italy_3rdwave_vaccine-betacostante.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.load_data import get_epidemic_data, get_vaccine_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Epidemic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total cases</th>\n",
       "      <th>Active infected</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Total recovered</th>\n",
       "      <th>Daily cases (avg 7 days)</th>\n",
       "      <th>Daily deaths (avg 7 days)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total cases  Active infected  Total deaths  Total recovered  \\\n",
       "2020-01-22          0.0              0.0           0.0              0.0   \n",
       "2020-01-23          0.0              0.0           0.0              0.0   \n",
       "2020-01-24          0.0              0.0           0.0              0.0   \n",
       "2020-01-25          0.0              0.0           0.0              0.0   \n",
       "2020-01-26          0.0              0.0           0.0              0.0   \n",
       "\n",
       "            Daily cases (avg 7 days)  Daily deaths (avg 7 days)  \n",
       "2020-01-22                       0.0                        0.0  \n",
       "2020-01-23                       0.0                        0.0  \n",
       "2020-01-24                       0.0                        0.0  \n",
       "2020-01-25                       0.0                        0.0  \n",
       "2020-01-26                       0.0                        0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidemic = get_epidemic_data('Italy')\n",
    "df_epidemic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% vaccinated with 1 dose</th>\n",
       "      <th>% vaccinated with 2 doses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-02</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            % vaccinated with 1 dose  % vaccinated with 2 doses\n",
       "date                                                           \n",
       "2020-12-29                      0.02                        0.0\n",
       "2020-12-30                      0.02                        0.0\n",
       "2020-12-31                      0.07                        0.0\n",
       "2021-01-01                      0.08                        0.0\n",
       "2021-01-02                      0.15                        0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine = get_vaccine_data('Italy')\n",
    "df_vaccine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vaccinazioni medie nell'ultimo mese 221793\n"
     ]
    }
   ],
   "source": [
    "media_ultimo_mese = int(np.mean(np.diff(np.sum(df_vaccine[-30:],axis=1))*60*10**6)/100)\n",
    "\n",
    "print('vaccinazioni medie nell\\'ultimo mese',media_ultimo_mese)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% vaccinated with 1 dose</th>\n",
       "      <th>% vaccinated with 2 doses</th>\n",
       "      <th>Total cases</th>\n",
       "      <th>Active infected</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Total recovered</th>\n",
       "      <th>Daily cases (avg 7 days)</th>\n",
       "      <th>Daily deaths (avg 7 days)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2021-04-06</th>\n",
       "      <td>13.31</td>\n",
       "      <td>5.92</td>\n",
       "      <td>3686707.0</td>\n",
       "      <td>555705.0</td>\n",
       "      <td>111747.0</td>\n",
       "      <td>3019255.0</td>\n",
       "      <td>17956.4</td>\n",
       "      <td>409.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>13.69</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3700393.0</td>\n",
       "      <td>547837.0</td>\n",
       "      <td>112374.0</td>\n",
       "      <td>3040182.0</td>\n",
       "      <td>16499.1</td>\n",
       "      <td>432.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>14.07</td>\n",
       "      <td>6.20</td>\n",
       "      <td>3717602.0</td>\n",
       "      <td>544330.0</td>\n",
       "      <td>112861.0</td>\n",
       "      <td>3060411.0</td>\n",
       "      <td>15788.4</td>\n",
       "      <td>430.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>14.47</td>\n",
       "      <td>6.33</td>\n",
       "      <td>3736526.0</td>\n",
       "      <td>536361.0</td>\n",
       "      <td>113579.0</td>\n",
       "      <td>3086586.0</td>\n",
       "      <td>15360.9</td>\n",
       "      <td>464.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-10</th>\n",
       "      <td>14.86</td>\n",
       "      <td>6.44</td>\n",
       "      <td>3754077.0</td>\n",
       "      <td>533085.0</td>\n",
       "      <td>113923.0</td>\n",
       "      <td>3107069.0</td>\n",
       "      <td>14832.9</td>\n",
       "      <td>459.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            % vaccinated with 1 dose  % vaccinated with 2 doses  Total cases  \\\n",
       "date                                                                           \n",
       "2021-04-06                     13.31                       5.92    3686707.0   \n",
       "2021-04-07                     13.69                       6.06    3700393.0   \n",
       "2021-04-08                     14.07                       6.20    3717602.0   \n",
       "2021-04-09                     14.47                       6.33    3736526.0   \n",
