p>
-<figure><img src="../fig/disease-reporting.jpg" alt="" class="figure mx-auto d-block"><figcaption>Timeline for chain of disease reporting, the
-Netherlands. Lab, laboratory; PHA, public health authority. From <a href="https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article" class="external-link">Marinović
-et al., 2015</a></figcaption></figure><p>The <strong>delay distribution</strong> could be inferred jointly
-with the underlying times of infection or estimated as the sum of the
-<strong><a href="reference.html#incubation">incubation
-period</a></strong> distribution and the distribution of delays from
-symptom onset to observation from line list data <strong>(<a href="reference.html#reportingdelay">reporting delay</a>)</strong>.</p>
-<p>For <a href="https://epiforecasts.io/EpiNow2/" class="external-link">EpiNow2</a>, we can specify these two complementary
-delay distributions in the <code>delays</code> argument.</p>
-<figure><img src="../fig/rt-adjusting-delays.png" alt="" class="figure mx-auto d-block"><figcaption>Rt is a measure of transmission at time t.
-Observations after time t must be adjusted. ICU, intensive care unit.
-From <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">Gostic
-et al., 2020</a></figcaption></figure><div id="reuse-an-incubation-period-for-covid-19" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="reuse-an-incubation-period-for-covid-19" class="callout-inner">
-<h3 class="callout-title">Reuse an Incubation period for COVID-19<a class="anchor" aria-label="anchor" href="#reuse-an-incubation-period-for-covid-19"></a>
-</h3>
-<div class="callout-content">
-<p>Use <code>{epiparameter}</code> to:</p>
-<ul>
-<li>Find an incubation period for COVID-19.</li>
-<li>Add our last <code>epinow()</code> code chunk using the
-<code>delays</code> argument and the <code>delay_opts()</code> helper
-function.</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint9" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint9" aria-expanded="false" aria-controls="collapseHint9">
-  <h4 class="accordion-header" id="headingHint9">Give me a hint</h4>
-</button>
-<div id="collapseHint9" class="accordion-collapse collapse" data-bs-parent="#accordionHint9" aria-labelledby="headingHint9">
-<div class="accordion-body">
-<p>The <code>delays</code> argument and the <code>delay_opts()</code>
-helper function are analogous to the <code>generation_time</code>
-argument and the <code>generation_time_opts()</code> helper
-function.</p>
-<div class="codewrapper sourceCode" id="cb70">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_time_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution11" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution11" aria-expanded="false" aria-controls="collapseSolution11">
-  <h4 class="accordion-header" id="headingSolution11">Show me the solution</h4>
-</button>
-<div id="collapseSolution11" class="accordion-collapse collapse" data-bs-parent="#accordionSolution11" aria-labelledby="headingSolution11">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb71">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_incubation</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Natalie"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">covid_incubation</span></span>
-<span></span>
-<span><span class="va">covid_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">epiparameter</span><span class="fu">::</span><span class="fu">discretise</span><span class="op">(</span><span class="va">covid_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">incubation_time_covid</span> <span class="op">&lt;-</span> <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>  mean <span class="op">=</span> <span class="va">covid_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>  sd <span class="op">=</span> <span class="va">covid_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>  max <span class="op">=</span> <span class="va">covid_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">example_confirmed</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">60</span><span class="op">]</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_covid</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_time_covid</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">base</span><span class="fu">::</span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution12" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution12" aria-expanded="false" aria-controls="collapseSolution12">
-  <h4 class="accordion-header" id="headingSolution12">How much has it changed?</h4>
-</button>
-<div id="collapseSolution12" class="accordion-collapse collapse" data-bs-parent="#accordionSolution12" aria-labelledby="headingSolution12">
-<div class="accordion-body">
-<p>After adding the incubation period, discuss:</p>
-<ul>
-<li>Does the retrospective trend of forecast change?</li>
-<li>Has the uncertainty changed?</li>
-<li>How would you explain or interpret any of these changes?</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays" class="callout-inner">
-<h3 class="callout-title">Ebola’s effective reproduction number was
-adjusted by reporting delays<a class="anchor" aria-label="anchor" href="#ebolas-effective-reproduction-number-was-adjusted-by-reporting-delays"></a>
-</h3>
-<div class="callout-content">
-<p>Using the same <a href="../data/ebola_cases.csv">Ebola dataset</a>:</p>
-<ul>
-<li>Reuse one additional epidemiological parameter for the
-<code>delays</code> argument in <code>EpiNow2::epinow()</code>.</li>
-<li>Estimate the effective reproduction number using
-<code>EpiNow2::epinow()</code>.</li>
-<li>Why did you choose that parameter?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint10" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint10" aria-expanded="false" aria-controls="collapseHint10">
-  <h4 class="accordion-header" id="headingHint10">Give me a hint</h4>
-</button>
-<div id="collapseHint10" class="accordion-collapse collapse" data-bs-parent="#accordionHint10" aria-labelledby="headingHint10">
-<div class="accordion-body">
-<p>We can use two complementary delay distributions to estimate the
-<span class="math inline">\(R_t\)</span> at time <span class="math inline">\(t\)</span>.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution13" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution13" aria-expanded="false" aria-controls="collapseSolution13">
-  <h4 class="accordion-header" id="headingSolution13">Show me the solution</h4>
-</button>
-<div id="collapseSolution13" class="accordion-collapse collapse" data-bs-parent="#accordionSolution13" aria-labelledby="headingSolution13">
-<div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb72">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># read data</span></span>
-<span><span class="co"># e.g.: if path to file is data/raw-data/ebola_cases.csv then:</span></span>
-<span><span class="va">ebola_confirmed</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">read_csv</span><span class="op">(</span><span class="fu">here</span><span class="fu">::</span><span class="fu">here</span><span class="op">(</span><span class="st">"data"</span>, <span class="st">"raw-data"</span>, <span class="st">"ebola_cases.csv"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># list distributions</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"ebola"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution for the generation time</span></span>
-<span><span class="va">ebola_serial</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_serial_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_serial</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">serial_interval_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_serial</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_serial_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># subset one distribution for delay of the incubation period</span></span>
-<span><span class="va">ebola_incubation</span> <span class="op">&lt;-</span> <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">ebola_incubation_discrete</span> <span class="op">&lt;-</span> <span class="fu">discretise</span><span class="op">(</span><span class="va">ebola_incubation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">incubation_period_ebola</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">ebola_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">ebola_incubation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">sd</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">ebola_incubation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"gamma"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">ebola_confirmed</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">serial_interval_ebola</span><span class="op">)</span>,</span>
-<span>  delays <span class="op">=</span> <span class="fu">delay_opts</span><span class="op">(</span><span class="va">incubation_period_ebola</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="extract-parameters" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="extract-parameters" class="callout-inner">
-<h3 class="callout-title">Extract parameters<a class="anchor" aria-label="anchor" href="#extract-parameters"></a>
-</h3>
-<div class="callout-content">
-<p>Use the <code>influenza_england_1978_school</code> from the
-<a href="https://github.com/reconhub/outbreaks" class="external-link">outbreaks</a> package to calculate the effective reproduction
-number.</p>
-</div>
-</div>
-</div>
-<div id="accordionHint11" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint11" aria-expanded="false" aria-controls="collapseHint11">
-  <h4 class="accordion-header" id="headingHint11">Give me a hint</h4>
-</button>
-<div id="collapseHint11" class="accordion-collapse collapse" data-bs-parent="#accordionHint11" aria-labelledby="headingHint11">
-<div class="accordion-body">
-<p>How to get the mean and standard deviation from a generation time
-with median and quantiles as summary statistics?</p>
-<ul>
-<li>Look at how to extract parameters from <code>{epiparameter}</code>
-vignette on <a href="https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html" class="external-link">parameter
-extraction and conversion</a>
-</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution14" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution14" aria-expanded="false" aria-controls="collapseSolution14">
-  <h4 class="accordion-header" id="headingSolution14">Show me the solution</h4>
-</button>
-<div id="collapseSolution14" class="accordion-collapse collapse" data-bs-parent="#accordionSolution14" aria-labelledby="headingSolution14">
-<div class="accordion-body">
-<!-- https://github.com/epiverse-trace/epiparameter/issues/114 -->
-<div class="codewrapper sourceCode" id="cb73">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># What parameters are available for Influenza?</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span>disease <span class="op">=</span> <span class="st">"influenza"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">list_distributions</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">as_tibble</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">count</span><span class="op">(</span><span class="va">epi_distribution</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_generation</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>    disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>    epi_dist <span class="op">=</span> <span class="st">"generation"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_generation_discrete</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">discretise</span><span class="op">(</span><span class="va">influenza_generation</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># problem</span></span>
-<span><span class="co"># the summary statistics do not have mean and sd</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">median</span></span>
-<span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span></span>
-<span></span>
-<span><span class="co"># solution</span></span>
-<span><span class="co"># extract parameters from percentiles</span></span>
-<span><span class="va">influenza_extracted</span> <span class="op">&lt;-</span> <span class="fu">extract_param</span><span class="op">(</span></span>
-<span>  type <span class="op">=</span> <span class="st">"percentiles"</span>,</span>
-<span>  values <span class="op">=</span> <span class="fu">c</span><span class="op">(</span><span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
-<span>             <span class="va">influenza_generation</span><span class="op">$</span><span class="va">summary_stats</span><span class="op">$</span><span class="va">quantiles</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>  distribution <span class="op">=</span> <span class="st">"lnorm"</span>,</span>
-<span>  percentiles <span class="op">=</span> <span class="fu">c</span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.95</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_extracted</span></span>
-<span></span>
-<span><span class="va">generation_time_influenza</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">dist_spec</span><span class="op">(</span></span>
-<span>    mean <span class="op">=</span> <span class="va">influenza_extracted</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
-<span>    sd <span class="op">=</span> <span class="va">influenza_extracted</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span>,</span>
-<span>    max <span class="op">=</span> <span class="va">influenza_generation_discrete</span><span class="op">$</span><span class="va">prob_dist</span><span class="op">$</span><span class="fu">q</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.999</span><span class="op">)</span>,</span>
-<span>    distribution <span class="op">=</span> <span class="st">"lognormal"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="va">influenza_cleaned</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">outbreaks</span><span class="fu">::</span><span class="va">influenza_england_1978_school</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">select</span><span class="op">(</span><span class="va">date</span>, confirm <span class="op">=</span> <span class="va">in_bed</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">epinow_estimates</span> <span class="op">&lt;-</span> <span class="fu">epinow</span><span class="op">(</span></span>
-<span>  <span class="co"># cases</span></span>
-<span>  reported_cases <span class="op">=</span> <span class="va">influenza_cleaned</span>,</span>
-<span>  <span class="co"># delays</span></span>
-<span>  generation_time <span class="op">=</span> <span class="fu">generation_time_opts</span><span class="op">(</span><span class="va">generation_time_influenza</span><span class="op">)</span>,</span>
-<span>  <span class="co"># computation</span></span>
-<span>  stan <span class="op">=</span> <span class="fu">stan_opts</span><span class="op">(</span></span>
-<span>    cores <span class="op">=</span> <span class="fl">4</span>, samples <span class="op">=</span> <span class="fl">1000</span>, chains <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>    control <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>adapt_delta <span class="op">=</span> <span class="fl">0.99</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot</span><span class="op">(</span><span class="va">epinow_estimates</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</section><section id="when-to-reuse-when-to-estimate"><h2 class="section-heading">When to reuse? When to estimate?<a class="anchor" aria-label="anchor" href="#when-to-reuse-when-to-estimate"></a>
-</h2>
-<hr class="half-width">
-<p>In the early stage of an outbreak, we can rely on reusing parameters
-for known pathogens to unknown ones, like for the <a href="https://en.wikipedia.org/wiki/Disease_X" class="external-link">Disease X</a>, a pathogen
-currently unknown to cause human disease and potentially cause a serious
-international epidemic (<a href="https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts" class="external-link">WHO,
-2018</a>).</p>
-<p>But when data from <strong>lines list paired with contact
-tracing</strong> is available, we can estimate the key delay
-distributions that best fit our data. These will help us to inform,
-compare and update any previous estimate about questions like:</p>
-<ul>
-<li>How long should contacts be followed?</li>
-<li>What is the required duration of contact tracing?</li>
-<li>How long should cases be isolated to reduce transmission?</li>
-</ul>
-<p>However, the methods to accurately estimate delays like the
-generation interval from contact tracing data involve adjusting for
-biases like <strong><a href="reference.html#censoring">censoring</a></strong>, <strong><a href="reference.html#truncation">right truncation</a></strong> and
-<strong><a href="reference.html#phasebias">epidemic phase
-bias</a></strong>. <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409" class="external-link">(Gostic
-et al., 2020)</a></p>
-<p>We can identify what entries in the <code>{epiparameter}</code>
-library assessed for these biases in their methodology with the
-<code>method_assess</code> nested entry:</p>
-<div class="codewrapper sourceCode" id="cb74">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">covid_serialint</span><span class="op">$</span><span class="va">method_assess</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>$censored
-[1] TRUE
-
-$right_truncated
-[1] TRUE
+                </div>
+              </div>
+            </div>
 
