efficacia_vaccini.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2119c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1d8e62",
   "metadata": {},
   "source": [
    "## Importa dati divisi per fasce d'età\n",
    "Report del 15 Settembre, https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_15-settembre-2021.pdf\n",
    "I dati si riferiscono ai 30 giorni precedenti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "422f2f51-a238-4292-87b1-b50b3fb43587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>età</th>\n",
       "      <th>non vaccinati</th>\n",
       "      <th>vaccinati completo</th>\n",
       "      <th>casi non vaccinati</th>\n",
       "      <th>casi vaccinati</th>\n",
       "      <th>ospedalizzati non vaccinati</th>\n",
       "      <th>ospedalizzati vaccinati</th>\n",
       "      <th>terapia intensiva non vaccinati</th>\n",
       "      <th>terapia intensiva vaccinati</th>\n",
       "      <th>decessi non vaccinati</th>\n",
       "      <th>decessi vaccinati</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12-39</td>\n",
       "      <td>6602412</td>\n",
       "      <td>7705018</td>\n",
       "      <td>57228</td>\n",
       "      <td>11700</td>\n",
       "      <td>1705</td>\n",
       "      <td>111</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>4684966</td>\n",
       "      <td>12382591</td>\n",
       "      <td>29184</td>\n",
       "      <td>16554</td>\n",
       "      <td>2528</td>\n",
       "      <td>283</td>\n",
       "      <td>295</td>\n",
       "      <td>15</td>\n",
       "      <td>114</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>1765005</td>\n",
       "      <td>11343849</td>\n",
       "      <td>8629</td>\n",
       "      <td>11620</td>\n",
       "      <td>1903</td>\n",
       "      <td>792</td>\n",
       "      <td>366</td>\n",
       "      <td>88</td>\n",
       "      <td>353</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80+</td>\n",
       "      <td>281773</td>\n",
       "      <td>4168357</td>\n",
       "      <td>1859</td>\n",
       "      <td>5116</td>\n",
       "      <td>705</td>\n",
       "      <td>1145</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>294</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     età  non vaccinati  vaccinati completo  casi non vaccinati  \\\n",
       "0  12-39        6602412             7705018               57228   \n",
       "1  40-59        4684966            12382591               29184   \n",
       "2  60-79        1765005            11343849                8629   \n",
       "3    80+         281773             4168357                1859   \n",
       "\n",
       "   casi vaccinati  ospedalizzati non vaccinati  ospedalizzati vaccinati  \\\n",
       "0           11700                         1705                      111   \n",
       "1           16554                         2528                      283   \n",
       "2           11620                         1903                      792   \n",
       "3            5116                          705                     1145   \n",
       "\n",
       "   terapia intensiva non vaccinati  terapia intensiva vaccinati  \\\n",
       "0                               73                            4   \n",
       "1                              295                           15   \n",
       "2                              366                           88   \n",
       "3                               52                           56   \n",
       "\n",
       "   decessi non vaccinati  decessi vaccinati  \n",
       "0                      9                  2  \n",
       "1                    114                 15  \n",
       "2                    353                100  \n",
       "3                    294                288  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_età = pd.read_csv('dati/data_iss_età_2021-09-15.csv', sep=';')\n",
    "\n",
    "df_età"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4016e2e",
   "metadata": {},
   "source": [
    "## Elaborazione dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "784a90a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Casi, non vaccinati</th>\n",
       "      <th>Casi, vaccinati</th>\n",
       "      <th>Ospedalizzati, non vaccinati</th>\n",
       "      <th>Ospedalizzati, vaccinati</th>\n",
       "      <th>In terapia intensiva, non vaccinati</th>\n",
       "      <th>In terapia intensiva, vaccinati</th>\n",
       "      <th>Deceduti, non vaccinati</th>\n",
       "      <th>Deceduti, vaccinati</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>età</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12-39</th>\n",
       "      <td>202.247300</td>\n",
       "      <td>35.431455</td>\n",
       "      <td>6.025576</td>\n",
