TP-graphes/graph21-22.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Graph project: Epidemic spreading</h1></center>\n",
    "\n",
    "<center><h4>ENSEEIHT SN</h4></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "## Introduction\n",
    "<a id='intro'></a>\n",
    "    \n",
    "<p style=\"text-align:justify;\"> As you know, a new epidemic has overwhelmed the world, COVID-19 jeopardizes people and changes our habits. It is easy to realise that knowing how illnesses spread is vital to our own protection. How can we predict whether a disease will cause an epidemic, how many people it will infect, which people it will infect, and whether or not it is dangerous to society as a whole ? Also, how can we determine which techniques to use in fighting an epidemic once it begins ? One way to answer all of these questions is through <strong>mathematical modeling</strong>. </p>\n",
    "\n",
    "<p style=\"text-align:justify;\"> In this work you will have to review different epidemic modelings relying all on the representation by graphs of a human network called a <strong>contact network</strong>. A vertex in a contact network represents an individual and an edge between two vertices represents a contact between two individuals. The disease only spread from individual to individual if they are in contact, so through the edges. This representation is actually really common in research, and a lot of state-of-the-art modeling are built over it. From these different models you will be asked to draw conclusions from experiments on varying contact networks</p>\n",
    "\n",
    "<p style=\"text-align:justify;\"> For readability and ease of use, this project will be carried on a Jupyter Notebook, hence code and question answering have to be written in this unique file. This is a <strong>DUO</strong> project, no group of one person will be accepted, the duo has to be composed of same TD group students, if the number of students in the TD group is odd we will accept one group composed of three students. It will be coded in Julia using the LightGraphs package. <strong>BEWARE:</strong> \n",
    "    \n",
    "- If the code does not provide good results, its readability as well as its comments are essential for the corrector to potentially find some notation points.\n",
    "- The specifications of the functions have to be strictly respected.\n",
    "- Do not neglect written questions they stand for an important part of the notation, you are not only evaluated on the coding. Also, even so a written question may not ask you directly to code or provide results from code, support your arguments when possible with a runable example is very welcome and sometimes even expected.\n",
    "- Any initiatives and additional efforts bringing contents and thoughts out of the question scope may result in bonus points if pertinent.\n",
    "</p> \n",
    "    \n",
    "<p style=\"text-align:justify;\"> Deliverable: You will deliver your work on moodle before <strong style=\"font-size:1.3em\">23.01.2022</strong> in a <strong style=\"font-size:1.3em\">.tar</strong> containing the notebook with your codes and your written answers, and the different graph figures in .png you will generate. The corrector will use the student N7 computers for running your code, so take care of verifying that your work is running as expected on these computers !</p>\n",
    "\n",
    "LightGraphs documentation: https://juliagraphs.org/LightGraphs.jl/v1.2/index.html\n",
    "    \n",
    "<!---\n",
    "http://makie.juliaplots.org/stable/basic-tutorial.html#Adding-plots-to-a-Scene\n",
    "TODO: afficher l'historique gplot des graphes durant la simu pour voir visuellement ce que ça donne. Peut etre necessiter de locker l'affichage graphique.\n",
    "afficher pour tau fixer sur different graphe de degré le nombre max d'infecté durant la simu.\n",
    "    \n",
    "Faire coder le modèle SAIS et SIR\n",
    "\n",
    "    Pour SIR faire la courbe dans un des sites dans les favoris\n",
    "    \n",
    "    Expliquer en quoi la distance et Jordan ne sont pas tout le temps optimal. Expliquer pourquoi le DMP peut présenter de meilleur résultat.\n",
    "\n",
    "Demander d'imaginer d'autres modèle inspirer de maladie\n",
    "--->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "## Environment and packages installation\n",
    "<a id='env'></a>\n",
    "    \n",
    "\n",
    "<p style=\"text-align:justify;\"> <strong>IMPORTANT</strong>: For evaluation, coding questions have to run with no additional packages ! Only the ones present here ! However if you want to use another package to go further in your answer and add bonus contents, take care of separating the cells and precising which packages you are using.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/Documents/Cours/ENSEEIHT/S7 - Graphes/projet`\n",
      "┌ Warning: The active manifest file at `/home/laurent/Documents/Cours/ENSEEIHT/S7 - Graphes/projet/Manifest.toml` has an old format that is being maintained.\n",
      "│ To update to the new format run `Pkg.upgrade_manifest()` which will upgrade the format without re-resolving.\n",