-$phase_bias_adjusted
-[1] FALSE</code></pre>
-</div>
-<div id="how-to-estimate-delay-distributions-for-disease-x" class="callout testimonial">
-<div class="callout-square">
-<i class="callout-icon" data-feather="heart"></i>
-</div>
-<div id="how-to-estimate-delay-distributions-for-disease-x" class="callout-inner">
-<h3 class="callout-title">How to estimate delay distributions for
-Disease X?<a class="anchor" aria-label="anchor" href="#how-to-estimate-delay-distributions-for-disease-x"></a>
-</h3>
-<div class="callout-content">
-<p>Refer to this excellent tutorial on estimating the serial interval
-and incubation period of Disease X accounting for <em>censoring</em>
-using Bayesian inference with packages like <a href="https://mc-stan.org/rstan/" class="external-link">rstan</a> and
-<a href="https://cran.r-project.org/package=coarseDataTools" class="external-link">coarseDataTools</a>.</p>
-<ul>
-<li>Tutorial in English: <a href="https://rpubs.com/tracelac/diseaseX" class="external-link uri">https://rpubs.com/tracelac/diseaseX</a>
-<!-- to request -->
-</li>
-<li>Tutorial en Español: <a href="https://epiverse-trace.github.io/epimodelac/EnfermedadX.html" class="external-link uri">https://epiverse-trace.github.io/epimodelac/EnfermedadX.html</a>
-</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionSpoiler3" class="accordion spoiler-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler3" aria-expanded="false" aria-controls="collapseSpoiler3">
-  <h3 class="accordion-header" id="headingSpoiler3">
-<div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div>How to assess for pre-symptomatic infection?</h3>
-</button>
-<div id="collapseSpoiler3" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler3" aria-labelledby="headingSpoiler3">
-<div class="accordion-body">
-<p>The lengths of the Serial interval and Incubation period determine
-the type of disease transmission.</p>
-<figure><img src="../fig/incubation-period-serial-interval.jpg" alt="" class="figure mx-auto d-block"><figcaption>The relationship between the incubation period and
-serial interval. From <a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-2020</a></figcaption></figure><p>Estimating the proportion of pre-symptomatic infections, or the
-extent to which infectiousness precedes symptom onset will determine the
-effectiveness of contact tracing and the feasibility of controlling an
-outbreak (<a href="https://www.pnas.org/doi/full/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a> and <a href="https://www.thelancet.com/article/S2214-109X(20)30074-7/fulltext" class="external-link">Hellewell
-et al., 2020</a>).</p>
-<!-- The proportion of pre-symptomatic transmission contributes to the effective reproduction number and expected new cases. -->
-<figure><img src="../fig/reproduction-number-pre-symptomatic.png" alt="" class="figure mx-auto d-block"><figcaption>Parameter estimates. Plausible ranges for the key
-parameters R0 and θ (read the main text for sources) for four viral
-infections of public concern are shown as shaded regions. The size of
-the shaded area reflects the uncertainties in the parameter estimates.
-<a href="https://www.pnas.org/doi/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a></figcaption></figure><p>Meta-analysis on the proportion of pre-symptomatic and asymptomatic
-transmission in SARS-CoV-2 found limitations of the evidence given high
-heterogeneity and high risk of selection and information bias between
-studies (<a href="https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003" class="external-link">Buitrago-Garcia
-et al., 2022</a>). This is a call to action to improve the Outbreak
-Analytic pipelines to use and reuse in the early phase of an
-outbreak.</p>
-</div>
-</div>
-</div>
-</div>
-<div id="what-type-of-transmission" class="callout challenge">
-<div class="callout-square">
-<i class="callout-icon" data-feather="zap"></i>
-</div>
-<div id="what-type-of-transmission" class="callout-inner">
-<h3 class="callout-title">What type of transmission?<a class="anchor" aria-label="anchor" href="#what-type-of-transmission"></a>
-</h3>
-<div class="callout-content">
-<p>Compare the serial interval and incubation period of Influenza and
-MERS:</p>
-<ul>
-<li>What type of transmission has Influenza?</li>
-<li>What type of transmission has MERS?</li>
-<li>Do these results correlate with the available evidence?</li>
-</ul>
-</div>
-</div>
-</div>
-<div id="accordionHint12" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint12" aria-expanded="false" aria-controls="collapseHint12">
-  <h4 class="accordion-header" id="headingHint12">Give me a hint</h4>
-</button>
-<div id="collapseHint12" class="accordion-collapse collapse" data-bs-parent="#accordionHint12" aria-labelledby="headingHint12">
-<div class="accordion-body">
-<p>For types of transmission, we refer to infections with symptomatic or
-pre-symptomatic transmission.</p>
-<p>Key functions:</p>
-<ul>
-<li><code>epidist_db()</code></li>
-<li><code>epidist$summary_stats$</code></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div id="accordionSolution15" class="accordion challenge-accordion accordion-flush">
-<div class="accordion-item">
-<button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution15" aria-expanded="false" aria-controls="collapseSolution15">
-  <h4 class="accordion-header" id="headingSolution15">Show me the solution</h4>
-</button>
-<div id="collapseSolution15" class="accordion-collapse collapse" data-bs-parent="#accordionSolution15" aria-labelledby="headingSolution15">
-<div class="accordion-body">
-<p>In this solution we use <code>purrr::pluck()</code> to extract
-elements within the <code>summary_stats</code> object which is of class
-<code>list</code>.</p>
-<div class="codewrapper sourceCode" id="cb76">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># pre-symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"influenza"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"mers"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"median"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"mers"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper sourceCode" id="cb77">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># pre-symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Stephen"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"covid"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  author <span class="op">=</span> <span class="st">"Nishiura"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># symptomatic transmission</span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"incubation"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">epidist_db</span><span class="op">(</span></span>
-<span>  disease <span class="op">=</span> <span class="st">"ebola"</span>,</span>
-<span>  epi_dist <span class="op">=</span> <span class="st">"serial"</span>,</span>
-<span>  single_epidist <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"summary_stats"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
-<span>  <span class="fu">pluck</span><span class="op">(</span><span class="st">"mean"</span><span class="op">)</span></span></code></pre>
-</div>
-</div>
-</div>
-</div>
+            <hr class="half-width">
+<div class="accordion accordion-flush resources" id="accordionFlush12">
+              <div class="accordion-item">
+                <h2 class="accordion-header" id="flush-headingTwelve">
+                  <button class="accordion-button collapsed" id="resources" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapseTwelve" aria-expanded="false" aria-controls="flush-collapseTwelve">
+                    RESOURCES
+                  </button>
+                </h2>
+                <div id="flush-collapseTwelve" class="accordion-collapse collapse" aria-labelledby="flush-headingTwelve" data-bs-parent="#accordionFlush12">
+                  <div class="accordion-body">
+                    <ul>
+<li>
+                        <a href="../instructor/key-points.html">Key Points</a>
+                      </li>
+                      <li>
+                        <a href="../instructor/instructor-notes.html">Instructor Notes</a>
+                      </li>
+                      <li>
+                        <a href="../instructor/images.html">Extract All Images</a>
+                      </li>
+                      <hr>
+<li><a class="dropdown-item" href="reference.html">Glossary of Terms: Epiverse-TRACE</a></li>
+                    </ul>
 </div>
-<!--
-## Concept map
+                </div>
+              </div>
+            </div>
+            <hr class="half-width resources">
+<a href="../instructor/aio.html">See all in one page</a>
+            
 
