5-SIR-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['axes.titlesize'] = 16\n",
    "mpl.rcParams['axes.labelsize'] = 14\n",
    "mpl.rcParams['legend.fontsize'] = 12\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['axes.spines.right'] = False\n",
    "mpl.rcParams['axes.spines.top'] = False\n",
    "\n",
    "from typing import List\n",
    "from collections import Counter\n",
    "from scipy.integrate import solve_ivp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The SIR model\n",
    "\n",
    "We will focus on the SIR model, probably the most studied model in computational epidemics. In its simplest form, the SIR model can be defined as follows. A large population of $N$ individuals is divided into three classes:\n",
    "+ **Susceptible** individuals (S): those who do not have the disease but can get it.\n",
    "+ **Infected** individuals (I) : those who have the disease.\n",
    "+ **Recovered** individuals (R): those who cannot get the disease anymore.\n",
    "\n",
    "The basic dynamics of the model are simple as well:\n",
    "1. Susceptible individuals might get infected if they are in contact with an infected individual. \n",
    "2. Infected individuals become recovered after some time. \n",
    "\n",
    "Notice that individuals can only go from *S* to *I* and from *I* to *R*. We will also assume that infective individuals have contacts with randomly chosen individuals of all states at an average rate $\\beta$ per unit time, and recover and acquire immunity (or die) at an average rate $\\gamma$ per unit time. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully mixed population\n",
    "If we assume a very large population that is **fully mixed**, then the model can be descrived by a set of coupled nonlinear differential equations:  \n",
    "\n",
    "\n",
    "<img src=\"attachment:image.png\" width=\"400\"> "
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.1\n",
    "The `scipy.integrate` library has a function `solve_ip` that can be used to solve initial value problems for systems of ODES.\n",
    "The documentation can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html).\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the documentation and try to understand how to use `solve_ivp`. The key bit is to notice that we have to write a **function** `f(t, y)`  whose first argument is the timepoint `t` and whose second argument `y` is a numpy array. In our case, y will have dimension 3. Do you see why?\n",
    "\n",
    "\n",
    "The Initial Value Problem to be solved is then simply\n",
    "\n",
    "```text\n",
    "    dy / dt = f(t, y)\n",
    "    y(t0) = y0\n",
    "```\n",
    "\n",
    "\n",
    "Write a function `ode_SIR` that represents the system of differential equations of a SIR model and follows the specification of `solve_ivp`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def odes_SIR(t, y, beta, gamma):\n",
    "    \"\"\"\n",
    "    The main ODE equations of the SIR model\n",
    "    \"\"\"\n",
    "    #definisco la struttura delle tre equazioni differenziali\n",
    "    \n",
    "    S, I, R = y[0], y[1], y[2]\n",
    "    N =S+I+R\n",
    "    f=np.array([\n",
    "        - beta*(I/N)*S,\n",
    "        beta*(I/N)*S - gamma*I,\n",
    "        gamma*I,        \n",
    "    ])\n",
    "    return f\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.2\n",
    "Use `solve_ivp` and your `ode_SIR` function to study the behaviour of the SIR model for different values of the parameters, and describe your findings with a short paragraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parameters\n",
    "beta=0.4\n",
    "gamma=0.04\n",
    "y0 = (997, 3, 0)\n",
    "tmax = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# solve ODE\n",
    "ode_result = solve_ivp(odes_SIR, [0,100], y0, args=(beta,gamma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  message: 'The solver successfully reached the end of the integration interval.'\n",
      "     nfev: 116\n",
      "     njev: 0\n",
      "      nlu: 0\n",
      "      sol: None\n",
      "   status: 0\n",
      "  success: True\n",
      "        t: array([0.00000000e+00, 1.17830911e-02, 1.29614002e-01, 1.30792311e+00,\n",
      "       3.99186727e+00, 7.51064787e+00, 1.15519667e+01, 1.63835523e+01,\n",
      "       2.13986467e+01, 2.50280201e+01, 2.86573936e+01, 3.26579132e+01,\n",
      "       3.70605830e+01, 4.21544159e+01, 4.82514515e+01, 5.58550574e+01,\n",
      "       6.59007449e+01, 8.04994099e+01, 1.00000000e+02])\n",
      " t_events: None\n",
      "        y: array([[9.97000000e+02, 9.96985873e+02, 9.96841280e+02, 9.95005514e+02,\n",
      "        9.86452814e+02, 9.52814423e+02, 8.25344455e+02, 4.63249670e+02,\n",
      "        1.42733501e+02, 5.41603175e+01, 2.17521331e+01, 8.90080338e+00,\n",
      "        3.82597479e+00, 1.69482742e+00, 7.74841329e-01, 3.67997756e-01,\n",
      "        1.83933660e-01, 9.91071215e-02, 6.50517740e-02],\n",
      "       [3.00000000e+00, 3.01271007e+00, 3.14279870e+00, 4.79423689e+00,\n",
      "        1.24836672e+01, 4.26528380e+01, 1.55769524e+02, 4.60272308e+02,\n",
      "        6.63206254e+02, 6.55071401e+02, 5.96355609e+02, 5.19884509e+02,\n",
      "        4.40522728e+02, 3.61213569e+02, 2.83839529e+02, 2.09745542e+02,\n",
      "        1.40486334e+02, 7.84109021e+01, 3.59723452e+01],\n",
      "       [0.00000000e+00, 1.41696411e-03, 1.59209966e-02, 2.00249090e-01,\n",
      "        1.06351848e+00, 4.53273892e+00, 1.88860215e+01, 7.64780217e+01,\n",
      "        1.94060246e+02, 2.90768281e+02, 3.81892258e+02, 4.71214687e+02,\n",
      "        5.55651297e+02, 6.37091603e+02, 7.15385629e+02, 7.89886460e+02,\n",
      "        8.59329732e+02, 9.21489991e+02, 9.63962603e+02]])\n",
      " y_events: None\n"
     ]
    }
   ],
   "source": [
    "print(ode_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract results\n",
    "assert ode_result.success\n",
    "t_range = ode_result.t\n",
    "S, I, R = ode_result.