       "2021-04-10                     14.86                       6.44    3754077.0   \n",
       "\n",
       "            Active infected  Total deaths  Total recovered  \\\n",
       "date                                                         \n",
       "2021-04-06         555705.0      111747.0        3019255.0   \n",
       "2021-04-07         547837.0      112374.0        3040182.0   \n",
       "2021-04-08         544330.0      112861.0        3060411.0   \n",
       "2021-04-09         536361.0      113579.0        3086586.0   \n",
       "2021-04-10         533085.0      113923.0        3107069.0   \n",
       "\n",
       "            Daily cases (avg 7 days)  Daily deaths (avg 7 days)  \n",
       "date                                                             \n",
       "2021-04-06                   17956.4                      409.7  \n",
       "2021-04-07                   16499.1                      432.6  \n",
       "2021-04-08                   15788.4                      430.6  \n",
       "2021-04-09                   15360.9                      464.4  \n",
       "2021-04-10                   14832.9                      459.9  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df_vaccine.join(df_epidemic,on=df_vaccine.index)\n",
    "\n",
    "# the 3rd wave in Italy started approximately the 23th of February 2021, \n",
    "# with the increasing in the number of hospitalized people\n",
    "df_merged = df_merged[df_merged.index >= '2021-04-06']\n",
    "\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit of the epidemiological model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.epi_model import SIR2\n",
    "from src.optimizer import fit_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.139411\n",
      "         Iterations: 47\n",
      "         Function evaluations: 90\n",
      "Error on susceptible 0.0 %\n",
      "Error on infected 0.3 %\n",
      "Error on removed 0.1 %\n",
      "The average error of the model is 0.1 %\n"
     ]
    }
   ],
   "source": [
    "inhabitants = 60*10**6\n",
    "\n",
    "# true data vaccine\n",
    "ydata_vacc = np.array(df_merged['% vaccinated with 2 doses'])*inhabitants/100\n",
    "vacc_eff = 1\n",
    "vacc_speed = 0.13\n",
    "\n",
    "tmax = len(ydata_vacc)\n",
    "\n",
    "# true data epidemic\n",
    "ydata_cases = np.array(df_merged['Total cases']) + ydata_vacc\n",
    "ydata_inf = np.array(df_merged['Active infected'])\n",
    "ydata_rec = (np.array(df_merged['Total recovered'])+np.array(df_merged['Total deaths']))\n",
    "\n",
    "ll = len(ydata_cases)\n",
    "\n",
    "# optimization of the parameters of the SIR 2.0 model\n",
    "minpar = fit_model(y_data = [ydata_cases,ydata_inf,ydata_rec],\n",
    "                   inhabitants=inhabitants,\n",
    "                   vacc_eff = vacc_eff,\n",
    "                   vacc_speed = vacc_speed,\n",
    "                   V0 = ydata_vacc[0],\n",
    "                   which_error = 'perc',\n",
    "                   decay=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parametri ottimizzati: beta 0.034 gamma 0.039 tau inf\n"
     ]
    }
   ],
   "source": [
    "beta = round(minpar[0],3)\n",
    "gamma = round(minpar[1],3)\n",
    "tau = round(minpar[2],1)\n",
    "\n",
    "if tau >= 1000:\n",
    "    tau=np.inf\n",
    "\n",
    "print('Parametri ottimizzati: beta',beta,'gamma',gamma,'tau',tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parametro Rt per l'Italia è pari a 0.76\n"
     ]
    }
   ],
   "source": [
    "model_check = SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n",
    "              vacc_eff=vacc_eff,\n",
    "              vacc_speed=vacc_speed,\n",
    "              t0=0,  \n",
    "              I0=ydata_inf[0],\n",
    "              R0=ydata_rec[0],\n",
    "              V0=ydata_vacc[0])\n",
    "\n",
    "\n",
    "SN_ratio = model_check[1][ll]/inhabitants\n",
    "Rt = round(beta/gamma*SN_ratio,2)\n",
    "print('Parametro Rt per l\\'Italia è pari a',Rt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check del modello, con curve di suscettibili, infetti e rimossi\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(inhabitants-ydata_cases,label='Data')\n",
    "plt.plot(model_check[1][0:ll],\n",
    "         linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n",
    "plt.title('Susceptible')\n",
    "plt.legend()\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(ydata_inf,label='Data')\n",
    "plt.plot(model_check[2][0:ll],\n",
    "         linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n",
    "plt.title('Infected')\n",
    "plt.legend()\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(ydata_rec,label='Data')\n",
    "plt.plot(model_check[3][0:ll],\n",
    "         linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n",
    "plt.title('Recovered + Deaths')\n",
    "plt.legend()\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(ydata_vacc,label='Data')\n",
    "plt.plot(model_check[4][0:ll],\n",
    "         linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n",
    "plt.title('Vaccinated')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('results/check_model_italy3rd.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### previsioni del modello epidemiologico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model that accounts for lockdown (tau) and vaccination (vacc_speed)\n",