-update it from last epiparameter test 
--->
-<div id="keypoints1" class="callout keypoints">
-<div class="callout-square">
-<i class="callout-icon" data-feather="key"></i>
-</div>
-<div class="callout-inner">
-<h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="#keypoints1"></a>
-</h3>
-<div class="callout-content">
-<ul>
-<li>Use <code>{epiparameter}</code> to access the systematic review
-catalogue of epidemiological delay distributions.</li>
-<li>Use <code>epidist_db()</code> to select single delay
-distributions.</li>
-<li>Use <code>list_distributions()</code> for an overview of multiple
-delay distributions.</li>
-<li>Use <code>discretise()</code> to convert continuous to discrete
-delay distributions.</li>
-<li>Use <code>{epiparameter}</code> probability functions for any delay
-distributions.</li>
-</ul>
-</div>
-</div>
+            <hr class="d-none d-sm-block d-md-none">
+<div class="d-grid gap-1">
+            
+            </div>
+          </div>
+<!-- /div.accordion -->
+        </div>
+<!-- /div.sidebar-inner -->
+      </nav>
 </div>
-<!-- 
-Place links that you need to refer to multiple times across pages here. Delete
-any links that you are not going to use. 
- -->
-</section></section><section id="aio-quantify-transmissibility"><p>Content from <a href="quantify-transmissibility.html">Quantifying transmission</a></p>
+<!-- /div.sidebar -->
+  </div>
+<!-- /div.sidebar-col -->
+<!-- END:   inst/pkgdown/templates/navbar.html-->
+
+        <!-- START: inst/pkgdown/templates/content-extra.html -->
+  <div class="col-xl-8 col-lg-12 primary-content">
+    <main id="main-content" class="main-content"><div class="container lesson-content">
+        
+        
+<section id="aio-quantify-transmissibility"><p>Content from <a href="quantify-transmissibility.html">Quantifying transmission</a></p>
 <hr>
-<p>Last updated on 2024-02-08 |
+<p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/quantify-transmissibility.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <p>Estimated time: <i aria-hidden="true" data-feather="clock"></i> 30 minutes</p>
@@ -3052,7 +392,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
   <h3 class="accordion-header" id="headingInstructor1">
 <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="edit-2"></i></div>Instructor Note</h3>
 </button>
-<div id="collapseInstructor1" class="accordion-collapse collapse" data-bs-parent="#accordionInstructor1" aria-labelledby="headingInstructor1">
+<div id="collapseInstructor1" class="accordion-collapse collapse" aria-labelledby="headingInstructor1" data-bs-parent="#accordionInstructor1">
 <div class="accordion-body">
 <p>This tutorial illustrates the usage of <code>epinow()</code> to
 estimate the time-varying reproduction number and infection times.
@@ -3420,11 +760,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:24:53] epinow: There were 12 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 13:39:13] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 10:24:53] epinow: Examine the pairs() plot to diagnose sampling problems
- - </code></pre>
+WARN [2024-02-12 13:39:13] epinow: Examine the pairs() plot to diagnose sampling problems
+ - 
+WARN [2024-02-12 13:39:15] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
+Running the chains for more iterations may help. See
+https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
 </div>
 <div class="section level3">
 <h3 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a>
@@ -3466,24 +809,24 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date     7232 (4056 -- 12590)
+1: New confirmed cases by infection date     7340 (4136 -- 12899)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.57 -- 1.3)
-4:                        Rate of growth -0.015 (-0.064 -- 0.039)
-5:          Doubling/halving time (days)          -47 (18 -- -11)</code></pre>
+3:            Effective reproduction no.        0.9 (0.58 -- 1.4)
+4:                        Rate of growth -0.013 (-0.062 -- 0.042)
+5:          Doubling/halving time (days)          -54 (17 -- -11)</code></pre>
 </div>
 <p>As these estimates are based on partial data, they have a wide
 uncertainty interval.</p>
 <ul>
 <li><p>From the summary of our analysis we see that the expected change
 in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4056 – 12590).</p></li>
+cases 7340 (4136 – 12899).</p></li>
 <li><p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 0.89 (0.57 – 1.3).</p></li>
-<li><p>The exponential growth rate of case numbers is -0.015 (-0.064 –
-0.039).</p></li>
+data) is 0.9 (0.58 – 1.4).</p></li>
+<li><p>The exponential growth rate of case numbers is -0.013 (-0.062 –
+0.042).</p></li>
 <li><p>The doubling time (the time taken for case numbers to double) is
--47 (18 – -11).</p></li>
+-54 (17 – -11).</p></li>
 </ul>
 <div id="expected-change-in-daily-cases" class="callout callout">
 <div class="callout-square">
@@ -3590,17 +933,17 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>INFO [2024-02-08 10:24:58] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-02-08 10:24:58] Reporting estimates using data up to: 2020-04-28
-INFO [2024-02-08 10:24:58] No target directory specified so returning output
-INFO [2024-02-08 10:24:58] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-02-08 10:24:58] Regions excluded: none
-INFO [2024-02-08 11:08:54] Completed regional estimates
-INFO [2024-02-08 11:08:54] Regions with estimates: 13
-INFO [2024-02-08 11:08:54] Regions with runtime errors: 0
-INFO [2024-02-08 11:08:54] Producing summary
-INFO [2024-02-08 11:08:54] No summary directory specified so returning summary output
-INFO [2024-02-08 11:08:54] No target directory specified so returning timings</code></pre>
+<pre class="output" tabindex="0"><code>INFO [2024-02-12 13:39:18] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-02-12 13:39:18] Reporting estimates using data up to: 2020-04-28
+INFO [2024-02-12 13:39:18] No target directory specified so returning output
+INFO [2024-02-12 13:39:18] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-02-12 13:39:18] Regions excluded: none
+INFO [2024-02-12 14:27:58] Completed regional estimates
+INFO [2024-02-12 14:27:58] Regions with estimates: 13
+INFO [2024-02-12 14:27:58] Regions with runtime errors: 0
+INFO [2024-02-12 14:27:58] Producing summary
+INFO [2024-02-12 14:27:58] No summary directory specified so returning summary output
+INFO [2024-02-12 14:27:58] No target directory specified so returning timings</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3613,49 +956,49 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                      Region New confirmed cases by infection date
                       &lt;char&gt;                                &lt;char&gt;
- 1:            East Midlands                      340 (211 -- 551)
- 2:          East of England                      533 (332 -- 847)
- 3:                  England                   3548 (2264 -- 5694)
- 4:                   London                      292 (193 -- 460)
- 5:               North East                      251 (141 -- 430)
- 6:               North West                      566 (327 -- 870)
- 7:         Northern Ireland                         45 (23 -- 86)
- 8:                 Scotland                      289 (160 -- 522)
- 9:               South East                      599 (360 -- 996)
-10:               South West                      423 (294 -- 623)
-11:                    Wales                        95 (64 -- 136)
-12:            West Midlands                      270 (141 -- 488)
-13: Yorkshire and The Humber                      478 (284 -- 790)
+ 1:            East Midlands                      345 (208 -- 547)
+ 2:          East of England                      535 (324 -- 843)
+ 3:                  England                   3570 (2204 -- 5553)
+ 4:                   London                      298 (193 -- 464)
+ 5:               North East                      258 (149 -- 425)
+ 6:               North West                      554 (319 -- 885)
+ 7:         Northern Ireland                         43 (23 -- 85)
+ 8:                 Scotland                      286 (158 -- 544)
+ 9:               South East                      582 (357 -- 962)
+10:               South West                      417 (294 -- 608)
+11:                    Wales                        95 (65 -- 137)
+12:            West Midlands                      268 (141 -- 494)
+13: Yorkshire and The Humber                      472 (283 -- 798)
     Expected change in daily cases Effective reproduction no.
                             &lt;fctr&gt;                     &lt;char&gt;
- 1:              Likely increasing          1.2 (0.85 -- 1.6)
- 2:              Likely increasing          1.2 (0.84 -- 1.6)
- 3:              Likely decreasing         0.91 (0.65 -- 1.3)
- 4:              Likely decreasing         0.78 (0.56 -- 1.1)
- 5:              Likely decreasing         0.91 (0.59 -- 1.3)
- 6:              Likely decreasing         0.87 (0.57 -- 1.2)
- 7:              Likely decreasing         0.65 (0.39 -- 1.1)
- 8:              Likely decreasing          0.92 (0.6 -- 1.4)
- 9:                         Stable         0.99 (0.68 -- 1.4)
+ 1:              Likely increasing          1.2 (0.84 -- 1.6)
+ 2:              Likely increasing          1.2 (0.82 -- 1.6)
+ 3:              Likely decreasing         0.92 (0.64 -- 1.3)
+ 4:              Likely decreasing         0.79 (0.56 -- 1.1)
+ 5:              Likely decreasing         0.92 (0.61 -- 1.3)
+ 6:              Likely decreasing         0.86 (0.57 -- 1.2)
+ 7:              Likely decreasing         0.64 (0.39 -- 1.1)
+ 8:              Likely decreasing         0.91 (0.58 -- 1.4)
+ 9:                         Stable         0.98 (0.68 -- 1.4)
 10:                     Increasing           1.4 (1.1 -- 1.8)
-11:                     Decreasing        0.57 (0.41 -- 0.75)
+11:                     Decreasing        0.57 (0.42 -- 0.75)
 12:              Likely decreasing          0.7 (0.42 -- 1.1)
 13:                         Stable            1 (0.69 -- 1.4)
                Rate of growth Doubling/halving time (days)
                        &lt;char&gt;                       &lt;char&gt;
- 1:    0.022 (-0.02 -- 0.069)               31 (10 -- -34)
- 2:   0.022 (-0.022 -- 0.066)               32 (11 -- -31)
- 3:  -0.011 (-0.051 -- 0.033)              -61 (21 -- -14)
- 4:   -0.03 (-0.066 -- 0.011)              -23 (61 -- -11)
- 5:   -0.012 (-0.06 -- 0.038)              -58 (18 -- -11)
- 6:  -0.017 (-0.063 -- 0.024)              -40 (29 -- -11)
- 7:  -0.05 (-0.098 -- 0.0087)               -14 (80 -- -7)
- 8:  -0.011 (-0.059 -- 0.043)              -62 (16 -- -12)
- 9: -0.00073 (-0.046 -- 0.05)             -940 (14 -- -15)
-10:    0.048 (0.012 -- 0.087)                 14 (8 -- 57)
-11: -0.064 (-0.094 -- -0.034)            -11 (-20 -- -7.3)
-12:  -0.042 (-0.092 -- 0.012)             -17 (57 -- -7.5)
-13:  0.0026 (-0.044 -- 0.052)              260 (13 -- -16)</code></pre>
+ 1:   0.024 (-0.022 -- 0.069)               29 (10 -- -31)
+ 2:   0.021 (-0.025 -- 0.065)               32 (11 -- -28)
+ 3:  -0.011 (-0.052 -- 0.031)              -63 (22 -- -13)
+ 4:  -0.029 (-0.066 -- 0.013)              -24 (55 -- -11)
+ 5:  -0.011 (-0.057 -- 0.038)              -63 (18 -- -12)
+ 6:  -0.019 (-0.065 -- 0.023)              -37 (31 -- -11)
+ 7: -0.052 (-0.099 -- 0.0082)               -13 (85 -- -7)
+ 8:   -0.012 (-0.062 -- 0.05)              -60 (14 -- -11)
+ 9: -0.0029 (-0.046 -- 0.046)             -240 (15 -- -15)
+10:    0.046 (0.013 -- 0.084)               15 (8.2 -- 54)
+11: -0.065 (-0.093 -- -0.035)            -11 (-20 -- -7.4)
+12:  -0.043 (-0.092 -- 0.015)             -16 (48 -- -7.5)
+13:  0.0019 (-0.045 -- 0.051)              370 (14 -- -15)</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3694,7 +1037,7 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
  -->
 </section></section><section id="aio-create-forecast"><p>Content from <a href="create-forecast.html">Create a short-term forecast</a></p>
 <hr>
-<p>Last updated on 2024-02-08 |
+<p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/create-forecast.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <p>Estimated time: <i aria-hidden="true" data-feather="clock"></i> 60 minutes</p>
@@ -3784,10 +1127,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:15:14] epinow: There were 5 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:34:58] epinow: There were 2 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:15:14] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:34:58] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <p>We can visualise the estimates of the effective reproduction number
@@ -3884,10 +1227,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:22:06] epinow: There were 4 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:41:30] epinow: There were 10 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:22:06] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:41:30] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb8">
@@ -3901,11 +1244,11 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date   18000 (10430 -- 33164)
+1: New confirmed cases by infection date   17828 (10162 -- 32312)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.59 -- 1.4)
-4:                        Rate of growth -0.014 (-0.061 -- 0.043)
-5:          Doubling/halving time (days)          -48 (16 -- -11)</code></pre>
+3:            Effective reproduction no.       0.88 (0.57 -- 1.3)
+4:                        Rate of growth -0.015 (-0.064 -- 0.042)
+5:          Doubling/halving time (days)          -45 (17 -- -11)</code></pre>
 </div>
 <p>The estimates of transmission measures such as the effective
 reproduction number and rate of growth are similar (or the same in
@@ -4092,7 +1435,7 @@ <h3 class="callout-title">Challenge<a class="anchor" aria-label="anchor" href="#
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
 <div class="accordion-body">
 <p>Ensure the data is in the correct format :</p>
 <ul>
@@ -4111,7 +1454,7 @@ <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1">SOLUTION</h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
 <div class="accordion-body">
 <p>To estimate the effective reproduction number and growth rate, we
 will use the function <code>epinow()</code>.</p>
@@ -4167,10 +1510,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:24:26] epinow: There were 14 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:43:44] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:24:26] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:43:44] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb20">
@@ -4184,15 +1527,15 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                estimate
                                   &lt;char&gt;                  &lt;char&gt;
-1: New confirmed cases by infection date         103 (49 -- 263)
+1: New confirmed cases by infection date         101 (49 -- 262)
 2:        Expected change in daily cases              Increasing
-3:            Effective reproduction no.        1.7 (1.1 -- 3.1)
-4:                        Rate of growth 0.044 (0.0062 -- 0.095)
-5:          Doubling/halving time (days)         16 (7.3 -- 110)</code></pre>
+3:            Effective reproduction no.          1.7 (1.1 -- 3)
+4:                        Rate of growth 0.042 (0.0048 -- 0.092)
+5:          Doubling/halving time (days)         17 (7.5 -- 140)</code></pre>
 </div>
 <p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 1.7 (1.1 – 3.1). The exponential growth rate of case numbers is
-0.044 (0.0062 – 0.095).</p>
+data) is 1.7 (1.1 – 3). The exponential growth rate of case numbers is
+0.042 (0.0048 – 0.092).</p>
 <p>Visualize the estimates:</p>
 <div class="codewrapper sourceCode" id="cb22">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -4281,9 +1624,9 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/aio.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/aio.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/create-forecast.html b/instructor/create-forecast.html
index 9ac4942a..a0b7da58 100644
--- a/instructor/create-forecast.html
+++ b/instructor/create-forecast.html
@@ -92,11 +92,11 @@
   Real-time analysis and forecasting for outbreak analytics with R
 </div>
 
