epidemic_spreading_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Epidemic spreading analysis\n",
    "\n",
    "Networks (and network simulation), are a key tool in trying to predict the spread of worldwide pandemics. Many governments using modelling and simulation software to help them make decisions during an epidemic outbreak. Here, the aim is to build a simple model of disease spread on different artificial networks and investigate different vaccination policies to try and prevent or slow down the spread of the pandemic.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Susceptible, Infected, Removed (SIR).**\n",
    "\n",
    "Disease spread can be modelled in various ways using networks; The network built here is where nodes represent people and edges between two nodes indicate whether these two people have sufficient contact (interaction) such that a disease has a non-zero probability of transmission.\n",
    "\n",
    "Each node (person) in the network can be in one of 3 possible states: \n",
    "\n",
    "1. Susceptible (the default state) - indicating the person may contract the disease if one of their neighbours has the disease.\n",
    "\n",
    "2. Infected - the person currently has the disease and can infect their neighbours.\n",
    "\n",
    "3. Removed - the person has either recovered from the disease and is no longer infectious (and cannot contract the disease again) or the person has died.\n",
    "\n",
    "SIR is applicable in many real world diseases, e.g., HIV, chicken pox, etc. The following system of differential equations can be used to describe the SIR model:\n",
    "\n",
    "$$\n",
    "\\frac{ds}{dt} = -\\beta i(t) s(t)\n",
    "\\hspace{1in}\n",
    "\\frac{di}{dt} = \\beta i(t) s(t) - \\alpha i(t)\n",
    "\\hspace{1in}\n",
    "\\frac{dr}{dt} = \\alpha i(t)\n",
    "$$\n",
    "\n",
    "where $\\alpha$ and $\\beta$ describe the rate of removal (fraction of people moving from infected to removed per time unit) and the rate of infection (fraction of people moving from susceptible to infected per time unit) respectively.\n",
    "The assumption is of a well mixed population, i.e., every person has a chance of infecting every other person. In many real world scenarios this assumption is unlikely, since people usually only interact within their social circle. So, they will only contract and transmit the disease from/ to people with whom they had personal contact. However, it is still interesting to try and understand how the network structure impacts the spread, or how it might enable effective vaccination strategies.\n",
    "\n",
    "In this network-based model, each infected node has a probability of transmitting the disease to each of its immediate neighbours with probability $\\beta$ at each time step. Infected nodes also have a probability of recovering from the disease at each time step, with probability $\\alpha$. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*import libraries*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.patches as mpatches \n",
    "from __future__ import unicode_literals \n",
    "import random \n",
    "from tqdm import tqdm \n",
    "import sys \n",
    "import operator \n",
    "from enum import * \n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class State(Enum): \n",
    "    # define the three states of the nodes.\n",
    "    Succeptible = 0\n",
    "    Infected = 1\n",
    "    Removed = 2\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset(G):\n",
    "    \"\"\" Initialise/reset all the nodes in the network to be susceptible, before start of experiment\"\"\"\n",
    "    nx.set_node_attributes(G, State.Succeptible, 'state')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def initialise_infection(G, num_to_infect):\n",
    "    \"\"\"Set the state of a random selection of nodes to be infected\"\"\"\n",
    "    nodes_to_infect = random.sample(G.nodes(), num_to_infect)\n",
    "    for n in nodes_to_infect:\n",
    "        G.nodes[n]['state'] = State.Infected\n",
    "    return nodes_to_infect\n",
    "\n",
    "def get_infection_stats(G):\n",
    "    \"\"\"Creates lists of nodes in the graph G that are susceptible, infected and removed\"\"\"\n",
    "    infected = [] \n",
    "    succeptible = [] \n",
    "    removed = [] \n",