    "\n",
    "pred_lockdown_vaccine=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n",
    "                           vacc_eff = vacc_eff,\n",
    "                           vacc_speed = vacc_speed,\n",
    "                           t0=0,\n",
    "                           I0=ydata_inf[0],\n",
    "                           R0=ydata_rec[0],\n",
    "                           V0=ydata_vacc[0]) \n",
    "\n",
    "pred_lockdown_vaccine_best=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n",
    "                           vacc_eff = vacc_eff,\n",
    "                           vacc_speed = vacc_speed,\n",
    "                           t0=0,\n",
    "                           I0=ydata_inf[0],\n",
    "                           R0=ydata_rec[0],\n",
    "                           V0=ydata_vacc[0],\n",
    "                           vacc_custom = [len(ydata_cases),0.3]) \n",
    "\n",
    "pred_lockdown_vaccine_zero=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n",
    "                           vacc_eff = vacc_eff,\n",
    "                           vacc_speed = vacc_speed,\n",
    "                           t0=0,\n",
    "                           I0=ydata_inf[0],\n",
    "                           R0=ydata_rec[0],\n",
    "                           V0=ydata_vacc[0],\n",
    "                           vacc_custom = [len(ydata_cases),0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Risultati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vaccinati con 1 dose e con 2 dosi\n",
    "vaccinated_1dose = np.array(df_merged['% vaccinated with 1 dose'])\n",
    "vaccinated_2dose = np.array(df_merged['% vaccinated with 2 doses'])\n",
    "\n",
    "# incidenza settimanale ogni 100.000 abitanti\n",
    "weekly_incidence = 7/(inhabitants/10**5)*np.array(df_merged['Daily cases (avg 7 days)'])\n",
    "\n",
    "# decessi giornalieri per milione di abitanti\n",
    "daily_deaths = 1/(inhabitants/10**6)*np.array(df_merged['Daily deaths (avg 7 days)'])\n",
    "\n",
    "# dati sugli infetti attivi\n",
    "ydata_inf_complete = np.array(df_merged['Active infected'])\n",
    "\n",
    "xlab = ['May','Jun','Jul','Aug','Sep','Oct']\n",
    "\n",
    "tmax = 150\n",
    "xgrid = np.arange(0,len(pred_lockdown_vaccine[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(inhabitants-ydata_cases,label='Data',color='blue',marker='.')\n",
    "plt.fill_between(xgrid,pred_lockdown_vaccine[1],pred_lockdown_vaccine_best[1],\n",
    "                 label='SIR model with vaccinations',alpha=0.5,color='green')\n",
    "plt.plot(pred_lockdown_vaccine_zero[1],label='SIR model without vaccinations',color='red')\n",
    "plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n",
    "plt.xlim(0,tmax)\n",
    "plt.yticks(10**7*np.arange(0,6.1,0.5),['0','5M','10M','15M','20M','25M','30M','35M','40M','45M','50M','55M','60M'])\n",
    "plt.ylim(30*10**6,60*10**6)\n",
    "plt.title('Susceptible')\n",
    "plt.legend()\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(ydata_inf_complete,label='Data',color='blue',marker='.')\n",
    "plt.fill_between(xgrid,pred_lockdown_vaccine[2],pred_lockdown_vaccine_best[2],\n",
    "                 label='SIR model with vaccinations',alpha=0.5,color='green')\n",
    "plt.plot(pred_lockdown_vaccine_zero[2],label='SIR model without vaccinations',color='red')\n",
    "plt.title('Infected')\n",
    "plt.yticks(10**5*np.arange(0,6.1,1),['0','100k','200k','300k','400k','500k','600k'])\n",
    "plt.ylabel('Total number')\n",
    "plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n",
    "plt.xlim(0,tmax)\n",
    "plt.legend()\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(ydata_rec,label='Data',color='blue',marker='.')\n",
    "plt.fill_between(xgrid,pred_lockdown_vaccine[3],pred_lockdown_vaccine_best[3],\n",
    "                 label='SIR model with vaccinations',alpha=0.5,color='green')\n",
    "plt.plot(pred_lockdown_vaccine_zero[3],label='SIR model without vaccinations',color='red')\n",
    "plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n",
    "plt.xlim(0,tmax)\n",
    "plt.title('Recovered + Deaths')\n",
    "plt.yticks(10**6*np.arange(0,6.1,0.5),['0','0.5M','1M','1.5M','2M','2.5M','3M','3.5M','4M','4.5M','5M','5.5M','6M'])\n",
    "plt.ylim(2.5*10**6,5.5*10**6)\n",
    "plt.legend()\n",
    "plt.subplot(2,2,4)\n",
    "plt.title('Vaccinated')\n",
    "plt.plot(vaccinated_2dose,label='Data (vaccinated with 2 doses)',color='blue',marker='.')\n",
    "plt.fill_between(xgrid,pred_lockdown_vaccine_best[4]/inhabitants*100,\n",
    "                 pred_lockdown_vaccine[4]/inhabitants*100,\n",
    "                 label='SIR model with vaccinations',alpha=0.5,color='green')\n",
    "plt.plot(pred_lockdown_vaccine_zero[4]/inhabitants*100,label='SIR model without vaccinations',color='red')\n",
    "plt.xticks(np.arange(2,250,30.5),xlab,rotation=30)\n",
    "plt.yticks(np.arange(0,101,10),['0%','10%','20%','30%','40%','50%','60%','70%','80%','90%','100%'])\n",
    "plt.ylim(0,50)\n",
    "plt.xlim(0,tmax)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('results/modello_italia_ondata3_vaccini_bande.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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