      "└ @ Pkg.Types /build/julia/src/julia-1.7.0/usr/share/julia/stdlib/v1.7/Pkg/src/manifest.jl:287\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/Documents/Cours/ENSEEIHT/S7 - Graphes/projet/Project.toml`\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/Documents/Cours/ENSEEIHT/S7 - Graphes/projet/Manifest.toml`\n"
     ]
    }
   ],
   "source": [
    "# TO RUN ONCE\n",
    "using Pkg\n",
    "Pkg.activate(\".\") # Path to Manifest.toml and Project.toml\n",
    "Pkg.resolve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: both Graphs and LightGraphs export \"barabasi_albert\"; uses of it in module Main must be qualified\n",
      "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/Documents/Cours/ENSEEIHT/S7 - Graphes/projet`\n"
     ]
    }
   ],
   "source": [
    "# Import packages\n",
    "using Pkg\n",
    "Pkg.activate(\".\") # Path to Manifest.toml and Project.toml\n",
    "using GraphPlot\n",
    "using Colors\n",
    "using CairoMakie\n",
    "using StatsBase\n",
    "using Plots\n",
    "using JLD2\n",
    "using Compose\n",
    "using Graphs\n",
    "using Graphs: smallgraph\n",
    "using Graphs: stochastic_block_model\n",
    "using Graphs: barabasi_albert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "## Part 1 - SIS model\n",
    "<a id='part1'></a>\n",
    "\n",
    "<p style=\"text-align:justify;\">SIS is a compartmental model, ie a model where the population is divided into subgroups that represent the disease status of its members. SIS stands for Susceptible $\\rightarrow$ Infected $\\rightarrow$ Susceptible where the susceptible group contains those who remain susceptible to the infection, and the infected group consists of those who not only have the disease but are also in the contagious period of the disease.</p>\n",
    "    \n",
    "<p style=\"text-align:justify;\"> Combine with a contact network approach, this model can capture contact patterns (family, company, friends). Each vertex represents an individual in the host population, and contacts between two individuals are represented by an edge that connects the two. The probability of transmitting the disease from an infected to a susceptible individual along one of these edges or contacts is $\\beta$ (=<strong>infection rate</strong>). The probability to cure is $\\alpha$ (= <strong>curing rate</strong>). </p>\n",
    "\n",
    "    \n",
    "<p style=\"text-align:justify;\">In order for a disease to begin spreading through a network, the disease must be introduced into the population, either through infecting a proportion of the population or through infecting one individual. As time moves forward, the disease will spread away from those initially infected, and two things may occur simultaneously at each time step $t$. First, each infected individual will spread disease to each of its contacts with a probability $\\beta$. Secondly, each infectious individual will recover at a rate, $\\alpha$ , at which point the individual will then no longer infect any of its contacts. After the disease has run its course, we can determine how the disease affected the network by calculating various quantities that help us better understand the outbreak.\n",
    "</p>\n",
    "\n",
    "<br>\n",
    "    \n",
    "<p style=\"font-size:0.9em\">P. Van Mieghem, J. Omic, R. E. Kooij, <em>“Virus Spread in Networks”</em>,\n",
    "IEEE/ACM Transaction on Networking (2009)<p>\n",
    "\n",
    "\n",
    "<!---\n",
    "<div style=\"background-color:rgba(0, 0, 0, 0.0470588); padding:10px 0;font-family:monospace;\">\n",
    "<font size = 4.5px><b>Algorithm 1:</b> Girvan Newman</font><br>\n",
    "    <div style=\"margin-left:50px;border-left:2px solid black;padding-left:10px;\">\n",
    "    <b> WHILE </b> there are still edges<b> OR </b> desired nb of communities unreached <b>DO</b>\n",
    "        <div style=\"margin-left:50px;border-left:2px solid black;padding-left:10px;\">\n",
    "        Calculate betweeness of all edges <br>\n",
    "        Remove the edge with the highest betweeness <br>\n",
    "        Calculate the number of strongly connected components\n",
    "        </div>\n",
    "    <b> END WHILE </b>   \n",
    "    </div>\n",
    "</div>\n",
    "--->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "### 1.1 Contact networks sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"karat7: A graph representing the karate club of N7 and the connections between the persons in this club.\n",
    "There are 34 people in this network. It is actually inspired by one of the most famous problem in graph\n",
    "theory: the Zachary's karate club.\n",
    "\"\"\"\n",
    "karat7 = smallgraph(:karate)\n",
    "nodecolor = [colorant\"lightseagreen\"]\n",
    "# # draw(PNG(\"karat7.png\", 100cm, 100cm), gplot(karat7,nodefillc=nodecolor))\n",