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ehBDuxZTGYq31OownhKpbNw9of4p1GnjE/vJKSdFhfLY8nbTDhSTIFJZeYd3BdTy77Fl25OxgaMJQHu39qDQEC6eSISacrIu9h/H6jBxJBB6uoLSASWsm8d8t/yWqURSTB07m/NjznR2WEJIInK1dsxD8fSykZORyeddoZ4cjHGTh3oVM+HMCBwsPcm37a7mv+30E+UriF65BEoGT+VotdGwZygZpMPZIh44d4uUVL/Pznp9pE96GiQMmkhyZfOYdhWhAkghcQHJMOHNW7aXcprFapIexJ9Ba8/WOr3lt1WscKzvGPV3v4ebON8uYQMIlmfXUkKiHLtFhFJaUsysr39mhCBOkHU3j1l9u5emlT9O2cVu+vOxLbk++XZKAcFlSInABybEVDca5tG0WcoathasqtZUyPXU6761/Dz+LH0+f/TTD2w7HouR+S7g2SQQuILFpMEF+VlIycriqh0wo4o42HtrI+KXj2XpkK4PjBvN4n8eJahTl7LCEqBFJBC7AalF0ig6ToSbcUGFpIZPXTWbm5pk0CWjCGwPeYFD8IGeHJUStSCJwEckxYUxflkZpuQ1fq1QluIMlmUuY8OcEMvMzuabdNYztMZYQP6naE+5HEoGL6BITTknZbrbuz6NztEw36MqOlR3j+T+f59ud35IYlsi0odPo0ayHs8MSos4kEbiI5Io5jDNzJRG4sAMFB7hv/n1sPryZMUljuD3pdvysfs4OS4h6kUTgIuIiGhEW6MuGjFyu7e3saER1Ug+nct9v95Ffms9bA9+S4SGEx5DKaBehlCIpJowNMoexS/plzy+M/nE0PhYfPrnkE0kCwqNIInAhXaLD2Lo/j6LScmeHIuy01ry3/j0eWvgQ7SPa89k/PqNd43bODksIU0kicCFJMeGU2TSb/zrq7FAEUFRWxKOLHuXtdW9zaatL+fCiD2kS2MTZYQlhOmkjcCFJlRqMu8U1dnI03i2rMIv7599PyqEU7u9+P7d0vgWlZBwo4ZkkEbiQFmEBNA32Y/3eXDjb2dF4r82HN3Pv7/dytOSodBATXkGqhlyI0WAcTkpmjrND8Vq/pf/GqJ9GATB96HRJAsIrSCJwMV2iw9hxMJ+C4jJnh+JVtNZ8mPIhY+ePpU14G2b9YxYdmnRwdlhCNAhJBC4mOTYMm4bUfQ5qMM7NhJ3z4Uga2OTpJICS8hKeXPwkk9ZM4uKEi/nooo+IbBTp7LCEaDDSRuBiukSHA7AhI4feiRHmn2DOjZC5ynhv9YeIRIhoDU3sr4r3IS3ACxpHDx87zNj5Y1mXtY67u97N7Um3S6Ow8DqSCFxMZIg/LcMCHDN15cHNRhLocydEtYfDO41X9k7YMQ/Ki//e1jcIIlrBWUNhwBNg8bzC47Yj27j3t3vJLsrmtfNf46KEi5wdkhBOIYnABXWJCSMl0wGJYO2nYPGB8x6GoKYnrrOVQ26GkRQqEsSBjbDo31CcB0Nf9qgSwsK9C3lk0SME+wYzbeg0OjXt5OyQhHAaSQQuKCkmnJ9TD5B7rJSwQJOmNywvhQ2zod3Qk5MAgMUKjeONV+uBxjKt4ecn4c+3wS8YBo0zJxYn0lozY9MMJq6aSPuI9rw18C2aBTVzdljCyUpLS8nIyKCoqMjZodSL1WolPDycpk2bYqlFKV4SgQuq6Fi2MTOX/m2q+dKui+2/QkEWdL2u5vsoBRe9ACX58Mdr4B8M5zxgTjxOUFpeyoQ/J/DVjq+4MP5CXjjnBQJ9Ap0dlnABGRkZhISEkJCQ4LZtRFprSktLOXDgABkZGcTFxdV4X8+r+PUAXaIr5jDOMe+g62ZCUCS0vbB2+ykFw/4DXa6GeeNhxQfmxdSASstLGbtgLF/t+Irbk27ntfNfkyQgjisqKqJJkyZumwTA6Ifk5+dHdHQ0BQUFtdpXSgQuKLyRH/FNGpFiVoNxfhZs+wn63gnWOlQ1WaxwxbtQUgj/exj8gqDrSHNiawBltjIe/eNRFmUsYlzfcVxz1jXODkm4IHdOApXVpkro+D4OiEOYoEt0mHlPDqXMAVsZdL2+7sew+sJVH0GrAfDN3ZD6tTmxOVi5rZynljzFr2m/8kivRyQJCFENSQQuKjkmnMycYxzOLz7zxqejNaydCdE9jEdG68M3AEZ8BjG94MtbYdsv9Tueg2mtmfDnBH7Y9QP3dbuPGzre4OyQhHBJkghcVBd7g/GG+j5G+tc6OJhau0bi0/ELgpFzIKoDzLkBdv9hznFNprXmlZWv8OX2L7mty23clnSbs0MSos4WL15Mv379CAsLIyIigv79+7Ny5UrTjm9aIlBKjVBKbVZKFSildiqlzrUvH6SU2qKUKlRKzVdKxVfaRymlXlFKHba/XlWeUlFXT52jw1AKNuytZyJY+yn4BEDn4eYEBhAYDjd8BeHxMGsEZKwy79gm0Fozac0kZm6eyfUdrufebvc6OyQh6uzo0aMMGzaMe++9l+zsbDIzM3nmmWfw9/c37RymJAKl1IXAK8BNQAhwHrBLKdUUmAuMAyKAVcDsSruOAa4AkoEkYBhwuxkxubtgfx9aRwbXbyTS0iJI+RzaDzO+vM0U1BRu/Mb4+elw2L/R3OPXw5QNU5i6cSpXt7uaR3o94jGNgMI7bdu2DYBrr70Wq9VKYGAgQ4YMISkpybRzmPXU0LPAc1rrP+2fMwGUUmOAVK315/bP44FDSqn2WustwChgotY6w75+InAb8J5Jcbm1pOgw/thxCK113b7Mtv4ARbnQzaRqoapCW8CN38JHQ+GTK+CmH6FpW8ecq4amp05n8rrJXNrqUp7q+5QkAVEnz36XyiZHDfxo17FlKM9ceuYe7e3atcNqtTJq1ChGjBhB3759adzY3Imr6l0iUEpZgZ5ApFJqh1IqQyk1WSkVCHQC1ldsq7UuAHbal1N1vf19tf8ySqkxSqlVSqlVWVlZ9Q3bLSTFhJGVV8yBo3VsMF47E0JjINGBE603jjdKBlrDJ/+EgkOOO9cZzN4ym9dWvcaQ+CE81/85LEqawIT7Cw0NZfHixSiluO2224iMjOSyyy7jwIEDpp3DjBJBM8AXuAo4FygFvgGeAoKBqt/auRjVR9jX51ZZF6yUUlprXXknrfUUYApAz549T1jnqbrEhAPGSKTNw5rXbufcTNj5O5z3L6MfgCNFtoPrPoePL4bPRxvtB3Xpr1AP3+z4hueXP8+AmAG8fO7L+Fiki4you5rcqTekDh06MG3aNAC2bNnC9ddfz9ixY5k1a5YpxzfjlumY/edbWuu/tNaHgNeBS4B8ILTK9qFAnv191fWhQH7VJOCtOrUMxWpRdetPsH4WoBuu41d0d7h0Euz5A355qmHOaffT7p94eunTnN3ibF4b8Bq+DZyEhGhI7du3Z/To0WzcaF67XL0Tgdb6CJABVPflnYrREAyAUioIaG1fftJ6+/tUBAABvlbaNQup/SOkWhtDSsSfY8w30FCSR0Dfu2D5e7DuswY55e/pv/P4H4/TLaobkwZOwt9q3pMUQriCLVu2MHHiRDIyMgDYu3cvs2bNom/fvqadw6xK1I+Be5VSUUqpxsBY4HvgK6CzUmq4UioAeBrYYG8oBpgBPKiUilZKtQQeAqaZFJNHSIoOY0NGDrUqJKX/Cdm7HNdIfDoXToDE8+C7sZC52qGnWpK5hIcXPkyHJh14e9DbMnaQ8EghISEsX76cPn36EBQURN++fencuTMTJ0407RxmVaROAJoC24AiYA7wgta6SCk1HJgMfAosB0ZU2u99oBWQYv/8oX2ZsEuKDWP2qr1kHDlGbESjmu209lNj2OiOlzs2uOpYfeCqaTBlAMy+AcYsgOAo00+zcv9K7p9/P63DW/Pu4HcJ8g0y/RxCuILo6GjmzJnj0HOYUiLQWpdqre/SWodrrZtrre/TWhfZ183TWrfXWgdqrQdorfdU2k9rrR/RWkfYX49I+8CJkuxTV9Z4JNLifEj9CjpdYfQCdoagJjDiUyjMNqbGLCsx9fDrs9Zzz2/3EB0czfsXvk+Yf5ipxxfC28jzdS7urOYh+FktNR+JdNM3UFpQvwHmzNAiGS6fDOnL4OfHTTvspsObuPPXO2ka2JQPh3xIRIAD5nUWwsvIM3Yuzs/HQocWITUvEaybaUxAH2deQ1KddbnKGOto6VtGYuh+Y70Ol5mfyR2/3kGwXzAfDvmQyEaR5sQphJeTEoEbSIoJZ2PmUWy2M9SaZe+CtCVGI7Gr9KgdNB5aXQA/PAR76z5I1rGyY4ydP5YyXcaUC6fQIriFeTEK4eUkEbiBLjFh5BeXsfvwGWYdWvcZKAskX9swgdWE1ceYxyCkhTFaad7+Wh9Ca80zS59ha/ZWXj3vVRLCEsyPUwgvJonADVTMYbzhdNVDtnJYN8uYeD60ZcMEVlONIox5DIpy69R4PGPTDH7c/SP3db+Pc6LPcVCQQngvSQRuoE1kMIG+1tP3MN69EI5mmDfvgNmad4Yr3oG9y+HHR2q827J9y3h99etcGH8ht3S+xYEBCuG9pLHYDfhYLXRqGXr6RLB2JgSEw1mXNFhctdbpn/DXelj8H6PxuOdNp908Iy+Dfy36F63CWvF8/+dlJFEhHERKBG4iKSac1H25lJXbTl557Ahs/g66XG1MJ+nKBo6DNoPhf/+C9OWn3OxY2TEeWPAANm1j0gWTaORbw850Qohak0TgJpJiwigqtbH9YP7JKzd+CeXFzhlSorYsVhj+IYTFGI3HuRknbVK1cTguNM4JgQrhOhISEpg3b57Dji+JwE1UNBhX27Fs7UyI6gQtujZsUHUV2Biu/S+UHoPPRkBx3gmrpXFYiIYlicBNJDQJIsTfhw1Vp648uBn2rYFu17tO34GaiGoPV0+Dg5vgi1uMp56QxmEhnEEai92ExaLoEhN2coPx2k/B4gNJ1zgnsPpoMwj+8Rp8/wD8/CQZ59wjjcPCNfz4GOxPOfN29dG8C1z8smPPUUOSCNxIl5gwPlq8m+Kycvx9rFBeChtmQ7uhxiTy7qjnzXB4J8f+fJuxucuxaRtvXvCmNA4L0YAkEbiRpOhwSss1W/fnkRQTDtt/hYIso1rIjenBz/LMwYVsK8rinQ5jiA2NdXZIwtu5yJ16Q5E2Ajfydw9je/XQupkQFAVtLnRiVPU3ffOn/GjL5b6yQM757RXYb94UfEKIM5NE4EZiGgfSuJGvMdREfhZs+wmS/88Yz8dNLd23lP+s+Q9D4odwy9Vfg38IfPZ/kHfA2aEJ4TUkEbgRpRRJMeFGiWDDbLCVOX/egXrIyMvgkUWP0CqsFRP6T0CFRcPI2XAsG2aNgJJCZ4cohFeQROBmkmLC2H4wD9vaTyG6p/EYphsqLC3k/vn3n9w43CIZhk+FfWvhq9vBVk1PaiG8zJ49exg8eLDDji+JwM0kxYTTUe/CkrXZPXoSn8KrK19l+5HtvHreqyc3Dre/BC56ATZ/C78/55wAhfAi7lu57KWSYsK42rqQMos/Pp2HOzucOlmUsYgvt3/JzZ1vPnXP4b53weEdxgB1Ea2h+w0NG6QQXkRKBG6mWbAvV/gsY11Qfwhwv0nbjxQd4eklT9O2cVvu7nr3qTdUCi5+1Zhf4fuxsHtRg8UohLeRROBu9qcQSj5fFyah9RmmrnQxWmsm/DmB3JJcXjrnJfysfqffweprDEPRpA3Mvh4ObW+QOIXwNpII3E3aEgDmFbQmPdu9nqr53+7/8Wvar9zd9W7OijirZjsFhMHIOWD1g5lXQ8FhxwYphBeSROBu0pZSEhrPfpqwfHe2s6Opsf0F+3lh+Qt0jezKTZ1OPyHNSRrHw4hZcHQfzL4OyoodE6QQXkoSgTux2SBtKb6tzqFxI19Wukki0Frz9JKnKbOV8cI5L2C1WGt/kNhe8M93IX0ZfHGzMc6SEMIUkgjcSdYWOJaNiu9Pz4QIVuxxj0Qwe+tslv21jId7Ply/SWY6DzcakLd8D1/eAuVl5gUphBeTROBO7O0DJPSnT2IEaYcLOXC0yLkxnUHa0TQmrppI/5b9ubrd1fU/YJ/b4aIXYdM3MPc2SQZCmEASgTtJWwqh0RAeT6+ECABWuHD1UJmtjCcWP4Gf1Y9n+z1r3vwCZ98NF06A1Lnw9R3HJ7URwlMlJCQQGBhIcHAwzZs3Z/To0eTnVzNtbR1JInAXWhslgvh+oBSdWobSyM/KSheuHvp448dsyNrAU32follQM3MP3v8+GPQMpHwOX98lyUB4vO+++478/HzWrVvH2rVreemll0w7tmmJQCnVVilVpJT6tNKyQUqpLUqpQqXUfKVUfKV1Sin1ilLqsP31qpIpqU4texfkH4D4/gD4WC30iG/ssiWCzYc38866dxiaMJSLEy92zEnOfRAGPgUb/gvf3ivjEgmv0Lx5cy666CLWrVtn2jHNHGLibWBlxQelVFNgLnAr8B0wAZgN9LVvMga4AkgGNPArsAt4z8SYPMeexcZPeyIA6J0QwevztpFTWEJ4ozN0zmpAxeXFPLH4CRoHNObJPk869mTn/csoDSx4CSxWGDYJLFLQFfXzyopX2JK9xaHnaB/Rnkd7P1rr/TIyMvjxxx8ZOHCgabGY8hejlBoB5AC/VVp8JZCqtf5ca10EjAeSlVIVw2WOAiZqrTO01pnARGC0GfF4pLSlEBQJTdseX9QrMQKtYdWeI04M7GRvr32bHTk7eLbfs4QHhDv+hOc/aiSENTPghwelZCA80hVXXEFISAixsbFERUXx7LPPmnbsepcIlFKhwHPAIOCWSqs6AesrPmitC5RSO+3Lt1Rdb3/fqb7xeKy0pcfbByp0jQ3Hz2ph5Z5sBnc0uQ6+jlYfWM201Glc3e5qzo05t2FOqhRc8KRRMlj8ulEyuOS1E/6thKiNutypO9rXX3/N4MGDWbhwISNHjuTQoUOEh4ebcmwzSgQTgKla671VlgcDuVWW5QIhp1ifCwSfqp1AKTVGKbVKKbUqKyvLhLDdSE465KafUC0EEOBrJSkmzGV6GBeUFvDk4ieJDo7m4Z4PN+zJlYJBT0O/+2Dlh/DTY0YDuxAe5vzzz2f06NE8/LB5f2P1KhEopboCg4Fu1azOB0KrLAsF8k6xPhTI16cYSU1rPQWYAtCzZ0/v+gtPW2r8jO930qreiRFMWbSLwpIyGvk5d1Txf6/8N/vy9zH94ul/TzTTkJSCC58DbYNlk0FZjXkNpGQgPMzYsWNJSEhg3bp1dO3atd7Hq2+JYACQAKQrpfYDDwPDlVJrgFSMhmAAlFJBQGv7cqqut79PRZwsbYkx+FrUyTVnvRIjKLNp1qbnNHxclVTMMXBT55voFlXdfUEDUQqGPA997oQ/34Zfx0nJQHicyMhIbrzxRiZMmGDK8ep7CzkF+G+lzw9jJIY77Z//rZQaDvwAPA1s0FpXNMXPAB5USv0P46mhh4C36hmPZ9qzBOL6Vfs0TI/4xliU0bGsf5umTgjOmHZywp8TaBPe5vRzDDQUpWDoS8aczkvfAouP0edASgbCTe3Zs+ekZe+++65px69XItBaFwLHx0JWSuUDRVrrLPvn4cBk4FNgOTCi0u7vA62AFPvnD+3LRGV5+yF7J/QYXe3q0ABfOrQIdWp/gvc2vMf+gv3MuHjGmecYaChKwSX/Bl1uzHJm8TEalCUZCHESUyuVtdbjq3yeB1Q7u7q9LeAR+0ucSkX7QEL/U27SOzGCWSvSKSmz4efTsM/Qbz+ynU9SP+Gfbf7p3Cqh6igFl0w0niZa9G8jGQx4zNlRCeFypOeNq0tbAr5B0Dz5lJv0ToigqNRGSmbVh7QcS2vN838+T5BfEA/0eKBBz11jFgsMewO6Xm90OlvwirQZCFGFJAJXl7YU4vqA9dSFt16JxgB0DT3u0Lc7v2XNwTU80P0BGgc0btBz14rFApe9CckjYcGL8M09MrmNEJVIInBlhdlwcNNJ/QeqahrsT6vIoAZtJ8gtzuX11a+THJnMP9v+s8HOW2cWK1z+ttELed2nMP0yyPey/ijitNxtDvBTsdWhZ70kAld2vP/A6RMBQJ/ECFbuyabc1jC/zJPWTCK3OJdxfcdhUW7ya2SxwAVPwFUfwV/r4IMLYH/KGXcTni8gIIDDhw+7dTLQWlNSUkJmZiZBQUG12te5PZDE6aUtBZ8AiO5+xk17JUQwa8Vetu7Po2PLqv34zLUhawNfbPuC6zteX/NJ6F1J5+EQ0QpmjYSpF8GV70OHS50dlXCimJgYMjIycPdRC3x8fAgLC6Np09o9Si6JwJWlLYaYXuDjf8ZNe1dqJ3BkIiizlfH8n88TGRjpGn0G6qplNxgzH/47EmZfDxc8Bec9LI+XeilfX18SExOdHYbTuEmZ3gsV5RrVFtUMK1GdmMaNaBkW4PB2gtlbZ7M5ezOP9H6EIN/aFT9dTkhzGP0/SPo/mP+8MQ9y6TFnRyVEg5NE4Kr2rjDGzKlhIgCjVLBiT7bD6jmzCrOYvHYy/Vv2Z0j8EIeco8H5BsA/34fB42HjXPj4Yji6z9lRCdGgJBG4qj2LjQ5QMb1rvEuvxAiy8orZc7jwzBvXwb9X/puS8hKe6POEefMPuwKl4JwH4NpZcGg7TLkAMlY7OyohGowkAleVthRadge/mo/i2aeincAB1UPL9i3jxz0/cmuXW4kLjTP9+C7hrIvhll/Bx88oGWz43NkRCdEgJBG4opJC2LemVtVCAK0jg4kI8jN9foKS8hJeXP4icSFx3NzlZlOP7XKadYTbFkBMT5h7K8x7VmY8Ex5PEoErylhhjJyZcE6tdlNK0Suhsek9jD/e+DF7ju7hyT5P4m898xNMbi+oCdzwNXQfZcx4Nvt6KM47425CuCtJBK4obSkoC8TWvH2gQq+ECNKzC9mfW2RKKHvz9vJBygcMiR9Cv+jalVDcmo8fXDoJLn4Vtv0EU4fAkT3OjkoIh5BE4IrSlkLzLsZkNLXUJ7EJACtMKBVorXlp+UtYlZVHennhILFKQZ/b4fov4GgmfDDQmBtCCA8jicDVlBVDxkqIr121UIUOLUII8rOyYvfheofyW/pv/JH5B3d3vZtmQc3qfTy31Xog3Po7BEbAjMth9XRnRySEqSQRuJrMNVBWVOuG4go+Vgs9EiJYuftIvcIoKivi1ZWv0q5xO0Z2GFmvY3mEpm3g1nmQeB58dx/8+BiUlzk7KiFMIYnA1aQtNn7GnV3nQ/ROaMzWA3kcKSip8zGmp07nr4K/eKz3Y/hYZCQSAALDYeQc6HsXLH8XProIDsg028L9SSJwNWlLIaqj8eRKHfW2txOsSqtbqeBAwQGmbpzKhfEX0qt5rzrH4ZGsPsZ8yMOnGo3H759nPGIqQ1MINyaJwJWUl0H68jpXC1VIignDz2qpczvBG2veoNxWzoM9HqxXHB6ty1Vwz0pjnKLFr8O7/WDXQmdHJUSdSCJwJX+th9KCeieCAF8rXWPDWbGn9iWC9Vnr+X7X94zqNIqYkJh6xeHxGkXAFe/Ajd8Y01/OuAy+vtuYUEgINyKJwJWk2R9NrMFENGfSK7ExGzNzKSiueYOmTdt4ZcUrRAZGcmuXW+sdg9doNQDuWmaMV7R+FkzuBSlfyNzIwm1IInAlaUshorUxPHI99U5sQrlNszY9p8b7fL/re1IOpTC2x1ga+dZ8jCMB+AYaI5jevhDC44whrWdeDUfSnB2ZEGckicBV2MohfSkk1L80ANA9LhyLosbtBIWlhbyx+g26NO3CsFbDTInBKzXvYjxmOvQVI7G/0xeWTpZHTYVLk0TgKg5uMiajMaFaCCAkwJdOLcNq3MP4w5QPyTqWxaO9H3WfOYhdlcUKfe+Au5dDwrnwy5Pw4SCjDUgIFyR/8a7i+ET15o3n0yshgrXpORSXlZ92u4y8DKanTmdYq2EkRyabdn6vFx4LI2fDVR8bk91MuQB+GWeMLiuEC5FE4Cr2LIawOKN+2SS9EyMoLrOxMTP3tNu9vvp1rBYrY7uPNe3cwk4p6Hwl3LMCul0HS980qot2/ObsyIQ4ThKBK9DaKBGYWBoA6JXQGOC08xOs3L+SX9N+5ZbOt3j3eEKOFtgYLnsLRv8AVl/49EqYOwYKDjk7MiEkEbiEQ9ug8JDpiaBJsD9tooJPOWNZua2cV1a8QsuglozqNMrUc4tTSDgH7lgC5/0LNn5pPGq6bpY8aiqcShKBK6joP1DLiWhqoldCBKv2HKHcdvIXzdwdc9l6ZCsP9HyAAJ8A088tTsE3AAY+Bbf/AU3awNd3wCdXQPYuZ0cmvJQkAleQthSCm0FEK9MP3ScxgrziMrbsP3rC8qMlR5m8djLdo7pzUfxFpp9X1ECzjnDzz3DJa5CxGt7pB4vfgPJSZ0cmvEy9E4FSyl8pNVUplaaUylNKrVVKXVxp/SCl1BalVKFSar5SKr7SOqWUekUpddj+elUppeobk1vR2pjsJL6f0bBosl72Ce1XVKkeen/9+xwpOsJjvR/D2/7JXYrFAr1vMx41bT0Q5j1jPF20Z4lUF4kGY0aJwAfYC5wPhAHjgDlKqQSlVFNgrn1ZBLAKmF1p3zHAFUAykAQMA243ISb3cWQP5O0zrf9AVdHhgUSHB54wj/Hu3N18tvkzrmx7JR2adHDIeUUthUXDtZ/B/31qtBdNu8Toe7BxrnRGEw5X70SgtS7QWo/XWu/RWtu01t8Du4EewJVAqtb6c611ETAeSFZKtbfvPgqYqLXO0FpnAhOB0fWNya0c7z/gmEQAxmOkK3Zno+13mK+teg1/H3/u6XaPw84p6qjDpXDvaqO66NgR+OImeLMbLHsHivOcHZ3wUKa3ESilmgHtgFSgE3C8O6XWugDYaV9O1fX2952ohlJqjFJqlVJqVVZWltlhO0/aEuPRwsj2Z962jnonRnAov4TdhwpYnLmYRRmLuCPpDpoGNnXYOUU9+AUZ1UX3rIL/m2mUFn5+HF7vZHRIy810doTCw5iaCJRSvsBMYLrWegsQDFTtzZQLhNjfV12fCwRX106gtZ6ite6pte4ZGRlpZtjOlbbEKA1YHNdu3yvBaCdYsnMfLy1/ibiQOK7rcJ3DzidMYrFCh2Fw80/GnMltBsKyyTApCb68TYasEKYxbQ5CpZQF+AQoASrqHPKB0CqbhgJ5p1gfCuRr7SWtZLmZRhtB7zEOPU3ryCCaBPkxe+dU0svSmTpkKr5WX4eeU5gspgdcPc0YzXT5e7BmBqTMMcYy6ncvtLnQoTcTwrOZ8ptjv4OfCjQDhmutK55/S8VoCK7YLghobV9+0nr7e++ZBNYB4wtVRylF+/gc0kp/5Op2V9O7RW+Hnk84UON4Y6rMB1LhwueMvgefXQPv9IHV06C0yNkRCjdk1i3Eu0AH4FKtdeXJW78COiulhiulAoCngQ32aiOAGcCDSqlopVRL4CFgmkkxub60JeAXAs2THHqakvISMn2mYSsL5do2dzr0XKKBBIZD//vh/vVw5QfgEwDf3Q//6QQLXpahK0StmNGPIB7jkc+uwH6lVL79dZ3WOgsYDrwAHAH6ACMq7f4+8B2QAmwEfrAv8w5pSyCur1EX7EBTNkzhcGk6RX9dyabMEoeeSzQwqy8kXQO3L4JR30F0D1jwkpEQvrsfsrY5O0LhBurdRqC1TgNO2SNJaz0PqPaRGHtbwCP2l3fJzzLGGEq+1qGn2Zq9lakpUxnW6lK+29WJFbuzubxrtEPPKZxAKUg8z3hlbYVlbxtjGK2eBu2Gwtn3GEOYSOdBUQ1pXXKWdHv7gAPGF6pQaitl3JJxhPmH8VjvR+kR3/ikHsbCA0WeBZe9abQjnP8YZKyE6cNgyvmw4XMZwkKcRBKBs+z+A3wCoUVXh51ieup0Nmdv5qm+TxHmH0bvxAi2H8wnu0Cqh7xCcCRc8LiREC6dBKXHYO6tMCkZlrxpzIgnBJIInCP/IKz7DNpdBD5+DjnFrpxdvLvuXYbED2Fw/GDA6FgGnDDchPACvoHQYzTctRxGzjEGN/x1nNFB7acnICfd2REKJ5NE4AwLXobyYhg4ziGHL7eV8/TSpwn0DeTxPo8fX54UE4afj+WU8xMID2exGDcfo7+HMQvhrIthxfswqSt8PtoYAVV4JUkEDS1rm9GA1+MmaNrGIaf4bMtnrM9az2O9HzthGAl/HyvdYsP5dfOBM85jLDxcy64w/APj8dOz7zamzvxwIEy9yGhoPrhFRj/1IpIIGtpvz4JvIzj/UYccfu/Rvby55k3OjzmffyT+46T1dwxoTdrhQt5bIJOgCCAsBoZMgAc3wUUvwbFs+PkJo4PafzrDN/dA6lfGAHjCY5k2xISogbRlsOV7Y3aqYPPHS7JpG+OXjcfH4sO4vuOqnWfggrOiGJbUgrfn72BYcgtaRwabHodwQ/4hcPZdxisnHXb+bpQSNn0Laz8BZTH6KLQeBG0GQcvuYJWvD0+h3HFYn549e+pVq1Y5O4za0RqmXgi5GXDvGvBrZPop5mydw4Q/JzD+7PEMbzf8lNsdzCti8MSFdGwZyqzb+srENOLUyssgczXs/M1IDPvWgLZBQBgknm8khdaDIDzW2ZGKmqn2j11SekPZ9I3xPPdlkx2SBPYX7Of11a/Tp0Ufrmx75Wm3jQoJ4LGLO/DEVyl8vjqDa3rKH7E4BasPxPUxXhc8AYXZsGuBPTH8Dpu/NbZr2u7v0kJ8f4f8jgvHkRJBQygrgbd7G+PB3LnE9CEltNbc9dtdrD6wmrmXzSUmJOaM+9hsmmveX8aOrHx+e/B8mgT7mxqT8AJaG72YK0oLaUugrAis/hB/tpEYWg+EZp2kR7PrqPY/QhqLG8Lqj+HIbmO0SAeMK/T9ru9ZnLmY+7vfX6MkAGCxKF66sgsFxWU8/8Nm02MSXkApiGpvPHV0w1x4dA9cP9eYVCfvgNFX4b3+MLE9fHUnpHwBBYedHbWohpQIHK0o15hqsFknuPFb0++MDh07xOVfX07r8NZMGzoNi6pdbp/4y1be+n0Hn9zSm3PbetCEP8L5cjONRuedv8HO+VCUAyjj0dWKaqSYXsbAeaKhVPsFJInA0eY9C4tfhzELoGU30w//wPwHWJSxiC8u+4LEsMRa719UWs7Fk/7ApjU/jz2PAF/HjoQqvJStHPat/ftppIyVoMuNYdgTzzNmX2s9CCJq/zssakUaixtcbib8+Q50ucYhSeCXPb8wL30eD/R4oE5JACDA18oLV3Rm5IfLefO37Twy1HFzJwsvZrFCTE/jdf4jcCwHdi/6u9F56w/GdhGt/i4tJJwL/vJ4c0OQEoEjfX0XpHxuTELeON7UQ+cU5XD5N5fTPKg5My+ZiY+lfjn9oTnr+WZdJt/fdw7tm1edXVQIB9IaDu8wSgo7f4M9i6G0ECy+xnwdrQcaiaFZF5mOs/6kaqhB7d8I750D/e6BIc+bfvjH/3icn/b8xOxhs2nXuF29j5ddUMKgiQtIbBrEF3f0w2KRpzyEk5QVQ/oyezXS73AgxVgeFAmtLrD3XRgIwVHOjdM9SdVQg5r3jNHp5tyHTD/0wr0L+X7X99yZfKcpSQAgIsiPp/7RkYc+X8/MFenc0NfcEowQNebjD60GGK8Ln4O8/UZj8057iSFljrFd8y5GNVJML2jSGhonGCOtilqTEoEj7JwPn1xhlAT63WvqodcdXMf98+8nIiCCOcPm4GviExdaa66fupwNe3OZ99D5NAsNMO3YQpjCZoP96+3VSL/D3uVgK/t7fWiM0eAc0cp4NWlt/GycKJ3cDFI11CBsNphynvHY6D2rjLsbE2it+WzLZ7y28jVaBLdg8sDJtApvZcqxK9t9qICL3ljE4A5RvHNdD9OPL4SpivOMKV+zd8PhnZC9y/7aCYVV+iyEtLQniMS/E0RFkvCeRmmpGmoQKXNgfwpc+aFpSaCwtJDxy8bz4+4fGRA7gBfOeYFQP8c06CY2DeK+gW147Zdt/Lb5AIM6NHPIeYQwhX+IMRhedDU3LcdyjI6ch3caiaIiQWz7CQqyTtw2uHmlUkSrv99HtDLO4eGkRGCm0iJ4qwcENYXb5pvyhMPu3N08uOBBduXu4t5u93Jz55tr3WmstkrKbAx76w8Kisv55YHzCPKX+wXhYYqOVkoSu+yJwv4+/8CJ2wZFnSJJtIYAt3vCTkoEDrfifTiaAf9815Qk8Gvar4xbMg4/ix/vX/g+fVv0NSHIM/PzsfDiP7tw1XvLePa7VCZc0Rl/H+loJjxIQCi0SDZeVRXnV0kS9teu+bD+sxO3bdT0xLaIyq/A8Aa5FDNIicAshdnGlH9xfeC6z+t1qDJbGW+sfoPpm6aT1DSJiQMm0jyouTlx1sILP2zigz92k9g0iHHDOjCwvVQTCS9XUgBH9pycJLJ3wdHME7cNjKgmQbQ22igaRTglfKSx2MF+fMwoEdyxBJp1rPNhDh07xMMLH2b1gdX831n/xyO9HsHP6pgJ7mti/taDTPh+E7uyChhwViTjhnWUyWyEqE7psWqShL19IjcDqPRdGxBeJUlUet8owpGjtUoicIhD2+HXp2Hr/6D7jXDZW3U+1JoDa3h44cPkleTx9NlPc2nrS00MtO5KymzMWLaHSfO2c6y0nNH9ErhvcFtCA2SwMCFqpLTISBInlCLsCSNnLycmibBqShH290FN65skJBGYquAwLHwZVn0EPoFw7gPQ927wrf2z91prPtn0Ca+vfp3o4Gj+c8F/TOsoZqasvGJe+3krc1bvpUmQH/+66Cyu7hErvZCFqI+yYjiSdookkW7MCFfBPxTGptSn/UESgSlKi4wqoEUToSQPeoyGAU/UeQ7igtICnln6DD/v+ZmBsQN5/pznCfFz7cfVUjJyGf9dKqvTjtAlOozxl3WkR7zT6jyF8FxlJUYyqEgQuXuNjqp1LxVIIqgXrSF1Lswbb/zHtB0CF04wJuaoo105uxi7YCxpR9O4v/v93NTpJreZP1hrzbfr9/HS/7aw/2gRV3RtycMXnUVMY+m9KYQLk0RQZ3tXwM9PGGOoN+tsZOTWF9TpUMXlxWw8tJFV+1fx0caPCPAJ4NXzXqVPiz4mB90wCorLeHfBTqb8sYuSMhstwwJIjg0nOTacpJgwukSHESJtCUK4CkkEtVJSYIwg+uc7sOlro+fhwKeg68haTTeZXZTN2oNrWXdwHWsPriX1cCpl9rFRejXvxYvnvOiUR0PNtje7kJ9T97M+I5cNGTmkHS4EjBJs68hgkmPCSY4NIzkmnPYtQqRfghDO4ZqJQCkVAUwFhgCHgMe11p+dbh9TE4HWRk/C/Smwf4Px5b8/xRgfHQ2+jaDffcbgcWcYj0Rrze6ju49/6a87uI49R/cA4GvxpVOTTnSL6ka3qG50jepK44DG5lyDCzpSUML6jBw2ZOSyfm8O6zNyOJRfAoCf1UKHFiF0bBlKkyB/whv50riRHxFBfsffNw7yIzTAx22qyoRwEy6bCGYBFuAWoCvwA9BPa516qn3qnAg2zIGcNMjPgoKDkH/QGLCq8rgj4fHoZp0pb96ZsmYdKWrehaM+vuSV5JFbkkteSR5HS44aP4uPHv+cW5zLluwtHCk+YhzGP5yukV3pGtWV7s2607FJR/yt5ow95I601uzLLTqeFNbvzWH7gXyOFJZgO8WvoNWiCA/0JbyRrz1J+NG4kS+Bvlb8fCzGy1rpvY8Ff6ul0jrLCesqPlsUWJTCohRWi0Ip41wVyyz2z8q+vur2FoUkKOGuXC8RKKWCgCNAZ631NvuyT4BMrfVjp9qvrong3qldSbMVYbNYKLdYsSkrZRbjVaoUZWjKbGWU2crQnPnfxUf5EOofSqhfKCF+IbQKa2Xc8TfrRmJoonxZ1IDNpskrKuNIYcnfr4JSjhSWkFNYSnZhCTlVlh0rLaekzEZJuY3yU2URB1PK+ItSSh3/yzKWGStUpc+VtwX7X2Ll/ascS6njW51wvuPvq4nl73Wq2uVV9zvd72ZNfm1rtE313zl1OM6ZjuFdf2c/jT23PlWrLjnWUDugvCIJ2K0Hzq+6oVJqDDAGIC4urk4ni+1wJX7FR7BafLFYLFiVFauy4mvxxcfig4/F5/j7ip8BPgGE+v39ZR/iF3L8faBPoNf9EprNYlGENfIlrJEvCQTVev9ymzaSQpmN4vLy4+9Lym1/vy+zUWz/XFpuw6aNEkq5TWPTRjKyaU25rvLZptEa+3JtX459P2NdxQ2D8Z6/lx3/rE9YB8b64++1PmG/im0rO/FeTZ9y3QnvT7fdaY5fkxsgkzahJjehZ9rCDZs4660mCbbWx3RyieBc4HOtdfNKy24DrtNaDzjVfi7RoUwIIdxPtVnE2TNB5wNVx3ENBfKcEIsQQnglZyeCbYCPUqptpWXJwCkbioUQQpjLqYlAa10AzAWeU0oFKaX6A5cDnzgzLiGE8CbOLhEA3AUEAgeBWcCdp3t0VAghhLmc/dQQWuts4ApnxyGEEN7KFUoEQgghnEgSgRBCeDlJBEII4eWcPtZQXSilsoC0Ou7eFGNwO28i1+wdvO2ave16of7XfEhrPbTqQrdMBPWhlFqlte7p7Dgaklyzd/C2a/a26wXHXbNUDQkhhJeTRCCEEF7OGxPBFGcH4ARyzd7B267Z264XHHTNXtdGIIQQ4kTeWCIQQghRiSQCIYTwcpIIhBDCy3lNIlBKRSilvlJKFSil0pRSI50dk5mUUv5Kqan2a8tTSq1VSl1caf0gpdQWpVShUmq+UiremfGaTSnVVilVpJT6tNIyj71mpdQIpdRm++/zTvtsfx57zUqpBKXU/5RSR5RS+5VSk5VSPvZ1bn/NSql7lFKrlFLFSqlpVdad8vqU4RWl1GH761VVh/lzvSYRAG8DJUAz4DrgXaVUJ+eGZCofYC/GfM9hwDhgjv0PqCnGvA/jgAhgFTDbWYE6yNvAyooPnnzNSqkLgVeAm4AQ4DxglydfM/AOxlD1LYCuGL/nd3nQNe8Dngc+qrywBtc3BmP05mQgCRgG3F7rsxuTa3v2CwjCSALtKi37BHjZ2bE5+Lo3AMPtvyxLq/x7HAPaOztGk65zBDAHGA98al/msdcMLAVuqWa5J1/zZuCSSp//DbzvadeMkQym1fT/1P67MKbS+luAP2t7Xm8pEbQDyrXW2yotWw94UongBEqpZhjXnYpxnesr1mljZrideMD1K6VCgeeAh6qs8shrVkpZgZ5ApFJqh1Iqw15NEoiHXrPdJGCEUqqRUioauBj4Cc++Zjjz9Z2wnjp+r3lLIggGcqssy8UoVnscpZQvMBOYrrXegmdf/wRgqtZ6b5XlnnrNzQBf4CrgXIxqkm7AU3juNQMsxPiCOwpkYFSRfI1nXzOc+fqqrs8FgmvbTuAtiSAfCK2yLBTIc0IsDqWUsmBUe5UA99gXe+T1K6W6AoOB/1Sz2iOvGaNaAOAtrfVfWutDwOvAJXjoNdt/p3/GqCsPwhiBszFGO4lHXnMlZ7q+qutDgXxtryeqKW9JBNsAH6VU20rLkjGqTTyG/S5gKsZd43Ctdal9VSrG9VZsFwS0xv2vfwCQAKQrpfYDDwPDlVJr8NBr1lofwbgjru4P3SOvGaORNBaYrLUu1lofBj7GSH6ees0VznR9J6ynrt9rzm4cacBGmP8CszDuKPpjFKE6OTsuk6/xPeBPILjK8kj79Q4HAjDupGrdoORqL6AR0LzS6zXgC/v1euQ126/7OYwnpKIw7oz/wKgi8+Rr3gU8hvF0XDjwFUb1p0dcs/26AoCXMEr0AfZlp70+4A6MhvRooKU9CdxR6/M7+x+gAf+hIzDqFAuAdGCks2My+friMe4SizCKixWv6+zrBwNbMKoWFgAJzo7ZAf8G47E/NeTJ14zRRvAOkAPsB94EAjz8mrvar+cIxsQsnwNRnnLN9t9dXeU1/kzXByjgVSDb/noV+xhytXnJoHNCCOHlvKWNQAghxClIIhBCCC8niUAIIbycJAIhhPBykgiEEMLLSSIQQggvJ4lAiFpQSo1WSuU7Ow4hzCT9CISwU0qd6Y9hOnAnEKK1PtgAIQnRIHycHYAQLqRFpffDgA+qLDumtT7G3wO/CeERpGpICDut9f6KF8bwDScs01rnVq0aUkqNV0ptVEqNUkrtUUrlK6U+Vkr5KaXuUkrttU8h+Lp9FM2K/fzsUwxm2KebXKmUuqjhr1oIKREIYYYE4HKMUkQ08CXGIHj7gSFAe4wZ1JbY14ExemZrYCTGaKKXAN8ppXpprStPNCKEw0kiEKL+rMBNWutcYKNS6ieMOXWjtdYlwGal1BLgAuBLpVRr4FqMwcPS7ceYrJQajDHf7F0NfwnCm0kiEKL+0u1JoMIBYJs9CVReFmV/3x1j1MhNVSaS8gd+d2SgQlRHEoEQ9Vda5bM+xTKr/b3F/rlXNdtJQ7RocJIIhGh4azFKBM211vOdHYwQkgiEaGBa621KqZnANKXUQ8AajImTBgC7tNZznRmf8D6SCIRwjpuAJzFmlIrBmF1qBSAlBNHgpGexEEJ4OelQJoQQXk4SgRBCeDlJBEII4eUkEQghhJeTRCCEEF5OEoEQQng5SQRCCOHlJBEIIYSX+3/gNyAgfZ84BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OPTIONAL to get the same colors for S, I, R plots over all the notebook, we can define a dictionary of colors\n",
    "SIR_colors = {\n",
    "    \"S\": \"#8aceff\",\n",
    "    \"I\": \"#e35dbf\",\n",
    "    \"R\": \"#ace08b\"\n",
    "}\n",
    "\n",
    "# plot solution (S, I, R) on y axis, time on x-axis\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(t_range, S, label=\"S\")\n",
    "ax.plot(t_range, I, label=\"I\")\n",
    "ax.plot(t_range, R, label=\"R\")\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il grafico mostra un picco degli infetti, a seguito del quale aumenta la parte di popolazione che non può più essere infettata mentre la parte di popolazione che può ancora essere infettata tende a zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SIR model on a network\n",
    "The ODE approximation of the SIR model is not very realistic because it assumes a fully mixed population.\n",
    "The fact that we do not interact randomly with one another, but rather have complex social structures, schools, commuting etc is what makes the whole thing much more difficult.\n",
    "\n",
    "To explorer the importance of the **contact network** for the spread of a disease, we will implement a simple discrete-time SIR model that runs on an arbitrary network or graph. To do so, we will define a class `SIRonGraph` that holds together the **graph** where the SIR model runs and the **methods** (aka functions) that implement the SIR dynamics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the `networkx` module, a well-known python library to work with networks data. To check if you have it installed in your computer, run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a `ModuleNotFoundError`, that means that you need to install it before continuing. In most cases, simply opening a terminal and running\n",
    "\n",
    "```bash\n",
    "> pip install networkx\n",
    "```\n",
    "\n",
    "should work. Additional installation instructions can be found [here](https://networkx.org/documentation/stable/install.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Networkx provides you the `Graph` type, that is, a kind of variable that represents a graph or network. Let us try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Networks have nodes and edges. Let us add some. Here we use letters to label the nodes, but integers would work as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from([\"A\", \"B\", \"C\", \"D\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"A\"), (\"C\", \"D\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to have a look at our network. Networkx gives us some handy functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw_shell(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, networks can also hold some data on their nodes on edges. These are called **attributes** and are very useful. Let us create one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_status = {\n",
    "    \"A\": \"S\",\n",
    "    \"B\": \"S\",\n",
    "    \"C\": \"I\",\n",
    "    \"D\": \"S\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(G, initial_status, name=\"kind\")\n",
    "#modo che ho di attribuire delle proprietà ai nodi di un grafico - dopo aver già definito la \"carica\" di quella proprietà per il singolo grafico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now G carries the attribute *kind* with it. We can get the values of attributes of individual nodes easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes[\"C\"][\"kind\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also modify the value of an attribute. Let us suppose node C infects node B. The we could do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.nodes[\"B\"][\"kind\"] = \"I\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we need to get the attribute of all nodes, we can do it at once as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if G.nodes[\"B\"][\"kind\"] == \"I\":\n",