    "    for n in G:\n",
    "        if G.nodes[n]['state'] == State.Infected:\n",
    "            infected.append(n)\n",
    "        elif G.nodes[n]['state'] == State.Succeptible:\n",
    "            succeptible.append(n)\n",
    "        else:\n",
    "            removed.append(n)\n",
    "    return succeptible, infected, removed \n",
    "\n",
    "def execute_one_step(G, model):\n",
    "    \"\"\"execute the infection model\"\"\"\n",
    "    new_nodes_to_infect=[] \n",
    "    new_nodes_to_remove=[]    \n",
    "    for n in G: \n",
    "        i,remove = model(n, G) #execute transmission model on node n\n",
    "        new_nodes_to_infect = new_nodes_to_infect + i #\n",
    "        if remove: \n",
    "            new_nodes_to_remove.append(n) \n",
    "    apply_infection(G, new_nodes_to_infect, new_nodes_to_remove)\n",
    "\n",
    "def apply_infection(G, list_of_newly_infected, list_of_newly_removed):\n",
    "    for n in list_of_newly_infected:\n",
    "        G.nodes[n]['state'] = State.Infected\n",
    "    for n in list_of_newly_removed:\n",
    "        G.nodes[n]['state'] = State.Removed\n",
    "\n",
    "def print_infection_stats(G):\n",
    "    \"\"\"Prints the number of susceptible, infected and removed nodes in graph G.\"\"\"\n",
    "    s,i,r = get_infection_stats(G)\n",
    "    print(\"Susceptible: %d Infected: %d Removed %d\"% (len(s),len(i),len(r)))\n",
    "\n",
    "def draw_network_to_file(G,pos,t, initially_infected):\n",
    "    # create the layout\n",
    "    states = []\n",
    "    for n in G.nodes():\n",
    "        if n in initially_infected:\n",
    "            states.append(3)\n",
    "        else:\n",
    "            states.append(G.nodes[n]['state']) \n",
    "    from matplotlib import colors\n",
    "    cmap = colors.ListedColormap(['green', 'blue','red', 'yellow'])\n",
    "    bounds=[0,1,2,3]\n",
    "\n",
    "    # draw all nodes and edges\n",
    "    nx.draw_networkx_nodes(G,pos,cmap=cmap,alpha=0.5,node_size=170, node_color=states)\n",
    "    nx.draw_networkx_edges(G,pos,alpha=0.075)\n",
    "    plt.savefig(\"images/g\"+str(t)+\".png\")\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Runs single simulation of infection on the graph G, using the specified model. \n",
    "def run_spread_simulation(G, model, initial_infection_count, run_visualise=False):\n",
    "    initially_infected = initialise_infection(G, initial_infection_count)\n",
    "\n",
    "    s_results = []\n",
    "    i_results = []\n",
    "    r_results = []\n",
    "\n",
    "    dt = 0\n",
    "    s,i,r = get_infection_stats(ba)\n",
    "    \n",
    "    pos = nx.spring_layout(G, k=.75)\n",
    "    \n",
    "    while len(i) > 0:\n",
    "        execute_one_step(G, model) \n",
    "        dt += 1 \n",
    "        s,i,r = get_infection_stats(G) #calculate SIR stats \n",
    "        s_results.append(len(s)) \n",
    "        i_results.append(len(i)) \n",
    "        r_results.append(len(r)) \n",
    "        sys.stderr.write('\\rInfected: %d time step: %d' % (len(i), dt))\n",
    "        sys.stderr.flush()\n",
    "        if run_visualise: \n",
    "            draw_network_to_file(G,pos,dt,initially_infected)\n",
    "    return s_results, i_results, r_results, dt, initially_infected "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up model/ experiment\n",
    "\n",
    "In the first experiments, these parameters are specified:\n",
    "\n",
    "1. Size of network $N$\n",
    "2. The network \n",
    "3. The transmission model $m$ that specifies how a node infects others\n",
    "4. The number of people for early infection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmission_model_factory(beta=0.03, alpha=0.05):\n",
    "    \"\"\"\n",
    "    beta: specifies the rate of infection (movement from S to I)\n",
    "    alpha: specifies the rate of removal (movement from I to R)\"\"\"\n",
    "    def m(n, G):\n",
    "        list_of_neighbours_to_infect = [] #list of neighbours infected \n",
    "        removeMyself = False \n",
    "        if G.nodes[n]['state'] == State.Infected:\n",
    "            # infect susceptible neighbours with probability beta\n",
    "            for k in G.neighbors(n):\n",
    "                if G.nodes[k]['state'] == State.Succeptible:\n",