       "      <td>0.336145</td>\n",
       "      <td>0.257987</td>\n",
       "      <td>0.012113</td>\n",
       "      <td>0.031807</td>\n",
       "      <td>0.006057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-59</th>\n",
       "      <td>145.350041</td>\n",
       "      <td>31.193795</td>\n",
       "      <td>12.590629</td>\n",
       "      <td>0.533276</td>\n",
       "      <td>1.469239</td>\n",
       "      <td>0.028265</td>\n",
       "      <td>0.567774</td>\n",
       "      <td>0.028265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-79</th>\n",
       "      <td>114.075220</td>\n",
       "      <td>23.901352</td>\n",
       "      <td>25.157625</td>\n",
       "      <td>1.629077</td>\n",
       "      <td>4.838513</td>\n",
       "      <td>0.181009</td>\n",
       "      <td>4.666653</td>\n",
       "      <td>0.205692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>153.941885</td>\n",
       "      <td>28.637982</td>\n",
       "      <td>58.380327</td>\n",
       "      <td>6.409400</td>\n",
       "      <td>4.306067</td>\n",
       "      <td>0.313473</td>\n",
       "      <td>24.345839</td>\n",
       "      <td>1.612146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Casi, non vaccinati  Casi, vaccinati  Ospedalizzati, non vaccinati  \\\n",
       "età                                                                         \n",
       "12-39           202.247300        35.431455                      6.025576   \n",
       "40-59           145.350041        31.193795                     12.590629   \n",
       "60-79           114.075220        23.901352                     25.157625   \n",
       "80+             153.941885        28.637982                     58.380327   \n",
       "\n",
       "       Ospedalizzati, vaccinati  In terapia intensiva, non vaccinati  \\\n",
       "età                                                                    \n",
       "12-39                  0.336145                             0.257987   \n",
       "40-59                  0.533276                             1.469239   \n",
       "60-79                  1.629077                             4.838513   \n",
       "80+                    6.409400                             4.306067   \n",
       "\n",
       "       In terapia intensiva, vaccinati  Deceduti, non vaccinati  \\\n",
       "età                                                               \n",
       "12-39                         0.012113                 0.031807   \n",
       "40-59                         0.028265                 0.567774   \n",
       "60-79                         0.181009                 4.666653   \n",
       "80+                           0.313473                24.345839   \n",
       "\n",
       "       Deceduti, vaccinati  \n",
       "età                         \n",
       "12-39             0.006057  \n",
       "40-59             0.028265  \n",
       "60-79             0.205692  \n",
       "80+               1.612146  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ricava i tassi, dividendo per la popolazione vaccinati e non vaccinata\r\n",
    "tassi = (7/30)*10**5*np.transpose([df_età['casi non vaccinati']/df_età['non vaccinati'],\r\n",
    "         df_età['casi vaccinati']/df_età['vaccinati completo'],                  \r\n",
    "         df_età['ospedalizzati non vaccinati']/df_età['non vaccinati'],\r\n",
    "         df_età['ospedalizzati vaccinati']/df_età['vaccinati completo'],\r\n",
    "         df_età['terapia intensiva non vaccinati']/df_età['non vaccinati'],\r\n",
    "         df_età['terapia intensiva vaccinati']/df_età['vaccinati completo'],\r\n",
    "         df_età['decessi non vaccinati']/df_età['non vaccinati'],\r\n",
    "         df_età['decessi vaccinati']/df_età['vaccinati completo']])\r\n",
    "\r\n",
    "\r\n",
    "df_tassi = pd.DataFrame(tassi)\r\n",
    "df_tassi.columns = ['Casi, non vaccinati',\r\n",
    "                    'Casi, vaccinati',\r\n",
    "                    'Ospedalizzati, non vaccinati', \r\n",
    "                    'Ospedalizzati, vaccinati',\r\n",
    "                    'In terapia intensiva, non vaccinati',\r\n",
    "                    'In terapia intensiva, vaccinati',\r\n",
    "                    'Deceduti, non vaccinati',\r\n",
    "                    'Deceduti, vaccinati']\r\n",
    "\r\n",
    "df_tassi.index = df_età['età']\r\n",
    "\r\n",
    "df_tassi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a17df00",
   "metadata": {},
   "source": [
    "## Rappresentazione grafica dei risultati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15932fdd-cfa9-489f-987d-42ca0c54d497",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_axe(x):\n",
    "    axes[x].set_ylabel('Ogni 100.000 persone per ciascun gruppo')\n",