    "    print(\"yes\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_status = nx.get_node_attributes(G, \"kind\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 'S', 'B': 'I', 'C': 'I', 'D': 'S'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The SIR model as a python class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create a class `SIRonGraph` to represent a SIR model on an arbitrary network. A class holds together data and functions (callled methods), and is exactly what we need in this case: a network and some functions that implement the SIR dynamics on it.\n",
    "\n",
    "Classes can be a bit confusing at the beginning, but all you need to understand is that `self` means literary 'myself', that is, methods (functions) get as first argument the object they are acting on. This means that we can \"store\" a variable in one method and \"retrieve\" it in another! No need to pass it around!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the following example code and play with it to make sure you understand how `self` works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    # this is a special method that gets called when you create an instance of your class\n",
    "    def __init__(self, name): #gli argomenti del metodo sono io e il nome che voglio assegnare\n",
    "        self.name = name #\n",
    "    \n",
    "    def set_x(self, x): #gli argomenti del metodo sono io e il valore che voglio assegnare alla mia x\n",
    "        self.x = x #la mia x vale x\n",
    "        \n",
    "    def increment_x(self):\n",
    "        self.x += 1\n",
    "\n",
    "    def show_x(self):\n",
    "        return self.x\n",
    "    \n",
    "#negli esempi di seguono utilizzo la classe che ho definito qui    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = Model(name=\"my first model\")\n",
    "model2 = Model(name=\"another model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.set_x(3)\n",
    "model2.set_x(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.increment_x()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.show_x()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.show_x()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access information stored in our objects. Notice that our models had a name that was set at initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my first model'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let us go back to our SIR model. The input to create a new SIR model on an arbitrary network is the following:\n",
    "\n",
    "1. The graph onto which we want to run the SIR model.\n",
    "2. A list of initially infected nodes.\n",
    "3. The coefficient $\\beta$\n",
    "4. The coefficient $\\gamma$\n",
    "\n",
    "So, we create a class `SIRonGraph` that, when initialized, simply stores the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRonGraph:\n",
    "    def __init__(self,\n",
    "                 #devo essere un argomento dell'inizializzazione\n",
    "                 network: nx.Graph, \n",
    "                 initial_infected: List[int], \n",
    "                 beta: float, \n",
    "                 gamma: float) -> None:\n",
    "        #ho definito quali parametri devo passare come argomento per creare la classe\n",
    "        \n",
    "        \"\"\"\n",
    "        Initialize a SIR model on a network. \n",
    "        \"\"\"\n",
    "        \n",
    "        # store parameters\n",
    "        self.beta = beta\n",
    "        self.gamma = gamma\n",
    "        self.network = network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try it. First we need to create a graph! Networkx has some functions to generate standard graphs. We could start with a lattice graph, for instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 rows, 3 columns\n",
    "G = nx.hexagonal_lattice_graph(2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that nodes are labelled with a tuple of integers `(col, row)` and that `G` comes with a nice `pos` node attribute, which we can use to nicely plot the network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le coordinate con cui sono individuati i singoli nodi sono sostanzialmente (y,x) con gli assi orientati come in un normale piano cartesiano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n",
    "ax.set_aspect(1)\n",
    "ax.set_axis_off()\n",
    "\n",
    "pos = nx.get_node_attributes(G, 'pos')\n",
    "nx.draw_networkx(G, pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create our first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SIRonGraph(network=G, beta=0.3, gamma=0.1, initial_infected=[(1, 3)]) #ho un solo infetto all'inizio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that you can retrieve all the information that is stored in `self`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x243b98878b0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `model.network` is not a copy of `G`: it is exactly the same object, point to the same memory address!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x243b98878b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.3\n",
    "Our model should do a few more things at initialization:\n",
    "1. Checking that $\\beta$ and $\\gamma$ have sensible values.\n",
    "2. Making sure that all elements in input list of inital infected are really nodes of the network.\n",
    "2. Creating a node attribute called \"kind\" with value \"S\" for all nodes except for those in the list of `inital_infected`\n",
    "\n",
    "Extend the SIRonGraph code below and implement points 1 and 2 in the ``__init__`` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRonGraph:\n",
    "    def __init__(self, network: nx.Graph, initial_infected: List[int], beta: float, gamma: float) -> None:\n",
    "        \"\"\"Initialize a SIR model on a network. \n",
    "        All nodes are set to S except those passed in the initial_infected list.\n",
    "        \"\"\"\n",
    "        # make sure node in initial_infected list are really nodes of the network.\n",
    "        pos=np.array(network.nodes)\n",
    "        for i in range(len(initial_infected)):\n",
    "            assert any(pos[:,0]==initial_infected[i][0]) and any(pos[:,1]==initial_infected[i][1])\n",
    "        \n",
    "        # set initial status of nodes as node att\n",
    "        for node in network.nodes:\n",
    "            nx.set_node_attributes(G, {node:\"S\"}, name=\"kind\")\n",
    "            nx.set_node_attributes(G, {node:\"I\"}, name=\"kind\")\n",
    "\n",
    "        # make sure params make sense \n",
    "        assert beta>0 and beta<1\n",