    "                    if random.random() <= beta: \n",
    "                        list_of_neighbours_to_infect.append(k)\n",
    "            if random.random() <= alpha:\n",
    "                removeMyself = True\n",
    "        return list_of_neighbours_to_infect, removeMyself\n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*build initial network*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# size of network\n",
    "N = 1000 \n",
    "# the network\n",
    "ba = nx.barabasi_albert_graph(N, 3) \n",
    "# transmission model\n",
    "m = transmission_model_factory(0.03, 0.05) \n",
    "# number of people infected early\n",
    "number_initial_infections = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running first experiment\n",
    "After running experiment, few important metrics will be plotted:\n",
    "\n",
    "1. Peak Incidence\n",
    "2. Peak Time\n",
    "3. Total Infected\n",
    "\n",
    "Peak incidence is the maximum number of infections at a single time step. \n",
    "<br> The Peak Time indicates the time step at which we reach the peak incidence. \n",
    "<br> Total infected indicates the number of Susceptible -> Infected transitions. *[Note this is not the same as the final removed size, the final removed also includes the vaccinations, and the initially infected nodes.]*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting SIR after experiments\n",
    "def plot_infection(S,I,R,G):\n",
    "    peak_incidence = max(I)\n",
    "    peak_time = I.index(max(I))\n",
    "    total_infected = S[0]-S[-1]\n",
    "\n",
    "    fig_size= [18,13]\n",
    "    plt.rcParams.update({'font.size': 14, \"figure.figsize\": fig_size})\n",
    "    xvalues = range(len(S))\n",
    "    plt.plot(xvalues, S, color='g', linestyle='-', label=\"S\")\n",
    "    plt.plot(xvalues, I, color='b', linestyle='-', label=\"I\")\n",
    "    plt.plot(xvalues, R, color='r', linestyle='-', label=\"R\")\n",
    "    plt.axhline(peak_incidence, color='b', linestyle='--', label=\"Peak Incidence\")\n",
    "    plt.annotate(str(peak_incidence),xy=(1,peak_incidence+10), color='b')\n",
    "    plt.axvline(peak_time, color='b', linestyle=':', label=\"Peak Time\")\n",
    "    plt.annotate(str(peak_time),xy=(peak_time+1,8), color='b')\n",
    "    plt.axhline(total_infected, color='r', linestyle='--', label=\"Total Infected\")\n",
    "    plt.annotate(str(total_infected),xy=(1,total_infected+10), color='r')\n",
    "    plt.legend()\n",
    "    plt.xlabel('time step')\n",
    "    plt.ylabel('Count')\n",
    "    plt.title('SIR for network size ' + str(G.order()))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Infected: 0 time step: 1649"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset(ba) \n",
    "S, I, R, endtime, ii = run_spread_simulation(ba, m, number_initial_infections)\n",
    "plot_infection(S,I,R,ba) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The peak time is at time_step = 32. Total infected is 824 (out of 1000 nodes/people).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vaccination by degree\n",
    "\n",
    "Now, let's develop a vaccination strategy. Here, the number of vaccinations to apply is specified; The first strategy is to vaccinate the nodes (people) with the highest degree. In other words, the node (person) with the most of number of connections to other nodes (interactions) will be vaccinated first. Vaccination in this case means their 'state' is removed, i.e., they're already resilient to the the disease.\n",
    "\n",
    "The network is reset to be all susceptible, then the vaccination strategy is applied, experiments are re-run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_to_vacinate = 10\n",
    "\n",
    "def vacinate_by_degree(G, num_to_vacinate):\n",
    "    degrees = G.degree() \n",
    "    nodes_sorted_by_degree = sorted(dict(degrees()).items(), key=operator.itemgetter(1), reverse=True) \n",
    "    for x in range(num_to_vacinate): \n",
    "        node_id_to_vacinate = nodes_sorted_by_degree[x]\n",
    "        G.nodes[node_id_to_vacinate[0]]['state'] = State.Removed \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Infected: 0 time step: 2230"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rerun single simulation of vaccination\n",