-<aside class="col-md-12 lesson-progress"><div style="width: 47%" class="percentage">
-    47%
+<aside class="col-md-12 lesson-progress"><div style="width: 33%" class="percentage">
+    33%
   </div>
   <div class="progress incubator">
-    <div class="progress-bar incubator" role="progressbar" style="width: 47%" aria-valuenow="47" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
+    <div class="progress-bar incubator" role="progressbar" style="width: 33%" aria-valuenow="33" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
     </div>
   </div>
 </aside><div class="container">
@@ -153,25 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush3">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -183,7 +165,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
       <button class="accordion-button" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapsecurrent" aria-expanded="true" aria-controls="flush-collapsecurrent">
         <span class="visually-hidden">Current Chapter</span>
         <span class="current-chapter">
-        4. Create a short-term forecast
+        2. Create a short-term forecast
         </span>
       </button>
     </div><!--/div.accordion-header-->
@@ -259,7 +241,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Create a short-term forecast</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/create-forecast.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -348,10 +330,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:15:14] epinow: There were 5 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:34:58] epinow: There were 2 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:15:14] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:34:58] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <p>We can visualise the estimates of the effective reproduction number
@@ -445,10 +427,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:22:06] epinow: There were 4 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:41:30] epinow: There were 10 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:22:06] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:41:30] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb8">
@@ -462,11 +444,11 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date   18000 (10430 -- 33164)
+1: New confirmed cases by infection date   17828 (10162 -- 32312)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.59 -- 1.4)
-4:                        Rate of growth -0.014 (-0.061 -- 0.043)
-5:          Doubling/halving time (days)          -48 (16 -- -11)</code></pre>
+3:            Effective reproduction no.       0.88 (0.57 -- 1.3)
+4:                        Rate of growth -0.015 (-0.064 -- 0.042)
+5:          Doubling/halving time (days)          -45 (17 -- -11)</code></pre>
 </div>
 <p>The estimates of transmission measures such as the effective
 reproduction number and rate of growth are similar (or the same in
@@ -643,7 +625,7 @@ <h3 class="callout-title">Challenge<a class="anchor" aria-label="anchor" href="#
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
 <div class="accordion-body">
 <p>Ensure the data is in the correct format :</p>
 <ul><li>
@@ -660,7 +642,7 @@ <h4 class="accordion-header" id="headingHint1">HINT : data format</h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1">SOLUTION</h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
 <div class="accordion-body">
 <p>To estimate the effective reproduction number and growth rate, we
 will use the function <code>epinow()</code>.</p>
@@ -716,10 +698,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 11:24:26] epinow: There were 14 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 14:43:44] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 11:24:26] epinow: Examine the pairs() plot to diagnose sampling problems
+WARN [2024-02-12 14:43:44] epinow: Examine the pairs() plot to diagnose sampling problems
  - </code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb20">
@@ -733,15 +715,15 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                estimate
                                   &lt;char&gt;                  &lt;char&gt;
-1: New confirmed cases by infection date         103 (49 -- 263)
+1: New confirmed cases by infection date         101 (49 -- 262)
 2:        Expected change in daily cases              Increasing
-3:            Effective reproduction no.        1.7 (1.1 -- 3.1)
-4:                        Rate of growth 0.044 (0.0062 -- 0.095)
-5:          Doubling/halving time (days)         16 (7.3 -- 110)</code></pre>
+3:            Effective reproduction no.          1.7 (1.1 -- 3)
+4:                        Rate of growth 0.042 (0.0048 -- 0.092)
+5:          Doubling/halving time (days)         17 (7.5 -- 140)</code></pre>
 </div>
 <p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 1.7 (1.1 – 3.1). The exponential growth rate of case numbers is
-0.044 (0.0062 – 0.095).</p>
+data) is 1.7 (1.1 – 3). The exponential growth rate of case numbers is
+0.042 (0.0048 – 0.092).</p>
 <p>Visualize the estimates:</p>
 <div class="codewrapper sourceCode" id="cb22">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -837,9 +819,9 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/create-forecast.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/create-forecast.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/images.html b/instructor/images.html
index 2768d7e1..e4bfd1cf 100644
--- a/instructor/images.html
+++ b/instructor/images.html
@@ -188,7 +188,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -200,31 +200,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -288,82 +264,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
     <main id="main-content" class="main-content"><div class="container lesson-content">
         