    "gplot(karat7, nodefillc=nodecolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, UnitBox{Float64, Float64, Float64, Float64}(-1.2, -1.2, 2.4, 2.4, 0.0mm, 0.0mm, 0.0mm, 0.0mm), nothing, nothing, nothing, List([Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, nothing, nothing, nothing, nothing, List([]), List([Compose.Form{Compose.LinePrimitive}(Compose.LinePrimitive[Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.41327908213686443cx, -0.6739986590410558cy), (-0.5651318755846204cx, -0.527479070916072cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.39998184117018215cx, -0.6688875528937819cy), (-0.39981858066086645cx, -0.666536988119583cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.4024796273931856cx, -0.6688995939330098cy), (-0.42424589037716315cx, -0.39390103559746475cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.4094138130943621cx, -0.7003920183403316cy), (-0.5047112825145429cx, -0.8697893495837946cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.40734236457330714cx, -0.701378197407705cy), (-0.49870380775741113cx, -0.9326514499640973cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.4161550486897547cx, -0.6933786675062271cy), (-0.4843928133965433cx, -0.7282886233707364cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.4101475960088274cx, -0.671449711654358cy), (-0.4809140425808324cx, -0.5593582459326776cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.3915302279649085cx, -0.6718616450587421cy), (-0.2067303021302049cx, -0.4059736112047384cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.4032640517521167cx, -0.7024238962102303cy), (-0.42954560437093975cx, -0.9102784143743528cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(-0.41429799150412594cx, -0.6751562597424664cy), (-0.5370177130240902cx, -0.5767270538145788cy)])  …  Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.8402131813791817cx, -0.37704481704941967cy), (0.4761156095673812cx, -0.4141095296862379cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.5752529316696009cx, -0.059839128771370285cy), (0.7345900651501589cx, -0.12435149399373062cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.5551336144591611cx, -0.06975788816218408cy), (0.4638435460225368cx, -0.3995723140827788cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7322517320701617cx, -0.2281617422775172cy), (0.7157688776244165cx, -0.40121116501058496cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7254312236149342cx, -0.19678075804335565cy), (0.6178510435489052cx, -0.010186919272407825cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7380516449662804cx, -0.11900691468853129cy), (0.6215938057226444cx, -0.0072550225986181115cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7468241505968026cx, -0.14718772748697465cy), (0.719470115143508cx, -0.2804547081442246cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7858053186324424cx, -0.3572854240742122cy), (0.7433542131549539cx, -0.3545797030739068cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.7888787889097838cx, -0.3486056149972059cy), (0.7298285491190318cx, -0.30671741464006685cy)]), Compose.LinePrimitive{Tuple{Measure, Measure}}(Tuple{Measure, Measure}[(0.4746427902715461cx, -0.4087357007635186cy), (0.7007856391307027cx, -0.3040463143475426cy)])], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.20225995873897262mm)]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.8274509803921568,0.8274509803921568,0.8274509803921568,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, nothing, nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.20225995873897262mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.8274509803921568,0.8274509803921568,0.8274509803921568,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, nothing, nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[Compose.FontSizePrimitive(4.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, nothing, nothing, nothing, nothing, List([]), List([Compose.Form{Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}}(Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}[Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.4011497059645458cx, -0.6857020407216342cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.8796635478426869cx, -0.34110172438234343cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.3652001057665837cx, -0.23949681188676153cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.7348982731018701cx, -0.38235134598259746cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.42602579092184656cx, -0.7905723687743453cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.5772612517569391cx, -0.5157756892354937cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.7598538531097753cx, -0.6786396116143947cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.758740594810748cx, -0.48777473471859945cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.6009465129409024cx, -0.7544031656383009cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((-0.6916719890693883cx, -0.768093920033031cy), 0.006741998624632421w)  …  Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.8539851043752484cx, -0.5552192577877535cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.7338499270515504cx, -0.2113826874205721cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.7502131105766359cx, -0.130676947391958cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.7141706826430279cx, -0.41799021986753004cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.8026261828651409cx, -0.35835754139803155cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.9337392895726784cx, -0.17534095139063532cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.609432340112289cx, 0.004415010104808603cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.459347274238574cx, -0.41581652687182cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.7265333489222554cx, -0.35350758575008745cy), 0.006741998624632421w), Compose.CirclePrimitive{Tuple{Measure, Measure}, Measure}((0.7160811551636748cx, -0.2969654882392412cy), 0.006741998624632421w)], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.12549019607843137,0.6980392156862745,0.6666666666666666,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 1.0w,1.0h}, nothing, nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[Compose.FontSizePrimitive(4.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))]), List([]), List([]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"n7_2A: A graph representing the second year students at N7. Each department (SN, MF2E, 3EA) form a \n",
    "community where connections are denser, connections between department are rarer.\n",
    "\"\"\"\n",
    "c=[[10,0,0] [0.1,10,0] [0.1,0.1,10]]\n",
    "n=[100,70,50]\n",
    "n7_2A = stochastic_block_model(c,n)\n",
    "nodecolor = [colorant\"lightseagreen\"]\n",
    "# draw(PNG(\"n7_2A.png\", 100cm, 100cm), gplot(n7_2A,nodefillc=nodecolor))\n",
    "gplot(n7_2A, nodefillc=nodecolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"toulouse_neigh: A graph representing a neighborhood composed of 1000 people in Toulouse.\n",
    "\"\"\"\n",
    "toulouse_neigh = barabasi_albert(1000, 1)\n",
    "nodecolor = [colorant\"lightseagreen\"]\n",
    "# draw(PNG(\"toulouse_neigh.png\", 100cm, 100cm), gplot(toulouse_neigh,nodefillc=nodecolor))\n",
    "gplot(toulouse_neigh,nodefillc=nodecolor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "  \n",
    "### 1.2 Introduce the infection\n",
    "    \n",
    "<p style=\"text-align:justify;\">We denote by <code>state</code> a vector containing the disease status of each vertex where Susceptible=0 and Infected=1. Then <code>state</code> is an <code>Array{Int32,1}</code> of length the number of vertices. This array in addition of a graph (represented internally by an adjacency matrix or an adjacency list) will be the data structure of our model.</p>\n",
    "    \n",
    "<span style=\"font-size:0.9em\">In <code>Array{Int32,1}</code>, <code>Int32</code> refers to the kind of data in the array, here 32 bits integers, <code>1</code> refers to the dimension of the array, here we have a 1-dimensional structure so a vector.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cornflowerblue\">Question 1 (code):</strong> For each graph in the graph sample (<code>karat7</code>, <code>n7_2A</code>, <code>toulouse_neigh</code>) initialize the state array by assigning each vertex to susceptible and add randomly one or numerous infected people. Save the graph as a .png image using <code>gplot</code> and <code>draw</code>, infected should appear in a different color (<code>colorant\"orange\"</code>).\n",
    "    \n",
    "Due to a bug on certain version of Jupyter Notebook, the graph <span style=\"font-size:1.3em\">figures should be saved in a file and not plot inside the notebook </span> !!!\n",
    "    \n",
    "Gplot GitHub: https://github.com/JuliaGraphs/GraphPlot.jl\n",
    "    \n",
    "Gplot examples: https://juliagraphs.org/GraphPlot.jl/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Infect Karat7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Infect N7_2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Infect Toulouse_neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 2 (written):</strong> What do you think/predict about the influence of the initial number of infected people and their locations on the evolution of an SIS model epidemic ?\n",
    "\n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"><strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "  \n",
    "### 1.3 Spread the infection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "      \n",
    "<strong style=\"color:cornflowerblue\">Question 3 (code):</strong> Implement the <code>function SIS</code> (respect the header and the specifications). You can use <code>rand</code> to translate the probabilities. Test your algorithm on <code>karat7</code>, <code>n7_2A</code>, and <code>toulouse_neigh</code> with arbitrary $\\beta$, $\\alpha$, and $t$.\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>new_state = SIS(net,state,beta,alpha,t)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function SIS(net,state,beta,alpha,t)\n",
    "    \"\"\"Take a contact network at a certain state and apply t time steps\n",
    "    of an SIS model.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       state (Array{Int32,1}): disease status of each vertex\n",