    "reset(ba)        \n",
    "vacinate_by_degree(ba, number_to_vacinate)\n",
    "S, I, R, endtime,ii = run_spread_simulation(ba, m, number_initial_infections)\n",
    "plot_infection(S,I,R,ba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that initial infections are random, i.e., there is a stochastic nature introduced in the network. **By including a vaccination strategy, the peak-time has increased/delayed to time step = 38. Number of total infected is also reduced to 717 (out of 1000 nodes/people).** Obviously, when vaccination is introduced, the number of people infected has decreased, and the time step to reach peak infection is delayed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vaccination by betweenness\n",
    "\n",
    "Now, let's try a different strategy for vaccination and vaccinate the nodes with the highest betweenness centrality. 'Betweenness' measures the number of shortest paths that a node lies on. In other words, the node (person) most likely to act as a bridge along/between other nodes. \n",
    "The betweenness centrality on node $n_j$ is defined as:\n",
    "\n",
    "$$ \n",
    "g(n_j) \\sum_{i \\neq j \\neq k} = \\frac{\\sigma_{n_in_k}(n_j)} {\\sigma_{n_in_k}}$$\n",
    "\n",
    "where $\\sigma_{n_in_k}$ is the total number of shortest paths from node $n_i$ to $n_k$ and $\\sigma_{n_in_k}(n_j)$ is the number of those paths that pass through node $n_j$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vacinate_by_betweenness(G, number):\n",
    "    bet_cen = nx.betweenness_centrality(G)\n",
    "    nodes_sorted_by_betweenness = sorted(bet_cen.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    for x in range(number):\n",
    "        node_id_to_vacinate = nodes_sorted_by_betweenness[x]\n",
    "        G.nodes[node_id_to_vacinate[0]]['state'] = State.Removed  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Infected: 0 time step: 2346"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset(ba)        \n",
    "vacinate_by_betweenness(ba, number_to_vacinate)\n",
    "S, I, R, endtime,ii = run_spread_simulation(ba, m, number_initial_infections)\n",
    "plot_infection(S,I,R,ba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**By changing the vaccination strategy (to betweenness), the peak-time was increased/delayed to time step = 47. This is still higher than without a vaccination strategy. Number of total infected is also reduced to 795 (out of 1000 nodes/people), compared to having no vaccination strategy.**\n",
    "<br> Vaccinating the node (person) with the highest degree will be more effective against the disease, since the person will have the most personal interactions with other people. In reality, it is hard to implement, because should we vaccinate people based on the number of 'friends' in facebook/ instagram? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation in bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NetworkX does not have great visualisation capabilities built in, but we can try bokeh that creates nice interactive plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1002\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1002\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\": \"XypntL49z55iwGVUW4qsEu83zKL3XEcz0MjuGOQ9SlaaQ68X/g+k1FcioZi7oQAc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\": \"bEsM86IHGDTLCS0Zod8a8WM6Y4+lafAL/eSiyQcuPzinmWNgNO2/olUF0Z2Dkn5i\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\": \"TX0gSQTdXTTeScqxj6PVQxTiRW8DOoGVwinyi1D3kxv7wuxQ02XkOxv0xwiypcAH\"};\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1002\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\": \"XypntL49z55iwGVUW4qsEu83zKL3XEcz0MjuGOQ9SlaaQ68X/g+k1FcioZi7oQAc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\": \"bEsM86IHGDTLCS0Zod8a8WM6Y4+lafAL/eSiyQcuPzinmWNgNO2/olUF0Z2Dkn5i\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\": \"TX0gSQTdXTTeScqxj6PVQxTiRW8DOoGVwinyi1D3kxv7wuxQ02XkOxv0xwiypcAH\"};\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
      "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
      "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"3c566f98-db0c-491c-9f92-d9a2a61d58ea\" data-root-id=\"1003\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import *\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "# create a new plot with a title and axis labels\n",