    "    axes[x].set_xlabel('Fascia d\\'età')\n",
    "    axes[x].legend(['Non vaccinati', 'Vaccinati'])\n",
    "    axes[x].grid()\n",
    "    for tick in axes[x].get_xticklabels():\n",
    "        tick.set_rotation(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f813f84e",
   "metadata": {},
   "source": [
    "### Tassi di contagio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9edfece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes2 = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))\r\n",
    "\r\n",
    "# unpack all the axes subplots\r\n",
    "axes = axes2.ravel()\r\n",
    "\r\n",
    "df_tassi.iloc[:, [0, 1]].plot(ax=axes[0],kind='bar')\r\n",
    "axes[0].set_title('Incidenza settimanale dei nuovi casi')\r\n",
    "which_axe(0)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [2, 3]].plot(ax=axes[1], kind='bar')\r\n",
    "axes[1].set_title('Incidenza settimanale degli ospedalizzati')\r\n",
    "which_axe(1)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [4, 5]].plot(ax=axes[2], kind='bar')\r\n",
    "axes[2].set_title('Incidenza settimanale dei ricoveri in TI')\r\n",
    "which_axe(2)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [6, 7]].plot(ax=axes[3], kind='bar')\r\n",
    "axes[3].set_title('Incidenza settimanale dei deceduti')\r\n",
    "which_axe(3)\r\n",
    "\r\n",
    "plt.tight_layout()\r\n",
    "plt.savefig('risultati/tassi_per_età.png', dpi=300)\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3947148",
   "metadata": {},
   "source": [
    "### Efficacia dei vaccini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30c240f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "efficacia_contagio = (1 - df_tassi.iloc[:, 1]/df_tassi.iloc[:, 0])*100\r\n",
    "efficacia_osp = (1 - df_tassi.iloc[:, 3]/df_tassi.iloc[:, 2])*100\r\n",
    "efficacia_terint = (1 - df_tassi.iloc[:, 5]/df_tassi.iloc[:, 4])*100\r\n",
    "efficacia_decessi = (1 - df_tassi.iloc[:, 7]/df_tassi.iloc[:, 6])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1e9e268",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_to_plot():\r\n",
    "    plt.ylim(70, 100)\r\n",
    "    plt.grid()\r\n",
    "    plt.xlabel('Fascia d\\'età')\r\n",
    "    plt.yticks(np.arange(70, 101, 5), ['70%', '75%', '80%', '85%', '90%', '95%', '100%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16db9715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 9))\r\n",
    "\r\n",
    "plt.subplot(2, 2, 1)\r\n",
    "plt.bar(efficacia_contagio.index, efficacia_contagio, color='blue', width=0.5)\r\n",
    "plt.title('Efficacia contro il contagio')\r\n",
    "add_to_plot()\r\n",
    "\r\n",
    "plt.subplot(2, 2, 2)\r\n",
    "plt.bar(efficacia_osp.index, efficacia_osp, color='green', width=0.5)\r\n",
    "plt.title('Efficacia contro l\\'ospedalizzazione')\r\n",
    "add_to_plot()\r\n",
    "\r\n",
    "plt.subplot(2, 2, 3)\r\n",
    "plt.bar(efficacia_terint.index, efficacia_terint, color='red', width=0.5)\r\n",
    "plt.title('Efficacia contro il ricovero in TI')\r\n",
    "add_to_plot()\r\n",
    "\r\n",
    "plt.subplot(2, 2, 4)\r\n",
    "plt.bar(efficacia_decessi.index, efficacia_decessi, color='gray', width=0.5)\r\n",
    "plt.title('Efficacia contro il decesso')\r\n",
    "add_to_plot()\r\n",
    "\r\n",
    "plt.tight_layout()\r\n",
    "plt.savefig('risultati/efficacia_vaccini.png', dpi=300)\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5268f1b",
   "metadata": {},
   "source": [
    "### Grafico riassuntivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2635607c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_axe_bar(x):\r\n",
    "    axes[x].set_ylim(80, 100)\r\n",
    "    axes[x].set_yticks(np.arange(70, 101, 5))\r\n",
    "    axes[x].set_yticklabels(['70%', '75%', '80%', '85%', '90%', '95%', '100%'])\r\n",
    "    axes[x].set_xlabel('Fascia d\\'età')\r\n",
    "    axes[x].grid()\r\n",
    "    for tick in axes[x].get_xticklabels():\r\n",
    "        tick.set_rotation(0)\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "134156b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes2 = plt.subplots(nrows=2, ncols=4, figsize=(15, 8))\r\n",
    "\r\n",
    "# unpack all the axes subplots\r\n",
    "axes = axes2.ravel()\r\n",
    "\r\n",
    "df_tassi.iloc[:, [0, 1]].plot(ax=axes[0],kind='bar')\r\n",
    "axes[0].set_title('Incidenza settimanale dei nuovi casi')\r\n",
    "which_axe(0)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [2, 3]].plot(ax=axes[1], kind='bar')\r\n",
    "axes[1].set_title('Incidenza settimanale degli ospedalizzati')\r\n",
    "which_axe(1)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [4, 5]].plot(ax=axes[2], kind='bar')\r\n",
    "axes[2].set_title('Incidenza settimanale dei ricoveri in TI')\r\n",
    "which_axe(2)\r\n",
