Update bednet and lsm

dd-harp · Feb 1, 2024 · d9d474b · d9d474b
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diff --git a/R/bednet-interface.R b/R/bednet-interface.R
@@ -47,6 +47,7 @@ BedNetEffects <- function(t, pars, s) {
 #' @description This method dispatches on the type of `pars$ITNefsz`.
 #' @param t current simulation time
 #' @param pars a [list]
+#' @param s the vector species index
 #' @return a [list]
 #' @export
 BedNetEffectSizes <- function(t, pars, s) {

diff --git a/R/bednet-lemenach.R b/R/bednet-lemenach.R
@@ -47,7 +47,7 @@ BedNetEffectSizes.lemenach <- function(t, pars, s){
 #' in host seeking/bloodfeeding and resting/oviposition
 #' @param r probability of mosquito being repelled upon contact with ITN
 #' @param s probability of mosquito successfully feeding upon contact with ITN
-#' @param phi a [function] that takes a single argument `t` and returns the level of ITN coverage at that time
+#' @param F_phi a [function] that takes as argument `t` and `pars` and returns the level of ITN coverage at that time
 #' @return none
 #' @export
 setup_itn_lemenach <- function(pars, tau0_frac = c(0.68/3, 2.32/3), r = 0.56, s = 0.03, F_phi = function(t, pars){.8} ) {

diff --git a/R/lsm-interface.R b/R/lsm-interface.R
@@ -14,7 +14,6 @@ TreatHabitats <- function(t, pars) {
 #' @title Modify effects of LSM
 #' @description This method dispatches on the type of `pars$LSM`.
 #' @param t current simulation time
-#' @param y the state of the system
 #' @param pars a [list]
 #' @return a [list]
 #' @export
@@ -25,7 +24,6 @@ LSM_Effects <- function(t, pars) {
 #' @title Compute effect sizes of LSM
 #' @description This method dispatches on the type of `pars$LSM`.
 #' @param t current simulation time
-#' @param y the state of the system
 #' @param pars a [list]
 #' @return a [list]
 #' @export