    "        assert gamma>0 and gamma<1\n",
    "        \n",
    "        # store parameters\n",
    "        self.network = network\n",
    "        self.beta = beta\n",
    "        self.gamma = gamma\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.4\n",
    "1. Create a function to visualize a hexagonal lattice model, with nodes colored by type. Your function should take as input the model instance (object of kind SIRonGraph) and display the network, coloring S, I and R nodes with different colors.\n",
    "2. Show that your function works for a few examples with different input graph sizes and different sets of initially infected nodes.\n",
    "\n",
    "**Tip**: look into the networkx documentation! It is [here](https://networkx.org/documentation/stable/reference/drawing.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-35-c33ddf7e80fe>, line 17)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-35-c33ddf7e80fe>\"\u001b[1;36m, line \u001b[1;32m17\u001b[0m\n\u001b[1;33m    for kind in list(kinds.values())\u001b[0m\n\u001b[1;37m                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def show_hex_model(model, ax=None):\n",
    "    # creates a figure if no axis was passed\n",
    "    ax_was_none = ax is None\n",
    "    if ax_was_none:\n",
    "        # create a figure\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "        ax.set_aspect(1)\n",
    "        ax.set_axis_off()\n",
    "        \n",
    "        \n",
    "    # get the positions and kind of nodes\n",
    "    pos = nx.get_node_attributes(model.network, \"pos\")\n",
    "    kinds = nx.get_node_attributes(model.network, \"kind\")\n",
    "\n",
    "    # prepare a list of colors for the nodes using the kinds dict\n",
    "    node_color=[]\n",
    "    for kind in list(kinds.values())\n",
    "        node_color.append(SIR_colors[kind])\n",
    "    '''node_color = [\n",
    "        \"blue\", \"red\", \"green\"\n",
    "    ]\n",
    "\n",
    "    # draw the network\n",
    "    for node in model.network.nodes:\n",
    "        if model.network.nodes[node][\"kind\"]==\"S\":\n",
    "            nx.draw_network_nodes(model.network, pos=pos, nodelist=[node], node_color=node_color[0])\n",
    "        elif model.network.nodes[node][\"kind\"]==\"I\":\n",
    "            nx.draw_network_nodes(model.network, pos=pos, nodelist=[node], node_color=node_color[1])\n",
    "        elif model.network.nodes[node][\"kind\"]==\"R\":\n",
    "            nx.draw_network_nodes(model.network, pos=pos, nodelist=[node], node_color=node_color[2])'''\n",
    "\n",
    "            \n",
    "    # returns the new figure if it was created here\n",
    "    if ax_was_none:\n",
    "        return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'show_hex_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-01e37823576c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mshow_hex_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'show_hex_model' is not defined"
     ]
    }
   ],
   "source": [
    "show_hex_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SIR dynamics as class methods\n",
    "Now we want to be able to run the SIR dynamics on our `SIRonGraph` objects. To simplify things, we  consider **discrete time** simulation, so that at each time-step:\n",
    "+ Infected individuals have a chance to recover, and they do so with probability $\\gamma$.\n",
    "+ Susceptible individuals can be infected by any of the infected individuals to which they are connected. This happens with probability $\\beta$ for each S-I edge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.5\n",
    "Implement the dynamics of the SIR model as methods of your `SIRonGraph` class. You might want to implement two methods separately, as follows:\n",
    "\n",
    "+ `_step(self)`: this method implements one single time-step, as descrived above.\n",
    "+ `run(self, num_timesteps: int)`: this method (for now) simply calls `_step` repeatedly.\n",
    "\n",
    "You will see, in a while, that it is useful to organize things as we are doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-64-6d3cf4fc650a>, line 33)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-64-6d3cf4fc650a>\"\u001b[1;36m, line \u001b[1;32m33\u001b[0m\n\u001b[1;33m    nx.set_node_attributes(self.network, (node:\"R\"), name=\"kind\")\u001b[0m\n\u001b[1;37m                                              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "class SIRonGraph:\n",
    "    def __init__(self, network: nx.Graph, initial_infected: List[int], beta: float, gamma: float) -> None:\n",
    "        \"\"\"Initialize a SIR model on a network. \n",
    "        All nodes are set to S except those passed in the initial_infected list.\n",
    "        \"\"\"\n",
    "        # make sure node in initial_infected list are really nodes of the network.\n",
    "        pos=np.array(network.nodes)\n",
    "        for i in range(len(initial_infected)):\n",
    "            assert any(pos[:,0]==initial_infected[i][0]) and any(pos[:,1]==initial_infected[i][1])\n",
    "        \n",
    "        # set initial status of nodes as node att\n",
    "        for node in model.network.nodes:\n",
    "            nx.set_node_attributes(G, {node:\"S\"}, name=\"kind\")\n",
    "            nx.set_node_attributes(G, {node:\"I\"}, name=\"kind\")\n",
    "\n",
    "        # make sure params make sense \n",
    "        assert beta>0 and beta<1\n",
    "        assert gamma>0 and gamma<1\n",
    "\n",
    "        #ode_result = solve_ivp(odes_SIR, [0,100], y0, args=(beta,gamma))\n",
    "        \n",
    "    def run(self, num_timesteps: int):\n",
    "        \"\"\"Run SIR dynamics for num_timsteps steps\"\"\"\n",
    "        for i in num_timesteps:\n",
    "            self._step()\n",
    "        \n",
    "    def _step(self):\n",
    "        \"\"\"Do one discrete timestep.\"\"\"\n",
    "        # recoveries\n",
    "        # remember: I -> R with prob gamma\n",
    "        for node in self.network.nodes:\n",
    "            if self.network.nodes[node][\"kind\"]=='I' and np.random.rand()<self.gamma:\n",
    "                nx.set_node_attributes(self.network, (node:'R'), name='kind')\n",
    "            \n",
    "            \n",
    "        # infections\n",
    "        # remember: S -> I with prob beta for each edge S-I (or I-S)\n",
    "        for edge in self.network.edges:\n",
    "            ...\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.6\n",
    "Use your visualization function to show the evolution of a SIR model running in a hex lattice. You should decide the system size, number of timesteps and other parameterrs wisely to show that your code works properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.hexagonal_lattice_graph(10, 10)\n",
    "model = SIRonGraph(network=G, initial_infected=[(5, 5)], beta=0.3, gamma=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axx = plt.subplots(2, 3, figsize=(16, 12))\n",
    "axx = axx.reshape(-1)\n",
    "\n",
    "for ax in axx:\n",