    "       beta (Float64): infection rate\n",
    "       alpha (Float64): curing rate\n",
    "       t (Int32): number of time step\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Int32,1}): The new state of the contact network after t time steps.\n",
    "    \"\"\"     \n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Karat7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on N7_2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Toulouse_neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "### 1.4 Simulate and understand the epidemic\n",
    "    \n",
    "<p style=\"text-align:justify;\">In the SIS model of this project, every disease is characterized by:\n",
    "    \n",
    "* The infection rate $\\beta$ representing the chance of infection when being in contact with an infected individual.\n",
    "* The curing rate $\\alpha$ representing the chance of being cured of the disease.\n",
    "* The effective spreading rate $\\tau=\\frac{\\beta}{\\alpha}$ representing the capacity of the disease to spread. More the disease infect easily ($\\beta$ high) and less it is cured easily ($\\alpha$ low) more $\\tau$ can be high.\n",
    "\n",
    "We are now willing to understand what are the influences of these parameters as well as the contact network shape on an epidemic.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 4 (written):</strong> The <code>function SIS</code> you implemented launches one run of an SIS model on a given contact network. As it makes use of randomness, one run of spreading is stochastic. Then what simple method can you propose to provide a prediction of the disease spreading on a given contact network ?\n",
    "   \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <Strong>Answer:</Strong></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 5 (code):</strong> Implement the <code>function Simulation_SIS</code> (respect the header and the specifications).\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>predictions, taus = Simulation_SIS(net,nbinf,betas,alphas,t,nbsimu)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function Simulation_SIS(net,nbinf,betas,alphas,t,nbsimu)\n",
    "    \"\"\"Take a contact network, different diseases (defined by \n",
    "    different parameters alpha and beta), a number of initial\n",
    "    infected people and process nbsimu simulations of SIS over\n",
    "    t time steps. You will provide the prediction of the \n",
    "    percentage of infected at each time t as well as the \n",
    "    spreading rate of each disease.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       nbinf (Int32): number of infected at the start of each \n",
    "            simulation\n",
    "       betas (Array{Float64,1}): array of infection rate on edges\n",
    "       alphas (Array{Float64,1}): array of curing rate on vertices\n",
    "       t (Int32): number of time step\n",
    "       nbsimu (Int32): number of simulations\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Float64,2}): the prediction of the percentage of \n",
    "            infected at each time step and for each disease. The \n",
    "            first dimension contains the time steps and the second\n",
    "            contains the diseases\n",
    "        (Array{Float64,1}): effective spreading rate for each \n",
    "            disease\n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 6 (written)</strong>: Run the 2 scripts below and describe what you see. Conclude on the influence of $\\tau$, $\\beta$, and $\\alpha$ on an epidemic we can model with SIS.\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Script launching predictions on different diseases on karat7 and printing \n",
    "# the precentage of infected at each time step.\n",
    "betas=[0.05,0.1,0.01,0.4,0.04,0.05,0.005]\n",
    "alphas=[0.05,0.1,0.01,0.1,0.01,0.1,0.01]\n",
    "\n",
    "predictions, taus = Simulation_SIS(karat7,2,betas,alphas,1000,1000)\n",
    "\n",
    "Plots.plot(predictions, label=taus',xlabel=\"Time\",ylabel=\"% of infected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same as before but applied on toulouse_neigh. May be a bit long to run.\n",
    "betas = [0.05,0.1,0.15,0.25,0.35,0.45,0.55,0.65,0.75,0.85,0.95]\n",
    "alphas = [0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2]\n",
    "\n",
    "predictions, taus = Simulation_SIS(toulouse_neigh,100,betas,alphas,1000,100)\n",
    "\n",
    "Plots.plot(predictions, label=taus',xlabel=\"Time\",ylabel=\"% of infected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 7 (written):</strong> Change the initial number of infected in the scripts above, is it in accordance with your answer in Question 2 ?\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Change the initial number of infected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cornflowerblue\">Question 8 (code):</strong> Implement a script plotting the maximum percentage of infected people according to $\\tau$ over 300 time steps for 3 contact networks:\n",
    "\n",
    "* A regular graph of 200 vertices with degree 2.\n",