    "\r\n",
    "df_tassi.iloc[:, [6, 7]].plot(ax=axes[3], kind='bar')\r\n",
    "axes[3].set_title('Incidenza settimanale dei deceduti')\r\n",
    "which_axe(3)\r\n",
    "\r\n",
    "efficacia_contagio.plot(kind='bar', ax=axes[4], color='blue')\r\n",
    "axes[4].set_title('Efficacia contro il contagio')\r\n",
    "which_axe_bar(4)\r\n",
    "\r\n",
    "efficacia_osp.plot(kind='bar', ax=axes[5], color='green')\r\n",
    "axes[5].set_title('Efficacia contro l\\'ospedalizzazione')\r\n",
    "which_axe_bar(5)\r\n",
    "\r\n",
    "efficacia_terint.plot(kind='bar', ax=axes[6], color='red')\r\n",
    "axes[6].set_title('Efficacia contro il ricovero in TI')\r\n",
    "which_axe_bar(6)\r\n",
    "\r\n",
    "efficacia_decessi.plot(kind='bar', ax=axes[7], color='gray')\r\n",
    "axes[7].set_title('Efficacia contro il decesso')\r\n",
    "which_axe_bar(7)\r\n",
    "\r\n",
    "plt.tight_layout()\r\n",
    "plt.savefig('risultati/tassi_efficacia.png', dpi=300)\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a1ee2b-152d-4802-b7e3-815e4b951adb",
   "metadata": {},
   "source": [
    "### Focus sugli over 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "409cd5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "non vaccinati                       2046778\n",
       "vaccinati completo                 15512206\n",
       "terapia intensiva non vaccinati         418\n",
       "terapia intensiva vaccinati             144\n",
       "decessi non vaccinati                   647\n",
       "decessi vaccinati                       388\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_over60 = df_età.loc[[2,3], ['non vaccinati', 'vaccinati completo', \n",
    "                'terapia intensiva non vaccinati', 'terapia intensiva vaccinati',\n",
    "                'decessi non vaccinati', 'decessi vaccinati']].sum()\n",
    "\n",
    "df_over60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebad51c4-c907-4fff-80f9-5fe734148884",
   "metadata": {},
   "outputs": [],
   "source": [
    "over60_array = np.array(df_over60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c35d290f-94cc-49ce-96cb-fe0575e3a141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.bar(0, over60_array[0], width=0.5, color='red')\n",
    "plt.bar(1, over60_array[1], width=0.5, color='green')\n",
    "plt.xticks([0, 1], ['Non vaccinati', 'Vaccinati'])\n",
    "plt.yticks(10**6*np.arange(0, 17, 2), ['0', '2M', '4M', '6M', '8M', '10M', '12M', '14M', '16M'])\n",
    "plt.grid()\n",
    "plt.title('Popolazione over 60 \\n15 Ago - 15 Set')\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.bar(0, over60_array[2], width=0.5, color='red')\n",
    "plt.bar(1, over60_array[3], width=0.5, color='green')\n",
    "plt.xticks([0, 1], ['Non vaccinati', 'Vaccinati'])\n",
    "plt.grid()\n",
    "plt.title('In terapia intensiva \\n15 Ago - 15 Set')\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.bar(0, over60_array[4], width=0.5, color='red')\n",
    "plt.bar(1, over60_array[5], width=0.5, color='green')\n",
    "plt.xticks([0, 1], ['Non vaccinati', 'Vaccinati'])\n",
    "plt.grid()\n",
    "plt.title('Deceduti \\n15 Ago - 15 Set')\n",
    "plt.tight_layout()\n",
    "plt.savefig('risultati/focus_over60.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0ef7993-cb7a-4c9f-bf61-30bbf13909f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rapporto tra vaccinati e non vaccinati 7.6\n",
      "Rapporto tra ricoverati in terapia intensiva (novacc/vacc) 2.9\n",
      "Rapporto tra deceduti (novacc/vacc) 1.7\n"
     ]
    }
   ],
   "source": [
    "ratio_vacc_novacc = round(over60_array[1]/over60_array[0], 1)\n",
    "ratio_terint = round(over60_array[2]/over60_array[3], 1)\n",
    "ratio_dec = round(over60_array[4]/over60_array[5], 1)\n",
    "\n",
    "print('Rapporto tra vaccinati e non vaccinati', ratio_vacc_novacc)\n",
    "print('Rapporto tra ricoverati in terapia intensiva (novacc/vacc)', ratio_terint)\n",
    "print('Rapporto tra deceduti (novacc/vacc)', ratio_dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1823ace-5fe2-48d9-86ad-c690069937ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peso sul sistema sanitario di un non vaccinato over 60: 22.04\n",
      "Peso sulla mortalità di un non vaccinato over 60:       12.92\n"
     ]
    }
   ],
   "source": [
    "print('Peso sul sistema sanitario di un non vaccinato over 60:', ratio_vacc_novacc*ratio_terint)\n",
    "print('Peso sulla mortalità di un non vaccinato over 60:      ', ratio_vacc_novacc*ratio_dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b13e7967-8780-42c7-a2d3-9da3f62082c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec915fd-a8db-4356-bcb6-6057b338d017",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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