    "p = figure(title=\"SIR model\", x_axis_label='time step', y_axis_label='count', plot_width=1000, plot_height=800)\n",
    "xvalues = range(len(S))\n",
    "# add a line renderer with legend and line thickness\n",
    "p.line(xvalues, S, legend=\"Susceptible\", line_width=2, line_color=\"green\")\n",
    "p.line(xvalues, I, legend=\"Infected\", line_width=2, line_color=\"blue\")\n",
    "p.line(xvalues, R, legend=\"Recovered\", line_width=2, line_color=\"red\")\n",
    "\n",
    "# show the results\n",
    "show(p)\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Deciding on a Strategy with Incomplete Information\n",
    "\n",
    "If we run the above experiments repeatedly, we will see that in some cases no outbreak occurs, or a very limited outbreak occurs. The only initial condition that changes in the experiments is the set of nodes that were initially infected. This  is chosen randomly inside the **initialise_infection** function, performed with command **random.sample**. If that initial random sample of people from the graph just so happens to hit one important node (highly connected) the likelihood of an outbreak occurring is high. However, if the sample only contains nodes with very low degree (e.g., 1), then there is good chance that those node will be removed (state 'R'). This is also dependent on the disease and the values of $\\beta$ and $\\alpha$.\n",
    "\n",
    "This level of known and unknown parameters are somewhat realistic. In reality, policy makers may have information and data for different aspects of the pandemic. For example, they may have some idea about the network structure, but not all, they may also have an approximation (or range) for $\\beta$ and $\\alpha$, through data collected at hospitals. However, they will almost never have all the information necessary to make a prediction. In this case, it's necessary to assess the sensitivity of the results according to each unknown (and perhaps known) parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assuming a contact network that is scale-free. The question is: is it better to vaccinate \n",
    "<br> (a) nodes with higher degree or \n",
    "<br> (b) nodes with higher betweenness?**\n",
    "\n",
    "There are a number of situations that the answer might depend on; The control parameters are: 1. Method, 2. Number of Vaccinations, 3. Number of initial infections. There is also a stochastic parameter - the set of nodes that are initially infected, which will vary randomly.\n",
    "\n",
    "There is also the assumption that we already know the network (i.e., use a fixed network instance, with a fixed size), and the disease parameters - $\\alpha = 0.05$, and $\\beta = 0.03$, i.e., at each time step, a node will infect each neighbour with a 3% chance, and has a 5% chance to recover/be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Infected: 0 time step: 1854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('vacinate_by_betweenness', 0.005, 0.005, 363.6666666666667, 38.0, 882.0), ('vacinate_by_betweenness', 0.005, 0.01, 376.0, 37.0, 862.0), ('vacinate_by_betweenness', 0.05, 0.005, 400.6666666666667, 20.0, 831.0), ('vacinate_by_betweenness', 0.05, 0.01, 371.3333333333333, 20.666666666666668, 813.3333333333334), ('vacinate_by_degree', 0.005, 0.005, 383.6666666666667, 32.0, 879.0), ('vacinate_by_degree', 0.005, 0.01, 329.0, 41.333333333333336, 860.3333333333334), ('vacinate_by_degree', 0.05, 0.005, 422.3333333333333, 22.0, 844.0), ('vacinate_by_degree', 0.05, 0.01, 364.3333333333333, 26.333333333333332, 822.6666666666666)]\n"
     ]
    }
   ],
   "source": [
    "# create network\n",
    "N = 1000 \n",
    "ba = nx.barabasi_albert_graph(N, 4) \n",
    "\n",
    "reset(ba) \n",
    "m = transmission_model_factory(0.03, 0.05) \n",
    "\n",
    "#create a range on initial infection/vaccination counts\n",
    "fraction_initial_infections = [5e-3, 5e-2] \n",
    "fraction_vaccinations = [5e-3, 1e-2] \n",
    "\n",
    "vacination_methods = [vacinate_by_betweenness, vacinate_by_degree]\n",
    "repetitions = 3 # number of times to run each instance (i.e., vary the starting infection locations)\n",
    "\n",
    "results = []\n",
    "for method in vacination_methods:\n",