    "    ### plot model status on axis ax\n",
    "    ...\n",
    "    \n",
    "    ### run the model for some timesteps\n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing SIR evolution as a class attribute\n",
    "Now our class can run the SIR dynamics, but it doesn't \"remember\" the whole evolution of the simulation: only the final state is available. One way of storing some quantities of interest during the simulation is as follows:\n",
    "\n",
    "1. Create an empty list at initialization.\n",
    "```python\n",
    "class SIRonGraph:\n",
    "     def __init__(self, G: nx.Graph, initial_infected: List[int], beta: float, gamma: float) -> None:\n",
    "         ...\n",
    "         # empty list, we will add info durring the simulation\n",
    "         self.history = []\n",
    "         ...\n",
    "```\n",
    "\n",
    "2. Create a _log() method that, when called, computes the information we want to store and adds it to the self.history list.\n",
    "\n",
    "```python\n",
    "   def _log(self):\n",
    "        # do computations\n",
    "        var1 = ...\n",
    "        var2 = ...\n",
    "        ...\n",
    "        \n",
    "        # create a block of info as list\n",
    "        info = [var1, var2, ...]\n",
    "        # or as a dict\n",
    "        info = {\n",
    "            \"var1\": var1,\n",
    "            \"var2\": var2,\n",
    "            ...\n",
    "        }\n",
    "        # or using any other scheme you like\n",
    "        info = ...\n",
    "        \n",
    "        # add the info block to the history log\n",
    "        self.history.append(info)\n",
    "```\n",
    "\n",
    "3. Call the ._log() method every time you want to store the variables of interest. That could be at every timestep or once in a while only. \n",
    "It is also wise to call `._log()` once at initialization, to store the initial status. \n",
    "```python\n",
    "   def __init__(self, ...):\n",
    "         ...\n",
    "         self._log()\n",
    "        \n",
    "   def run(self, num_timesteps: int):\n",
    "         \"\"\"Run the SIR dynamics for some time.\"\"\"\n",
    "         ...\n",
    "         for _ in range(num_timesteps):\n",
    "              self._step()\n",
    "              self._log()\n",
    "         ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.7\n",
    "1. Modify your SIRonGraph class so that it stores the number of **S**, **I** and **R** individuals at each timestep.\n",
    "2. Simulate the SIR model in a network of your choice, and plot the evolution of the system (S, I, R) over time. Try to choose the parameters wisely, so that a typical epidemic wave can be seen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRonGraph:\n",
    "    def __init__(self, network: nx.Graph, initial_infected: List[int], beta: float, gamma: float) -> None:\n",
    "        \"\"\"Initialize a SIR model on a network. \n",
    "        All nodes are set to S except those passed in the initial_infected list.\n",
    "        \"\"\"\n",
    "        # copy back your code!\n",
    "        ...\n",
    "    \n",
    "        # create empty list to store history        \n",
    "        ...\n",
    "        # store info of initial condition\n",
    "        ...\n",
    "        \n",
    "        \n",
    "    def run(self, num_timesteps: int):\n",
    "        # modify your previous code!\n",
    "        ...\n",
    "    \n",
    "    def _step(self):\n",
    "        \"\"\"Do one discrete timestep.\"\"\"\n",
    "        # copy back your code!\n",
    "        ...\n",
    "        \n",
    "    def _log(self):\n",
    "        ...\n",
    "        ...\n",
    "        info = ...\n",
    "        self.history.append(info)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the model\n",
    "model = SIRonGraph(\n",
    "    network=nx.hexagonal_lattice_graph(30, 20),\n",
    "    initial_infected=[(10, 10), (2, 2), (2, 5)],\n",
    "    beta=...,\n",
    "    gamma=...\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the model for some time\n",
    "model.run(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract evolution of S, I, R from model.history\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot S,I,R vs time\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.8\n",
    "Which kind of network could give results equivalent to the ODE solution of the first part of the notebook? Explain why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.9\n",
    "Compare the ODE solution with simulation results on the appropiate network. Do you see differences? If so, explain why.\n",
    "Remember to properly add labels and legends to all your plots!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define your model\n",
    "model = SIRonGraph(\n",
    "    network=...,\n",
    "    beta=...,\n",
    "    gamma=...,\n",
    "    initial_infected=...\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the model, extrarct results\n",
    "...\n",
    "...\n",
    "...\n",
    "...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optional: maybe more than one simulation and then average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot results togetherr with old ODE results\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your reasoning about why we still see differences herer*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The spread of epidemic disease on networks\n",
    "We will now follow some of the derivations in \n",
    "\n",
    "*Spread of epidemic disease on networks.*  \n",
    "Newman, M.E.J. (2002).  \n",
    "Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 66, 016128.  \n",
    "\n",
    "You can get the preprint version of the paper, which is almost equal to the published verison, here:  \n",
    "[https://arxiv.org/abs/cond-mat/0205009](https://arxiv.org/abs/cond-mat/0205009)  \n",
    "\n",
    "Remember that the *transmissibility* $T$ is the average probability that a victim will infect each of their contacts.\n",
    "In the paper, Newmann computes it as (Eq 6)\n",
    "$$\n",
    "T =  1 - \\int_0^\\infty dr \\sum_{\\tau=0}^{\\infty} P(r) P(\\tau) (1-r)^\\tau\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.10\n",
    "Read the first part of the paper. Under which assumptions is the Eq. 6 valid? What is the meaning of $r$ and $\\tau$ in the manuscript? Can you relate $P(r)$ and $P(\\tau)$ to our coefficients $\\beta$ and $\\gamma$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.11\n",
    "Compute the transmissiblity $T$ of our discrete-time SIR model in terms of $\\beta$ and $\\gamma$ using Newman's calculation (but bear in mind that this result is valid only under the assumptions of the paper)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.12\n",
    "Add a method to your class to compute the transmissibility $T$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRonGraph:\n",
    "    # copy back all your code\n",
    "    ...\n",
    "    ...\n",
    "    ...\n",
    "    \n",
    "    \n",
    "    # add the new method\n",