    "* A regular graph of 200 vertices with degree 5.\n",
    "* A regular graph of 200 vertices with degree 10.\n",
    "\n",
    "You can use the function <code>random_regular_graph(n,d)</code> of LighGraphs. As you probably need to use a certain number of different values of $\\tau$ to visualize something interesting (the more there are the more the figure will be smooth) you should fix $\\alpha$ and make $\\beta$ vary. \n",
    "\n",
    "<span style=\"font-size:0.9em\">A regular graph is a graph where each vertex has the same degree.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots of the maximum percentage of infected people according to tau over 300 time \n",
    "# steps for 3 contact networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 9 (written):</strong> Describe the figure and draw conclusions on the epidemic behavior for different degrees $d$ on regular graphs. Thus, in addition of the inner properties of the disease ($\\alpha$, $\\beta$, $\\tau$) what other parameter is essential in the spreading ? Finally, what analogy can be done with real life from this experiment ?\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "## Part 2 - SIR and SAIR model\n",
    "<a id='part2'></a>\n",
    "    \n",
    "<p style=\"text-align:justify;\">Unfortunately SIS model is valuable for diseases we can catch back since a cured person can get ill again. This is true for the flu, the cold, etc. However COVID-19 might create immunity for whom already got it and SIS can not take into account immune or dead persons. That is why we propose in this part to consider another model more adapted to COVID-19 called SIR. It stands for Susceptible $\\rightarrow$ Infected $\\rightarrow$ Recovered where the susceptible group contains those who remain susceptible to the infection, the infected group consists of those who not only have the disease but are also in the contagious period of the disease, and the recovered group contains those who were ill, got cured, are not contagious and can not get ill anymore.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size:0.9em\">M. Youssef and C. Scoglio, <cite>\"An individual-based approach to SIR epidemics in contact networks\"</cite>, Journal of Theoretical Biology 283 (2011)</p>\n",
    "\n",
    "<br>\n",
    "    \n",
    "<p style=\"text-align:justify;\"> One limitation of SIR is that it does not model the reaction of humans when they feel the presence of the epidemic. Indeed, if feeling threaten or surrounded by infected, an individual may change its behaviors: wear mask, wash its hands, etc. This result in a smaller infection rate. That is why in this part we will also consider a variant of SIR called SAIR which stands for Susceptible $\\rightarrow$ Alert $\\rightarrow$ Infected $\\rightarrow$ Recovered. A susceptible individual becomes infected by the infection rate $\\beta_0$, an infected individual recovers and gets immune by the curing rate $\\alpha$, an individual can observe the states of its neighbors, then a susceptible individual might go to the alert state if surrounded by infected individuals with an alert rate $\\kappa$ on each contact with an infected, an alert inividual becomes infected by the infection rate $\\beta_1$ where $0<\\beta_1<\\beta_0$. In our simple SAIR model, an individual can not go back to a susceptible state when he got into the alert state.</p>\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<p style=\"font-size:0.9em\"> F. Darabi Sahneh and C. Scoglio, <cite>\"Epidemic Spread in Human Networks\"</cite>, 50th IEEE Conf. Decision and Contol, Orlando, Florida (2011)</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "### 2.1 SIR\n",
    "    \n",
    "<p style=\"text-align:justify;\">The vector containing the disease status <code>state</code> has to change a bit since we added a new state. Hence it will be an <code>Array{Int32,1}</code> where Susceptible=0, Infected=1, and Recovered=2.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 10 (code):</strong> Implement the <code>function SIR</code> (respect the header and the specifications). You can use <code>rand</code> to translate the probabilities. Test your algorithm on <code>karat7</code>, <code>n7_2A</code>, and <code>toulouse_neigh</code> with arbitrary $\\beta$, $\\alpha$, and $t$. Recovered vertices should appear in a different color (<code>colorant\"purple\"</code>).\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>new_state = SIR(net,state,beta,alpha,t)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function SIR(net,state,beta,alpha,t)\n",
    "    \"\"\"Take a contact network at a certain state and apply t time steps\n",
    "    of an SIR model.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       state (Array{Int32,1}): disease status of each vertex\n",
    "       beta (Float64): infection rate\n",
    "       alpha (Float64): curing rate\n",