         
-<section id="introduction"><h2 class="section-heading"><a href="introduction.html">Outbreak analytics pipelines</a></h2>
-<hr class="half-width">
-<h3 id="introduction-figure-1">Figure 1</h3>
-<figure><img src="../fig/introduction-rendered-unnamed-chunk-5-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="introduction-figure-2">Figure 2</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="https://epiverse-trace.github.io/task_pipeline-minimal.svg" alt="" class="figure mx-auto d-block"><figcaption>The outbreak analytics pipeline.</figcaption></figure><hr>
-<h3 id="introduction-figure-3">Figure 3</h3>
-<figure><img src="../fig/pkgs-hexlogos.png" class="figure mx-auto d-block"></figure><hr></section><section id="read-delays"><h2 class="section-heading"><a href="read-delays.html">Read delays</a></h2>
-<hr class="half-width">
-<h3 id="read-delays-figure-1">Figure 1</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/time-periods.jpg" alt="" class="figure mx-auto d-block"><figcaption>Definition of key time periods. From <a href="https://www.sciencedirect.com/science/article/pii/S2468042721000038" class="external-link">Xiang
-et al, 2021</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-2">Figure 2</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/reproduction-generation-time.png" alt="" class="figure mx-auto d-block"><figcaption>Video from the MRC Centre for Global Infectious
-Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group
-with Dr Anne Cori (27-07-2023) at <a href="https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL" class="external-link uri">https://youtu.be/VvpYHhFDIjI?si=XiUyjmSV1gKNdrrL</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-3">Figure 3</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/serial-interval-observed.jpeg" alt="" class="figure mx-auto d-block"><figcaption>A schematic of the relationship of different time
-periods of transmission between an infector and an infectee in a
-transmission pair. Exposure window is defined as the time interval
-having viral exposure, and transmission window is defined as the time
-interval for onward transmission with respect to the infection time (<a href="https://academic.oup.com/jid/article/224/10/1664/6356465" class="external-link">Chung
-Lau et al. 2021</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-4">Figure 4</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/serial-interval-pairs.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial intervals of possible case pairs in (a)
-COVID-19 and (b) MERS-CoV. Pairs represent a presumed infector and their
-presumed infectee plotted by date of symptom onset (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-5">Figure 5</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/seria-interval-fitted-distributions.jpg" alt="" class="figure mx-auto d-block"><figcaption>Fitted serial interval distribution for (a) COVID-19
-and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia.
-We fitted three commonly used distributions, Lognormal, Gamma, and
-Weibull distributions, respectively (<a href="https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5" class="external-link">Althobaity
-et al., 2022</a>).</figcaption></figure><hr>
-<h3 id="read-delays-figure-6">Figure 6</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/serial-interval-covid-sars.jpg" alt="" class="figure mx-auto d-block"><figcaption>Serial interval of novel coronavirus (COVID-19)
-infections overlaid with a published distribution of SARS. (<a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-et al, 2020</a>)</figcaption></figure><hr>
-<h3 id="read-delays-figure-7">Figure 7</h3>
-<figure><img src="../fig/read-delays-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="read-delays-figure-8">Figure 8</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/fig5a-normaldistribution.png" alt="" class="figure mx-auto d-block"><figcaption>The four probability functions for the normal
-distribution (<a href="https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc" class="external-link">Jack
-Weiss, 2012</a>)</figcaption></figure><hr>
-<h3 id="read-delays-figure-9">Figure 9</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/disease-reporting.jpg" alt="" class="figure mx-auto d-block"><figcaption>Timeline for chain of disease reporting, the
-Netherlands. Lab, laboratory; PHA, public health authority. From <a href="https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article" class="external-link">Marinović
-et al., 2015</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-10">Figure 10</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/rt-adjusting-delays.png" alt="" class="figure mx-auto d-block"><figcaption>Rt is a measure of transmission at time t.
-Observations after time t must be adjusted. ICU, intensive care unit.
-From <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007" class="external-link">Gostic
-et al., 2020</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-11">Figure 11</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/incubation-period-serial-interval.jpg" alt="" class="figure mx-auto d-block"><figcaption>The relationship between the incubation period and
-serial interval. From <a href="https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext" class="external-link">Nishiura
-2020</a></figcaption></figure><hr>
-<h3 id="read-delays-figure-12">Figure 12</h3>
-<p aria-hidden="true">Image 1 of 1: ‘[decorative]’</p>
-<figure><img src="../fig/reproduction-number-pre-symptomatic.png" alt="" class="figure mx-auto d-block"><figcaption>Parameter estimates. Plausible ranges for the key
-parameters R0 and θ (read the main text for sources) for four viral
-infections of public concern are shown as shaded regions. The size of
-the shaded area reflects the uncertainties in the parameter estimates.
-<a href="https://www.pnas.org/doi/10.1073/pnas.0307506101" class="external-link">Fraser et
-al., 2004</a></figcaption></figure><hr></section><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
+<section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
 <hr class="half-width">
 <h3 id="quantify-transmissibility-figure-1">Figure 1</h3>
 <figure><img src="../fig/quantify-transmissibility-rendered-unnamed-chunk-4-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
@@ -428,9 +329,9 @@ <h3 id="create-forecast-figure-3">Figure 3</h3>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/images.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/images.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/index.html b/instructor/index.html
index 23733277..bc76a108 100644
--- a/instructor/index.html
+++ b/instructor/index.html
@@ -165,7 +165,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -174,25 +174,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -240,11 +222,11 @@ <h2 class="accordion-header" id="flush-headingTwelve">
         <!-- START: inst/pkgdown/templates/content-syllabus.html -->
   <div class="col-xl-8 col-lg-12 primary-content">
     <nav class="lesson-content mx-md-4" aria-label="Next Chapter"><!-- content for small screens --><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link float-end" href="../instructor/introduction.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="../instructor/quantify-transmissibility.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link float-end" href="../instructor/introduction.html" rel="next">Next: Outbreak analytics... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="../instructor/quantify-transmissibility.html" rel="next">Next: Quantifying... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <hr></nav><main id="main-content" class="main-content"><h1 class="schedule-heading">Summary and Schedule</h1>
       
@@ -306,28 +288,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
 <span class="visually-hidden">Duration: </span>00h 00m
 </td>
 <td>
- 1. <a href="introduction.html">Outbreak analytics pipelines</a>
-</td>
-<td>
-Why use R packages for Outbreak analytics?<br>What can we do to analyse
-our outbreak data?<br>How can I start doing Outbreak Analytics with R?
-</td>
-</tr><tr><td>
-<span class="visually-hidden">Duration: </span>00h 12m
-</td>
-<td>
- 2. <a href="read-delays.html">Read delays</a>
-</td>
-<td>
-How to get delay distributions from a systematic review?<br>How to
-connect reused delays with my existing analysis pipeline?<br>When
-should delays be reused from a systematic review?
-</td>
-</tr><tr><td>
-<span class="visually-hidden">Duration: </span>00h 24m
-</td>
-<td>
- 3. <a href="quantify-transmissibility.html">Quantifying
+ 1. <a href="quantify-transmissibility.html">Quantifying
 transmission</a>
 </td>
 <td>
@@ -336,17 +297,17 @@ <h2 class="accordion-header" id="flush-headingTwelve">
 metrics?
 </td>
 </tr><tr><td>
-<span class="visually-hidden">Duration: </span>00h 54m
+<span class="visually-hidden">Duration: </span>00h 30m
 </td>
 <td>
- 4. <a href="create-forecast.html">Create a short-term forecast</a>
+ 2. <a href="create-forecast.html">Create a short-term forecast</a>
 </td>
 <td>
 How do I create short-term forecasts from case data?<br>How do I
 account for incomplete reporting in forecasts?
 </td>
 </tr><tr><td>
-<span class="visually-hidden">Duration: </span>01h 54m
+<span class="visually-hidden">Duration: </span>01h 30m
 </td>
 <td>
 Finish
@@ -486,11 +447,11 @@ <h3 id="download-the-data">Download the data<a class="anchor" aria-label="anchor
 any links that you are not going to use. 
  -->
 </section></section></main><nav class="bottom-pagination mx-md-4" aria-label="Next Chapter"><!-- content for small screens --><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link float-end" href="../instructor/introduction.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="../instructor/quantify-transmissibility.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link float-end" href="../instructor/introduction.html" rel="next">Next: Outbreak analytics... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
+        <a class="chapter-link float-end" href="../instructor/quantify-transmissibility.html" rel="next">Next: Quantifying... <i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
     </nav></div>
 <!-- END  : inst/pkgdown/templates/content-syllabus.html -->
@@ -533,9 +494,9 @@ <h3 id="download-the-data">Download the data<a class="anchor" aria-label="anchor
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/index.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/index.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/instructor-notes.html b/instructor/instructor-notes.html
index bbe353da..70e22da6 100644
--- a/instructor/instructor-notes.html
+++ b/instructor/instructor-notes.html
@@ -188,7 +188,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -200,31 +200,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -296,29 +272,7 @@ <h1>Instructor Notes</h1>
  -->
 
 
-      <section id="aggregate-instructor-notes"><section id="introduction"><h2 class="section-heading"><a href="introduction.html">Outbreak analytics pipelines</a></h2>
-<hr class="half-width"></section><section id="read-delays"><h2 class="section-heading"><a href="read-delays.html">Read delays</a></h2>
-<hr class="half-width">
-<h3 id="read-delays-headingInstructor1">
-<a href="read-delays.html#headingInstructor1">Instructor Note</a><a class="anchor" aria-label="anchor" href="#read-delays-headingInstructor1"></a>
-</h3>
-<p>The objective of the assessment above is to assess the interpretation
-of a larger or shorter generation time.</p>
-<hr>
-<br><h3 id="read-delays-headingInstructor2">
-<a href="read-delays.html#headingInstructor2">Instructor Note</a><a class="anchor" aria-label="anchor" href="#read-delays-headingInstructor2"></a>
-</h3>
-<p>Access to the reference documentation (Help files) for these
-functions is accessible with the three double-colon notation:
-<code>epiparameter:::</code></p>
-<ul>
-<li><code>?epiparameter:::density.epidist()</code></li>
-<li><code>?epiparameter:::cdf.epidist()</code></li>
-<li><code>?epiparameter:::quantile.epidist()</code></li>
-<li><code>?epiparameter:::generate.epidist()</code></li>
-</ul>
-<hr>
-<br></section><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
+      <section id="aggregate-instructor-notes"><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
 <hr class="half-width">
 <h3 id="quantify-transmissibility-headingInstructor1">
 <a href="quantify-transmissibility.html#headingInstructor1">Instructor Note</a><a class="anchor" aria-label="anchor" href="#quantify-transmissibility-headingInstructor1"></a>
@@ -376,9 +330,9 @@ <h3 id="quantify-transmissibility-headingInstructor1">
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/instructor-notes.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/instructor-notes.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/key-points.html b/instructor/key-points.html
index b8f582f5..4bf5124b 100644
--- a/instructor/key-points.html
+++ b/instructor/key-points.html
@@ -188,7 +188,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -200,31 +200,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div>
-<!--/div.accordion-header-->
-        
-  </div>
-<!--/div.accordion-item-->
-</div>
-<!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -288,29 +264,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
     <main id="main-content" class="main-content"><div class="container lesson-content">
         