    "    def get_transmissibility(self):\n",
    "        ...\n",
    "                \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The critical transmissibility $T_c$\n",
    "The critical transmissibility $T_c$ can be defined as the value below which the system displays small outbreaks, and above which we have a global pandemic that spans the whole system. We will now try to see, numerically, that indeed there is a phase transition, and will compare our estimates of $T_c$ with some theoretical calculations from the literature. Notice that Newman explains in the manuscript that the SIR model on a network is equivalent to **bond percolation** with occupation probability $T$. Do you see why? Let us read Newman again:\n",
    "\n",
    "<img src=\"attachment:image.png\" width=\"400\">\n",
    "\n",
    "Therefore, Newman *maps* the SIR-on-a-network problem to a known statistical mechanics problem, and all known result on the later can be translated to the former straightforwardly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.13\n",
    "To study the total size of an epidemic, defined as the total number of individuals ever infected, simulations that run for a pre-fixed number of steps are not very useful, since some outbreaks might take much longer than others to extinguish.\n",
    "\n",
    "Add a new method to your class called `run_till_end(self)` that runs a simulation until no more infected individuals are left. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRonGraph:\n",
    "    # copy back your code\n",
    "    ...\n",
    "    \n",
    "    # add new method\n",
    "    def run_till_end(self):\n",
    "        ...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating realistic networks.\n",
    "Read carefully the part of the paper where it is explained which kind of networks are being used.\n",
    "To reproduce Figure 1, we need to generate networks with the distrirbution of Equation (30) in the paper, \n",
    "\n",
    "<img src=\"attachment:image.png\" width=\"400\">\n",
    "\n",
    "\n",
    "Using the `nx.configuration_model` function, we can generate a network with an arbitrary degree sequence.\n",
    "The following functions will help you generate networks with degree distribution as defined in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_of_k(k, alpha, kappa):\n",
    "    \"\"\"\n",
    "    Power-law of exponent alpha and exponential cuttof at kappa, \n",
    "    as defined in Newman, 2002. Equation 30.\n",
    "    \"\"\"\n",
    "    import mpmath\n",
    "    numerator = k ** (-alpha) * np.exp(-k / kappa)\n",
    "    denominator = mpmath.polylog(s=alpha, z=np.exp(-1 / kappa))\n",
    "    return float(numerator / denominator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_degrees(N, alpha, kappa):\n",
    "    \"\"\"\n",
    "    Sample from a power-law with exponential cuttof\n",
    "    as defined in Newman, 2002, Eq 30.\n",
    "    \"\"\"\n",
    "    # find kmax\n",
    "    for log2_kmax in range(1, 30):\n",
    "        kmax = 2 ** log2_kmax\n",
    "        if p_of_k(kmax, alpha, kappa) < 1e-16:\n",
    "            break\n",
    "    # compute p_k\n",
    "    p_k = np.array([\n",
    "        p_of_k(k, alpha, kappa)\n",
    "        for k in range(1, kmax)\n",
    "    ])\n",
    "    p = p_k / np.sum(p_k)\n",
    "    return np.random.choice(range(1, kmax), p=p, size=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_newman_network(N=1000, alpha=2, kappa=20):\n",
    "    degrees = get_degrees(N=N, alpha=alpha, kappa=kappa)\n",
    "    try:\n",
    "        return nx.configuration_model(degrees, create_using=nx.Graph)\n",
    "    except:\n",
    "        return get_newman_network(N=N, alpha=alpha, kappa=kappa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.14\n",
    "In this final exercise, we are going to reproduce Figure 1 (top plot) of Newman's paper. Before starting, read carefully the paper to understand what is being plotted.\n",
    "\n",
    "\n",
    "<img src=\"attachment:image.png\" width=\"600\">\n",
    "\n",
    "\n",
    "To obtain a similar plot, we will need to do the following: \n",
    "1. Find the right range of $\\beta$ and $\\gamma$ to obtain values of $T$ in the range [0, 0.8]. One trick is to fix $\\gamma$ and play with $\\beta$ only.\n",
    "2. Simulate the SIR model on networks generate using the `get_newman_network` function, matching the parameters used in the paper.\n",
    "3. Measure the epidemic size $S$ when simulations finish.\n",
    "4. For each value of $\\kappa$ and $T$, average $S$ over a few realisations\n",
    "5. Plot $S$ as a function of $T$, using different colors for different values of $\\kappa$.\n",
    "\n",
    "Reproducing figures from the a research article is *not* an easy exercise! Here are some final suggestions on what to copy exactly from the paperr, and what not to. \n",
    "+ Keep the same values of $\\alpha$ and $\\kappa$ as in the paper, otherwise you will not be able to compare.\n",
    "+ Do **not** keep the same network size and number of simulations. Start with small networks (<= 500 nodes), averaging over just 100 realizations. Your code is probably not as fast as the one they used, but that's fine. \n",
    "+ Do not sample the transmissibility $T$ so densely. Start with just few values of $T$. Study how $T$ depends on $\\beta$ at fixed $\\gamma$ to get a range of values for $T$ similar to that of Newman.\n",
    "+ If your code is too slow, do just one of the 3 curves.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose wisely a ranges of parameters\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe define a function that takes parameters as input and returns epidemic size\n",
    "# the function should internally define the network, the model, run the simulation etc\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterate over parameter ranges\n",
    "# and store results in lists, arrays or dicts\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot results, approximately reproducing Figure 1 in the MS\n",
    "# if you get a plot that qualitatively reproduces the main features of Figure 1,\n",
    "# then that is good enough!\n",
    "# It is very difficult to exactly match Newman's results (he used 100.000 nodes and 10.000 simulations per point)\n",
    "\n",
    "...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.15\n",
    "Approximately find the critical transmissibility $T_c$ for different values of $\\kappa$ in yourr simulations, and compare your results to those of the manuscript. Are they similar? Since we are simulating a smaller system, we do not expect a perfect match, but we do expect a similar qualitative behaviouor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.16\n",
    "Explain why $T_c$ decreases when $\\kappa$ is increased. How is this related to the presence of super-spreaders, and how do they impact the global epidemic dynamics?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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