diff --git a/man/BedNetEffectSizes.Rd b/man/BedNetEffectSizes.Rd
diff --git a/man/BedNetEffectSizes.lemenach.Rd b/man/BedNetEffectSizes.lemenach.Rd
diff --git a/man/BedNetEffectSizes.null.Rd b/man/BedNetEffectSizes.null.Rd
diff --git a/man/BedNetEffects.lemenach.Rd b/man/BedNetEffects.lemenach.Rd
diff --git a/man/LSM_EffectSizes.Rd b/man/LSM_EffectSizes.Rd
diff --git a/man/LSM_Effects.Rd b/man/LSM_Effects.Rd
diff --git a/man/setup_itn_lemenach.Rd b/man/setup_itn_lemenach.Rd
diff --git a/vignettes/environmental_heterogeneity.Rmd b/vignettes/environmental_heterogeneity.Rmd
@@ -14,10 +14,67 @@ knitr::opts_chunk$set(
 )
 ```
 
-Heterogeneous blood feeding is a basic feature of malaria transmission (see [Heterogeneous Transmission](heterogeneous_transmission.html)). In `exDE` the term **environmental heterogeneity** is used to describe the distribution of the expected number of bites within a homogenous human population stratum: biting is extremely heterogeneous even for individuals who have the same expectation. The approach is motivated by a study of heterogeneous exposure by (Cooper L, *et al.*, 2019)^[Cooper L, Kang SY, *et al.* (2019). Pareto rules for malaria super-spreaders and super-spreading. Nat Commun 10, 3939, https://doi.org/10.1038/s41467-019-11861-y].
+Heterogeneous blood feeding is a basic feature of malaria transmission (see [Heterogeneous Transmission](heterogeneous_transmission.html)). In `exDE` the term **environmental heterogeneity** is used to describe the distribution of the expected number of bites within a homogenous human population stratum: biting is extremely heterogeneous even for individuals who have the same expectation. The approach is motivated by a study of heterogeneous exposure by (Cooper L, *et al.*, 2019)^[Cooper L, Kang SY, *et al.* (2019). Pareto rules for malaria super-spreaders and super-spreading. Nat Commun 10, 3939, https://doi.org/10.1038/s41467-019-11861-y]. 
 
-## The Force of Infection and Attack Rates
+In the following, we derive formulas for the force of infection (FoI) from the model for the attack rate (AR) under the Poisson and . 
 
+## Attack Rates and the Force of Infection
+
+In mechanistic models of malaria, the hazard rate for exposure is generally assumed to be a linear function of the entomological inoculation rate. In the following, we assume that the number of bites per person over a day (or over some longer interval, $\tau$), is a random variable, and we formulate approximating models for attack rates and hazard rates. 
+
+### Poisson Hazard Rates
+
+We let $E$ denote the EIR, the expected number of bites per person over a day. If we assume that the distribution of the daily EIR is Poisson, and if a fraction $b$ of infective bites cause an infection, then the relationship between the between EIR and the FoI is a Poisson compounded with a binomial, which is also Poisson: 
+
+$$
+Z \sim F_E(z) = \mbox{Poisson}(z, \mbox{mu} = bE(t))
+$$
+
+Over a day, the daily attack rate, $\alpha$, is the fraction of individuals who received at least one infection, or: 
+
+$$
+\begin{array}{rl}
+\alpha &= 1-F_E(0) \\ &= 1-\mbox{Poisson}(0, \mbox{mu} = bE(t)) \\ 
+&= 1- e^{-bE(t)} \\
+\end{array}
+$$
+
+The daily FoI, $h$, is given by a generic formula: 
+
+$$
+\alpha = 1 - e^{-h} \mbox{ or equivalently } h = -\ln (1-\alpha)
+$$
+
+In this case, the relationship between the FoI and the EIR is:
+
+$$
+   h(t) = b E(t)
+$$
+
+It is highly mathematically convenient that the relationship is invariant with respect to the sampling period.  
+
+### Negative Binomial Daily Hazards 
+
+If we assume the number of infective bites, per person, per day, has a Gamma distribution in a population, then we could model the number of infective bites as a Gamma - Poisson mixture process, or a negative binomial distribution. Under this model, the counts for bites by sporozoite positive mosquitoes over one day, $Z$, would be a negative binomial random variable with mean $E$:
+
+$$
+Z \sim F_E(z) = \mbox{NB}(z, \mbox{mu} = bE(t), \mbox{size} = 1/\phi)
+$$
+
+Assuming an infectious bite causes an infection with probability $b$, the daily attack rate is: 
+
+$$
+\begin{array}{rl}
+\alpha &= 1-F_E(0) \\ &= 1-\mbox{NB}(0, \mbox{mu} = b E(t), \mbox{size} = 1/\phi) \\
+ &= 1- \left(1+b E(t)\phi \right)^{-1/\phi}
+\end{array}
+$$
+
+This is consistent with a formula that has a continuous daily FoI: 
+
+$$
+    h = \frac{\ln \left(1 + bE(t)\phi \right)} {\phi}
+$$
 
 
 

diff --git a/vignettes/vc_lemenach.Rmd b/vignettes/vc_lemenach.Rmd
@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 In this vignette we demonstrate how to set up a model incorporating the Le Menach model of ITN based vector control (see [this paper](https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-6-10)). We use the Ross-Macdonald model of adult mosquito dynamics, the SIS model of human population dynamics, and the basic competition model of aquatic mosquito dynamics to fill the dynamical components $\mathcal{M}, \mathcal{X}, \mathcal{L}$.
 
 ```{r, message=FALSE, warning=FALSE}
-#library(exDE)
+library(exDE)
 library(MASS)
 library(expm)
 library(deSolve)
@@ -28,7 +28,7 @@ library(ggplot2)
 
 
 ```{r, echo=FALSE} 
-devtools::load_all()
+#devtools::load_all()
 ```
 
 ## Parameters