         
-<section id="introduction"><h2 class="section-heading"><a href="introduction.html">Outbreak analytics pipelines</a></h2>
-<hr class="half-width">
-<ul>
-<li>Our vision is to have pipelines of R packages for outbreak
-analytics.</li>
-<li>Our strategy is to create interconnected tasks to get relevant
-outputs for public health questions.</li>
-<li>We plan to introduce package solutions and theory bits for each of
-the tasks in the outbreak analytics pipeline.</li>
-</ul></section><section id="read-delays"><h2 class="section-heading"><a href="read-delays.html">Read delays</a></h2>
-<hr class="half-width">
-<ul>
-<li>Use <code>{epiparameter}</code> to access the systematic review
-catalogue of epidemiological delay distributions.</li>
-<li>Use <code>epidist_db()</code> to select single delay
-distributions.</li>
-<li>Use <code>list_distributions()</code> for an overview of multiple
-delay distributions.</li>
-<li>Use <code>discretise()</code> to convert continuous to discrete
-delay distributions.</li>
-<li>Use <code>{epiparameter}</code> probability functions for any delay
-distributions.</li>
-</ul></section><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
+<section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
 <hr class="half-width">
 <ul>
 <li>Transmission metrics can be estimated from case data after
@@ -369,9 +323,9 @@ <h2 class="accordion-header" id="flush-headingTwelve">
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/key-points.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/key-points.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/profiles.html b/instructor/profiles.html
index b30831f9..6fe32d90 100644
--- a/instructor/profiles.html
+++ b/instructor/profiles.html
@@ -153,7 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -162,25 +162,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -288,9 +270,9 @@ <h1>Learner Profiles</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/instructor/profiles.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/instructor/profiles.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/instructor/quantify-transmissibility.html b/instructor/quantify-transmissibility.html
index 81ee7244..5af5f7a3 100644
--- a/instructor/quantify-transmissibility.html
+++ b/instructor/quantify-transmissibility.html
@@ -92,11 +92,11 @@
   Real-time analysis and forecasting for outbreak analytics with R
 </div>
 
-<aside class="col-md-12 lesson-progress"><div style="width: 21%" class="percentage">
-    21%
+<aside class="col-md-12 lesson-progress"><div style="width: 0%" class="percentage">
+    0%
   </div>
   <div class="progress incubator">
-    <div class="progress-bar incubator" role="progressbar" style="width: 21%" aria-valuenow="21" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
+    <div class="progress-bar incubator" role="progressbar" style="width: 0%" aria-valuenow="0" aria-label="Lesson Progress" aria-valuemin="0" aria-valuemax="100">
     </div>
   </div>
 </aside><div class="container">
@@ -150,31 +150,13 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
   </div><!--/div.accordion-item-->
 </div><!--/div.accordion-flush-->
 
-<div class="accordion accordion-flush" id="accordionFlush2">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush3">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
 <div class="accordion accordion-flush" id="accordionFlushcurrent">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-headingcurrent">
       <button class="accordion-button" type="button" data-bs-toggle="collapse" data-bs-target="#flush-collapsecurrent" aria-expanded="true" aria-controls="flush-collapsecurrent">
         <span class="visually-hidden">Current Chapter</span>
         <span class="current-chapter">
-        3. Quantifying transmission
+        1. Quantifying transmission
         </span>
       </button>
     </div><!--/div.accordion-header-->
@@ -192,10 +174,10 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
   </div><!--/div.accordion-item-->
 </div><!--/div.accordion-flush-->
 
-<div class="accordion accordion-flush" id="accordionFlush5">
+<div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+    <div class="accordion-header" id="flush-heading3">
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -243,14 +225,14 @@ <h2 class="accordion-header" id="flush-headingTwelve">
         <!-- START: inst/pkgdown/templates/content-instructor.html -->
   <div class="col-xl-8 col-lg-12 primary-content">
     <nav class="lesson-content mx-md-4" aria-label="Previous and Next Chapter"><!-- content for small screens --><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link" href="../instructor/read-delays.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
+        <a class="chapter-link" href="../instructor/index.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
         <a class="chapter-link float-end" href="../instructor/create-forecast.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link" href="../instructor/read-delays.html" rel="prev">
+        <a class="chapter-link" href="../instructor/index.html" rel="prev">
           <i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>
-          Previous: Read delays
+          Home
         </a>
         <a class="chapter-link float-end" href="../instructor/create-forecast.html" rel="next">
           Next: Create a short-term... 
@@ -259,7 +241,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Quantifying transmission</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/quantify-transmissibility.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -387,7 +369,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
   <h3 class="accordion-header" id="headingInstructor1">
 <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="edit-2"></i></div>Instructor Note</h3>
 </button>
-<div id="collapseInstructor1" class="accordion-collapse collapse" data-bs-parent="#accordionInstructor1" aria-labelledby="headingInstructor1">
+<div id="collapseInstructor1" class="accordion-collapse collapse" aria-labelledby="headingInstructor1" data-bs-parent="#accordionInstructor1">
 <div class="accordion-body">
 <p>This tutorial illustrates the usage of <code>epinow()</code> to
 estimate the time-varying reproduction number and infection times.
@@ -728,11 +710,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:24:53] epinow: There were 12 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 13:39:13] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 10:24:53] epinow: Examine the pairs() plot to diagnose sampling problems
- - </code></pre>
+WARN [2024-02-12 13:39:13] epinow: Examine the pairs() plot to diagnose sampling problems
+ - 
+WARN [2024-02-12 13:39:15] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
+Running the chains for more iterations may help. See
+https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
 </div>
 <div class="section level3">
 <h3 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a></h3>
@@ -773,23 +758,23 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date     7232 (4056 -- 12590)
+1: New confirmed cases by infection date     7340 (4136 -- 12899)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.57 -- 1.3)
-4:                        Rate of growth -0.015 (-0.064 -- 0.039)
-5:          Doubling/halving time (days)          -47 (18 -- -11)</code></pre>
+3:            Effective reproduction no.        0.9 (0.58 -- 1.4)
+4:                        Rate of growth -0.013 (-0.062 -- 0.042)
+5:          Doubling/halving time (days)          -54 (17 -- -11)</code></pre>
 </div>
 <p>As these estimates are based on partial data, they have a wide
 uncertainty interval.</p>
 <ul><li><p>From the summary of our analysis we see that the expected change
 in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4056 – 12590).</p></li>
+cases 7340 (4136 – 12899).</p></li>
 <li><p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 0.89 (0.57 – 1.3).</p></li>
-<li><p>The exponential growth rate of case numbers is -0.015 (-0.064 –
-0.039).</p></li>
+data) is 0.9 (0.58 – 1.4).</p></li>
+<li><p>The exponential growth rate of case numbers is -0.013 (-0.062 –
+0.042).</p></li>
 <li><p>The doubling time (the time taken for case numbers to double) is
--47 (18 – -11).</p></li>
+-54 (17 – -11).</p></li>
 </ul><div id="expected-change-in-daily-cases" class="callout callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -876,17 +861,17 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>INFO [2024-02-08 10:24:58] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-02-08 10:24:58] Reporting estimates using data up to: 2020-04-28
-INFO [2024-02-08 10:24:58] No target directory specified so returning output
-INFO [2024-02-08 10:24:58] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-02-08 10:24:58] Regions excluded: none
-INFO [2024-02-08 11:08:54] Completed regional estimates
-INFO [2024-02-08 11:08:54] Regions with estimates: 13
-INFO [2024-02-08 11:08:54] Regions with runtime errors: 0
-INFO [2024-02-08 11:08:54] Producing summary
-INFO [2024-02-08 11:08:54] No summary directory specified so returning summary output
-INFO [2024-02-08 11:08:54] No target directory specified so returning timings</code></pre>
+<pre class="output" tabindex="0"><code>INFO [2024-02-12 13:39:18] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-02-12 13:39:18] Reporting estimates using data up to: 2020-04-28
+INFO [2024-02-12 13:39:18] No target directory specified so returning output
+INFO [2024-02-12 13:39:18] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-02-12 13:39:18] Regions excluded: none
+INFO [2024-02-12 14:27:58] Completed regional estimates
+INFO [2024-02-12 14:27:58] Regions with estimates: 13
+INFO [2024-02-12 14:27:58] Regions with runtime errors: 0
+INFO [2024-02-12 14:27:58] Producing summary
+INFO [2024-02-12 14:27:58] No summary directory specified so returning summary output
+INFO [2024-02-12 14:27:58] No target directory specified so returning timings</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -899,49 +884,49 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                      Region New confirmed cases by infection date
                       &lt;char&gt;                                &lt;char&gt;
- 1:            East Midlands                      340 (211 -- 551)
- 2:          East of England                      533 (332 -- 847)
- 3:                  England                   3548 (2264 -- 5694)
- 4:                   London                      292 (193 -- 460)
- 5:               North East                      251 (141 -- 430)
- 6:               North West                      566 (327 -- 870)
- 7:         Northern Ireland                         45 (23 -- 86)
- 8:                 Scotland                      289 (160 -- 522)
- 9:               South East                      599 (360 -- 996)
-10:               South West                      423 (294 -- 623)
-11:                    Wales                        95 (64 -- 136)
-12:            West Midlands                      270 (141 -- 488)
-13: Yorkshire and The Humber                      478 (284 -- 790)
+ 1:            East Midlands                      345 (208 -- 547)
+ 2:          East of England                      535 (324 -- 843)
+ 3:                  England                   3570 (2204 -- 5553)
+ 4:                   London                      298 (193 -- 464)
+ 5:               North East                      258 (149 -- 425)
+ 6:               North West                      554 (319 -- 885)
+ 7:         Northern Ireland                         43 (23 -- 85)
+ 8:                 Scotland                      286 (158 -- 544)
+ 9:               South East                      582 (357 -- 962)
+10:               South West                      417 (294 -- 608)
+11:                    Wales                        95 (65 -- 137)
+12:            West Midlands                      268 (141 -- 494)
+13: Yorkshire and The Humber                      472 (283 -- 798)
     Expected change in daily cases Effective reproduction no.
                             &lt;fctr&gt;                     &lt;char&gt;
- 1:              Likely increasing          1.2 (0.85 -- 1.6)
- 2:              Likely increasing          1.2 (0.84 -- 1.6)
- 3:              Likely decreasing         0.91 (0.65 -- 1.3)
- 4:              Likely decreasing         0.78 (0.56 -- 1.1)
- 5:              Likely decreasing         0.91 (0.59 -- 1.3)
- 6:              Likely decreasing         0.87 (0.57 -- 1.2)
- 7:              Likely decreasing         0.65 (0.39 -- 1.1)
- 8:              Likely decreasing          0.92 (0.6 -- 1.4)
- 9:                         Stable         0.99 (0.68 -- 1.4)
+ 1:              Likely increasing          1.2 (0.84 -- 1.6)
+ 2:              Likely increasing          1.2 (0.82 -- 1.6)
+ 3:              Likely decreasing         0.92 (0.64 -- 1.3)
+ 4:              Likely decreasing         0.79 (0.56 -- 1.1)
+ 5:              Likely decreasing         0.92 (0.61 -- 1.3)
+ 6:              Likely decreasing         0.86 (0.57 -- 1.2)
+ 7:              Likely decreasing         0.64 (0.39 -- 1.1)
+ 8:              Likely decreasing         0.91 (0.58 -- 1.4)
+ 9:                         Stable         0.98 (0.68 -- 1.4)
 10:                     Increasing           1.4 (1.1 -- 1.8)
-11:                     Decreasing        0.57 (0.41 -- 0.75)
+11:                     Decreasing        0.57 (0.42 -- 0.75)
 12:              Likely decreasing          0.7 (0.42 -- 1.1)
 13:                         Stable            1 (0.69 -- 1.4)
                Rate of growth Doubling/halving time (days)
                        &lt;char&gt;                       &lt;char&gt;
- 1:    0.022 (-0.02 -- 0.069)               31 (10 -- -34)
- 2:   0.022 (-0.022 -- 0.066)               32 (11 -- -31)
- 3:  -0.011 (-0.051 -- 0.033)              -61 (21 -- -14)
- 4:   -0.03 (-0.066 -- 0.011)              -23 (61 -- -11)
- 5:   -0.012 (-0.06 -- 0.038)              -58 (18 -- -11)
- 6:  -0.017 (-0.063 -- 0.024)              -40 (29 -- -11)
- 7:  -0.05 (-0.098 -- 0.0087)               -14 (80 -- -7)
- 8:  -0.011 (-0.059 -- 0.043)              -62 (16 -- -12)
- 9: -0.00073 (-0.046 -- 0.05)             -940 (14 -- -15)
-10:    0.048 (0.012 -- 0.087)                 14 (8 -- 57)
-11: -0.064 (-0.094 -- -0.034)            -11 (-20 -- -7.3)
-12:  -0.042 (-0.092 -- 0.012)             -17 (57 -- -7.5)
-13:  0.0026 (-0.044 -- 0.052)              260 (13 -- -16)</code></pre>
+ 1:   0.024 (-0.022 -- 0.069)               29 (10 -- -31)
+ 2:   0.021 (-0.025 -- 0.065)               32 (11 -- -28)
+ 3:  -0.011 (-0.052 -- 0.031)              -63 (22 -- -13)
+ 4:  -0.029 (-0.066 -- 0.013)              -24 (55 -- -11)
+ 5:  -0.011 (-0.057 -- 0.038)              -63 (18 -- -12)
+ 6:  -0.019 (-0.065 -- 0.023)              -37 (31 -- -11)
+ 7: -0.052 (-0.099 -- 0.0082)               -13 (85 -- -7)
+ 8:   -0.012 (-0.062 -- 0.05)              -60 (14 -- -11)
+ 9: -0.0029 (-0.046 -- 0.046)             -240 (15 -- -15)
+10:    0.046 (0.013 -- 0.084)               15 (8.2 -- 54)
+11: -0.065 (-0.093 -- -0.035)            -11 (-20 -- -7.4)
+12:  -0.043 (-0.092 -- 0.015)             -16 (48 -- -7.5)
+13:  0.0019 (-0.045 -- 0.051)              370 (14 -- -15)</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
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+        <a class="chapter-link" href="../instructor/index.html" rel="prev">
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-          Previous: Read delays
+          Home
         </a>
         <a class="chapter-link float-end" href="../instructor/create-forecast.html" rel="next">
           Next: Create a short-term... 
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+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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         <h1>Glossary of Terms: Epiverse-TRACE</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
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diff --git a/key-points.html b/key-points.html
index d655f158..e6532fe4 100644
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-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
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-  </div>
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-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div>
 <!--/div.accordion-header-->
         