    "    for inf in fraction_initial_infections:\n",
    "        for vac_fraction in fraction_vaccinations:\n",
    "            total_peak_incidence = total_peak_time = total_total_infected = 0\n",
    "            for rep in range(1,repetitions+1):\n",
    "                reset(ba)\n",
    "                method(ba, int(vac_fraction*N))\n",
    "                S, I, R, endtime, ii = run_spread_simulation(ba, m, int(N*inf))\n",
    "                total_peak_incidence += max(I)\n",
    "                total_peak_time += I.index(max(I))\n",
    "                total_total_infected += S[0]-S[-1]\n",
    "            ave_peak_incidence = total_peak_incidence / rep\n",
    "            ave_peak_time = total_peak_time / rep\n",
    "            ave_total_infected = total_total_infected / rep\n",
    "            results.append((method.__name__, inf, vac_fraction,ave_peak_incidence,ave_peak_time,ave_total_infected))\n",
    "                \n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>initial_infection</th>\n",
       "      <th>vac_fraction</th>\n",
       "      <th>ave_peak_incidence</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.005</td>\n",
       "      <td>363.666667</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>882.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vacinate_by_betweenness</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>862.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vacinate_by_betweenness</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.005</td>\n",
       "      <td>400.666667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>831.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vacinate_by_betweenness</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.010</td>\n",
       "      <td>371.333333</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>813.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vacinate_by_degree</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>383.666667</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>879.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>vacinate_by_degree</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>41.333333</td>\n",
       "      <td>860.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>vacinate_by_degree</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.005</td>\n",
       "      <td>422.333333</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>844.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>vacinate_by_degree</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.010</td>\n",
       "      <td>364.333333</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>822.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    method  initial_infection  vac_fraction  \\\n",
       "0  vacinate_by_betweenness              0.005         0.005   \n",
       "1  vacinate_by_betweenness              0.005         0.010   \n",
       "2  vacinate_by_betweenness              0.050         0.005   \n",
       "3  vacinate_by_betweenness              0.050         0.010   \n",
       "4       vacinate_by_degree              0.005         0.005   \n",
       "5       vacinate_by_degree              0.005         0.010   \n",
       "6       vacinate_by_degree              0.050         0.005   \n",
       "7       vacinate_by_degree              0.050         0.010   \n",
       "\n",
       "   ave_peak_incidence  ave_peak_time  ave_total_infected  \n",
       "0          363.666667      38.000000          882.000000  \n",
       "1          376.000000      37.000000          862.000000  \n",
       "2          400.666667      20.000000          831.000000  \n",
       "3          371.333333      20.666667          813.333333  \n",
       "4          383.666667      32.000000          879.000000  \n",
       "5          329.000000      41.333333          860.333333  \n",
       "6          422.333333      22.000000          844.000000  \n",
       "7          364.333333      26.333333          822.666667  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# present in table form\n",
    "pd.DataFrame(data=results,columns=['method','initial_infection','vac_fraction','ave_peak_incidence','ave_peak_time','ave_total_infected'])"
   ]
  }
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