@@ -286,29 +262,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
     <main id="main-content" class="main-content"><div class="container lesson-content">
         
         
-<section id="introduction"><h2 class="section-heading"><a href="introduction.html">Outbreak analytics pipelines</a></h2>
-<hr class="half-width">
-<ul>
-<li>Our vision is to have pipelines of R packages for outbreak
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-<li>Our strategy is to create interconnected tasks to get relevant
-outputs for public health questions.</li>
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-</ul></section><section id="read-delays"><h2 class="section-heading"><a href="read-delays.html">Read delays</a></h2>
-<hr class="half-width">
-<ul>
-<li>Use <code>{epiparameter}</code> to access the systematic review
-catalogue of epidemiological delay distributions.</li>
-<li>Use <code>epidist_db()</code> to select single delay
-distributions.</li>
-<li>Use <code>list_distributions()</code> for an overview of multiple
-delay distributions.</li>
-<li>Use <code>discretise()</code> to convert continuous to discrete
-delay distributions.</li>
-<li>Use <code>{epiparameter}</code> probability functions for any delay
-distributions.</li>
-</ul></section><section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
+<section id="quantify-transmissibility"><h2 class="section-heading"><a href="quantify-transmissibility.html">Quantifying transmission</a></h2>
 <hr class="half-width">
 <ul>
 <li>Transmission metrics can be estimated from case data after
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diff --git a/pkgdown.yml b/pkgdown.yml
index cc7fae1d..bde02ce2 100644
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+last_built: 2024-02-12T14:43Z
 
diff --git a/profiles.html b/profiles.html
index fa250964..28fc42b7 100644
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+++ b/profiles.html
@@ -153,7 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
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@@ -162,25 +162,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
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-        <a href="read-delays.html">2. Read delays</a>
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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diff --git a/quantify-transmissibility.html b/quantify-transmissibility.html
index 2cd2f450..1e096da2 100644
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+++ b/quantify-transmissibility.html
@@ -92,11 +92,11 @@
   Real-time analysis and forecasting for outbreak analytics with R
 </div>
 
-<aside class="col-md-12 lesson-progress"><div style="width: 21%" class="percentage">
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-  </div><!--/div.accordion-item-->
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         <span class="visually-hidden">Current Chapter</span>
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-        3. Quantifying transmission
+        1. Quantifying transmission
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-<div class="accordion accordion-flush" id="accordionFlush5">
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-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+    <div class="accordion-header" id="flush-heading3">
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
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-        <a class="chapter-link" href="read-delays.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
+        <a class="chapter-link" href="index.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
         <a class="chapter-link float-end" href="create-forecast.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link" href="read-delays.html" rel="prev">
+        <a class="chapter-link" href="index.html" rel="prev">
           <i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>
-          Previous: Read delays
+          Home
         </a>
         <a class="chapter-link float-end" href="create-forecast.html" rel="next">
           Next: Create a short-term... 
@@ -259,7 +241,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Quantifying transmission</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/episodes/quantify-transmissibility.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -711,11 +693,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>WARN [2024-02-08 10:24:53] epinow: There were 12 divergent transitions after warmup. See
+<pre class="output" tabindex="0"><code>WARN [2024-02-12 13:39:13] epinow: There were 6 divergent transitions after warmup. See
 https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 to find out why this is a problem and how to eliminate them. - 
-WARN [2024-02-08 10:24:53] epinow: Examine the pairs() plot to diagnose sampling problems
- - </code></pre>
+WARN [2024-02-12 13:39:13] epinow: Examine the pairs() plot to diagnose sampling problems
+ - 
+WARN [2024-02-12 13:39:15] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
+Running the chains for more iterations may help. See
+https://mc-stan.org/misc/warnings.html#bulk-ess - </code></pre>
 </div>
 <div class="section level3">
 <h3 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a></h3>
@@ -756,23 +741,23 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                                 measure                 estimate
                                   &lt;char&gt;                   &lt;char&gt;
-1: New confirmed cases by infection date     7232 (4056 -- 12590)
+1: New confirmed cases by infection date     7340 (4136 -- 12899)
 2:        Expected change in daily cases        Likely decreasing
-3:            Effective reproduction no.       0.89 (0.57 -- 1.3)
-4:                        Rate of growth -0.015 (-0.064 -- 0.039)
-5:          Doubling/halving time (days)          -47 (18 -- -11)</code></pre>
+3:            Effective reproduction no.        0.9 (0.58 -- 1.4)
+4:                        Rate of growth -0.013 (-0.062 -- 0.042)
+5:          Doubling/halving time (days)          -54 (17 -- -11)</code></pre>
 </div>
 <p>As these estimates are based on partial data, they have a wide
 uncertainty interval.</p>
 <ul><li><p>From the summary of our analysis we see that the expected change
 in daily cases is Likely decreasing with the estimated new confirmed
-cases 7232 (4056 – 12590).</p></li>
+cases 7340 (4136 – 12899).</p></li>
 <li><p>The effective reproduction number <span class="math inline">\(R_t\)</span> estimate (on the last date of the
-data) is 0.89 (0.57 – 1.3).</p></li>
-<li><p>The exponential growth rate of case numbers is -0.015 (-0.064 –
-0.039).</p></li>
+data) is 0.9 (0.58 – 1.4).</p></li>
+<li><p>The exponential growth rate of case numbers is -0.013 (-0.062 –
+0.042).</p></li>
 <li><p>The doubling time (the time taken for case numbers to double) is
--47 (18 – -11).</p></li>
+-54 (17 – -11).</p></li>
 </ul><div id="expected-change-in-daily-cases" class="callout callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -859,17 +844,17 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="codewrapper">
 <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>INFO [2024-02-08 10:24:58] Producing following optional outputs: regions, summary, samples, plots, latest
-INFO [2024-02-08 10:24:58] Reporting estimates using data up to: 2020-04-28
-INFO [2024-02-08 10:24:58] No target directory specified so returning output
-INFO [2024-02-08 10:24:58] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
-INFO [2024-02-08 10:24:58] Regions excluded: none
-INFO [2024-02-08 11:08:54] Completed regional estimates
-INFO [2024-02-08 11:08:54] Regions with estimates: 13
-INFO [2024-02-08 11:08:54] Regions with runtime errors: 0
-INFO [2024-02-08 11:08:54] Producing summary
-INFO [2024-02-08 11:08:54] No summary directory specified so returning summary output
-INFO [2024-02-08 11:08:54] No target directory specified so returning timings</code></pre>
+<pre class="output" tabindex="0"><code>INFO [2024-02-12 13:39:18] Producing following optional outputs: regions, summary, samples, plots, latest
+INFO [2024-02-12 13:39:18] Reporting estimates using data up to: 2020-04-28
+INFO [2024-02-12 13:39:18] No target directory specified so returning output
+INFO [2024-02-12 13:39:18] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber
+INFO [2024-02-12 13:39:18] Regions excluded: none
+INFO [2024-02-12 14:27:58] Completed regional estimates
+INFO [2024-02-12 14:27:58] Regions with estimates: 13
+INFO [2024-02-12 14:27:58] Regions with runtime errors: 0
+INFO [2024-02-12 14:27:58] Producing summary
+INFO [2024-02-12 14:27:58] No summary directory specified so returning summary output
+INFO [2024-02-12 14:27:58] No target directory specified so returning timings</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -882,49 +867,49 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 </h3>
 <pre class="output" tabindex="0"><code>                      Region New confirmed cases by infection date
                       &lt;char&gt;                                &lt;char&gt;
- 1:            East Midlands                      340 (211 -- 551)
- 2:          East of England                      533 (332 -- 847)
- 3:                  England                   3548 (2264 -- 5694)
- 4:                   London                      292 (193 -- 460)
- 5:               North East                      251 (141 -- 430)
- 6:               North West                      566 (327 -- 870)
- 7:         Northern Ireland                         45 (23 -- 86)
- 8:                 Scotland                      289 (160 -- 522)
- 9:               South East                      599 (360 -- 996)
-10:               South West                      423 (294 -- 623)
-11:                    Wales                        95 (64 -- 136)
-12:            West Midlands                      270 (141 -- 488)
-13: Yorkshire and The Humber                      478 (284 -- 790)
+ 1:            East Midlands                      345 (208 -- 547)
+ 2:          East of England                      535 (324 -- 843)
+ 3:                  England                   3570 (2204 -- 5553)
+ 4:                   London                      298 (193 -- 464)
+ 5:               North East                      258 (149 -- 425)
+ 6:               North West                      554 (319 -- 885)
+ 7:         Northern Ireland                         43 (23 -- 85)
+ 8:                 Scotland                      286 (158 -- 544)
+ 9:               South East                      582 (357 -- 962)
+10:               South West                      417 (294 -- 608)
+11:                    Wales                        95 (65 -- 137)
+12:            West Midlands                      268 (141 -- 494)
+13: Yorkshire and The Humber                      472 (283 -- 798)
     Expected change in daily cases Effective reproduction no.
                             &lt;fctr&gt;                     &lt;char&gt;
- 1:              Likely increasing          1.2 (0.85 -- 1.6)
- 2:              Likely increasing          1.2 (0.84 -- 1.6)
- 3:              Likely decreasing         0.91 (0.65 -- 1.3)
- 4:              Likely decreasing         0.78 (0.56 -- 1.1)
- 5:              Likely decreasing         0.91 (0.59 -- 1.3)
- 6:              Likely decreasing         0.87 (0.57 -- 1.2)
- 7:              Likely decreasing         0.65 (0.39 -- 1.1)
- 8:              Likely decreasing          0.92 (0.6 -- 1.4)
- 9:                         Stable         0.99 (0.68 -- 1.4)
+ 1:              Likely increasing          1.2 (0.84 -- 1.6)
+ 2:              Likely increasing          1.2 (0.82 -- 1.6)
+ 3:              Likely decreasing         0.92 (0.64 -- 1.3)
+ 4:              Likely decreasing         0.79 (0.56 -- 1.1)
+ 5:              Likely decreasing         0.92 (0.61 -- 1.3)
+ 6:              Likely decreasing         0.86 (0.57 -- 1.2)
+ 7:              Likely decreasing         0.64 (0.39 -- 1.1)
+ 8:              Likely decreasing         0.91 (0.58 -- 1.4)
+ 9:                         Stable         0.98 (0.68 -- 1.4)
 10:                     Increasing           1.4 (1.1 -- 1.8)
-11:                     Decreasing        0.57 (0.41 -- 0.75)
+11:                     Decreasing        0.57 (0.42 -- 0.75)
 12:              Likely decreasing          0.7 (0.42 -- 1.1)
 13:                         Stable            1 (0.69 -- 1.4)
                Rate of growth Doubling/halving time (days)
                        &lt;char&gt;                       &lt;char&gt;
- 1:    0.022 (-0.02 -- 0.069)               31 (10 -- -34)
- 2:   0.022 (-0.022 -- 0.066)               32 (11 -- -31)
- 3:  -0.011 (-0.051 -- 0.033)              -61 (21 -- -14)
- 4:   -0.03 (-0.066 -- 0.011)              -23 (61 -- -11)
- 5:   -0.012 (-0.06 -- 0.038)              -58 (18 -- -11)
- 6:  -0.017 (-0.063 -- 0.024)              -40 (29 -- -11)
- 7:  -0.05 (-0.098 -- 0.0087)               -14 (80 -- -7)
- 8:  -0.011 (-0.059 -- 0.043)              -62 (16 -- -12)
- 9: -0.00073 (-0.046 -- 0.05)             -940 (14 -- -15)
-10:    0.048 (0.012 -- 0.087)                 14 (8 -- 57)
-11: -0.064 (-0.094 -- -0.034)            -11 (-20 -- -7.3)
-12:  -0.042 (-0.092 -- 0.012)             -17 (57 -- -7.5)
-13:  0.0026 (-0.044 -- 0.052)              260 (13 -- -16)</code></pre>
+ 1:   0.024 (-0.022 -- 0.069)               29 (10 -- -31)
+ 2:   0.021 (-0.025 -- 0.065)               32 (11 -- -28)
+ 3:  -0.011 (-0.052 -- 0.031)              -63 (22 -- -13)
+ 4:  -0.029 (-0.066 -- 0.013)              -24 (55 -- -11)
+ 5:  -0.011 (-0.057 -- 0.038)              -63 (18 -- -12)
+ 6:  -0.019 (-0.065 -- 0.023)              -37 (31 -- -11)
+ 7: -0.052 (-0.099 -- 0.0082)               -13 (85 -- -7)
+ 8:   -0.012 (-0.062 -- 0.05)              -60 (14 -- -11)
+ 9: -0.0029 (-0.046 -- 0.046)             -240 (15 -- -15)
+10:    0.046 (0.013 -- 0.084)               15 (8.2 -- 54)
+11: -0.065 (-0.093 -- -0.035)            -11 (-20 -- -7.4)
+12:  -0.043 (-0.092 -- 0.015)             -16 (48 -- -7.5)
+13:  0.0019 (-0.045 -- 0.051)              370 (14 -- -15)</code></pre>
 </div>
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -960,14 +945,14 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
  -->
 </section></div> <!-- / div.lesson-content -->
     </main><!-- / main#main-content.main-content --><nav class="bottom-pagination mx-md-4" aria-label="Previous and Next Chapter"><div class="d-block d-sm-block d-md-none">
-        <a class="chapter-link" href="read-delays.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
+        <a class="chapter-link" href="index.html"><i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>Previous</a>
         <a class="chapter-link float-end" href="create-forecast.html">Next<i aria-hidden="true" class="small-arrow" data-feather="arrow-right"></i></a>
       </div>
       <!-- content for large screens -->
       <div class="d-none d-sm-none d-md-block">
-        <a class="chapter-link" href="read-delays.html" rel="prev">
+        <a class="chapter-link" href="index.html" rel="prev">
           <i aria-hidden="true" class="small-arrow" data-feather="arrow-left"></i>
-          Previous: Read delays
+          Home
         </a>
         <a class="chapter-link float-end" href="create-forecast.html" rel="next">
           Next: Create a short-term... 
@@ -1015,9 +1000,9 @@ <h3 class="callout-title">Key Points<a class="anchor" aria-label="anchor" href="
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/quantify-transmissibility.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/reference.html b/reference.html
index be3711d2..3710a66a 100644
--- a/reference.html
+++ b/reference.html
@@ -153,7 +153,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush2">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading2">
-        <a href="introduction.html">1. Outbreak analytics pipelines</a>
+        <a href="quantify-transmissibility.html">1. Quantifying transmission</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -162,25 +162,7 @@ <h2 class="accordion-header chapters" id="flush-headingEleven">
 <div class="accordion accordion-flush" id="accordionFlush3">
   <div class="accordion-item">
     <div class="accordion-header" id="flush-heading3">
-        <a href="read-delays.html">2. Read delays</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush4">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading4">
-        <a href="quantify-transmissibility.html">3. Quantifying transmission</a>
-    </div><!--/div.accordion-header-->
-        
-  </div><!--/div.accordion-item-->
-</div><!--/div.accordion-flush-->
-
-<div class="accordion accordion-flush" id="accordionFlush5">
-  <div class="accordion-item">
-    <div class="accordion-header" id="flush-heading5">
-        <a href="create-forecast.html">4. Create a short-term forecast</a>
+        <a href="create-forecast.html">2. Create a short-term forecast</a>
     </div><!--/div.accordion-header-->
         
   </div><!--/div.accordion-item-->
@@ -244,7 +226,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Glossary of Terms: Epiverse-TRACE</h1>
-        <p>Last updated on 2024-02-08 |
+        <p>Last updated on 2024-02-12 |
         
         <a href="https://github.com/epiverse-trace/tutorials/edit/main/learners/reference.md" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
         
@@ -578,9 +560,9 @@ <h1>Glossary of Terms: Epiverse-TRACE</h1>
   "creativeWorkStatus": "active",
   "url": "https://epiverse-trace.github.io/tutorials/reference.html",
   "identifier": "https://epiverse-trace.github.io/tutorials/reference.html",
-  "dateCreated": "2024-02-08",
-  "dateModified": "2024-02-08",
-  "datePublished": "2024-02-08"
+  "dateCreated": "2024-02-12",
+  "dateModified": "2024-02-12",
+  "datePublished": "2024-02-12"
 }
 
   </script><script>
diff --git a/sitemap.xml b/sitemap.xml
index 368c35f9..ce233005 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -9,9 +9,15 @@
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/LICENSE.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/aio.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/create-forecast.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/images.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/index.html</loc>
   </url>
@@ -24,15 +30,27 @@
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/LICENSE.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/instructor/aio.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/create-forecast.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/instructor/images.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/index.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/instructor/instructor-notes.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/introduction.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/instructor/key-points.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/profiles.html</loc>
   </url>
@@ -45,9 +63,15 @@
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/instructor/reference.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/instructor-notes.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/introduction.html</loc>
   </url>
+  <url>
+    <loc>https://epiverse-trace.github.io/tutorials/key-points.html</loc>
+  </url>
   <url>
     <loc>https://epiverse-trace.github.io/tutorials/profiles.html</loc>
   </url>