    "       t (Int32): number of time step\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Int32,1}): The new state of the contact network after t time steps.\n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Karat7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on N7_2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Toulouse_neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 11 (code):</strong> Implement the <code>function Simulation_SIR</code> (respect the header and the specifications).\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>predictions, taus = Simulation_SIR(net,nbinf,betas,alphas,t,nbsimu)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function Simulation_SIR(net,nbinf,betas,alphas,t,nbsimu)\n",
    "    \"\"\"Take a contact network, different diseases (defined by \n",
    "    different parameters alpha and beta), a number of initial\n",
    "    infected people and process nbsimu simulations of SIR over\n",
    "    t time steps. You will provide the prediction of the \n",
    "    percentage of infected at each time t as well as the \n",
    "    spreading rate of each disease.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       nbinf (Int32): number of infected at the start of each \n",
    "            simulation\n",
    "       betas (Array{Float64,1}): array of infection rate on edges\n",
    "       alphas (Array{Float64,1}): array of curing rate on vertices\n",
    "       t (Int32): number of time step\n",
    "       nbsimu (Int32): number of simulations\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Float64,3}): the prediction of the percentage of \n",
    "            infected, the percentage of susceptible and the \n",
    "            percentage of recovered at each time step and for each \n",
    "            disease. The first dimension contains the time steps,\n",
    "            the second contains the diseases, and the third the status\n",
    "            (Infected: [:,:,1], Recovered: [:,:,2], Susceptible: [:,:,3])\n",
    "        (Array{Float64,1}): effective spreading rate for each \n",
    "            disease\n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 12 (written):</strong> Run the script below and describe what you see. Why the infected curve does not behave the same as for SIS ? \n",
    "    \n",
    "<br>\n",
    "\n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Script launching prediction on one disease on n7_2A and plotting the percentage\n",
    "# of infected, susceptible and recovered at each time step.\n",
    "predictions, taus = Simulation_SIR(n7_2A,2,[0.3],[0.2],50,1000)\n",
    "\n",
    "Plots.plot([predictions[:,:,1] predictions[:,:,2] predictions[:,:,3]],\n",
    "           label=[\"Infected\" \"Recovered\" \"Susceptible\"],xlabel=\"t\",ylabel=\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 13 (written):</strong> As for Question 6 script 2 plot the evolution of the percentage of infected for many $\\tau$. Describe what you see.\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equivalent experiment as for Question 6 script 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cornflowerblue\">Question 14 (code):</strong> Implement a script plotting the number of infected over 75 time steps for $\\beta=0.3$ and $\\alpha=0.2$ fixed and on 3 contact networks:\n",
    "    \n",
    "* A regular graph of 200 vertices with degree 2.\n",
    "* A regular graph of 200 vertices with degree 5.\n",
    "* A regular graph of 200 vertices with degree 10.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots of the number of infected people according to tau over 75 time \n",
    "# steps for 3 contact networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 15 (written):</strong> From the previous figure, explain why lockdown can be interesting when hospital places are lacking ?\n",
    "\n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "### 2.2 SAIR\n",
    "    \n",
    "<p style=\"text-align:justify;\">The vector containing the disease status <code>state</code> has to change a bit since we added a new state. Hence it will be an <code>Array{Int32,1}</code> where Susceptible=0, Infected=1, Recovered=2, and Alert=3.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 16 (code):</strong> Implement the <code>function SAIR</code> (respect the header and the specifications). You can use <code>rand</code> to translate the probabilities. Test your algorithm on <code>karat7</code>, <code>n7_2A</code>, and <code>toulouse_neigh</code> with arbitrary $\\beta$, $\\alpha$, and $t$. Alerted vertices should appear in a different color (<code>colorant\"lightgreen\"</code>).\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>new_state = SAIR(net,state,beta0,beta1,alpha,kappa,t)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function SAIR(net,state,beta0,beta1,alpha,kappa,t)\n",
    "    \"\"\"Take a contact network at a certain state and apply t time steps\n",
    "    of an SAIR model.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       state (Array{Int32,1}): disease status of each vertex\n",
    "       beta0 (Float64): infection rate when not alert\n",
    "       beta1 (Float64): infection rate when alert\n",
    "       alpha (Float64): curing rate\n",
    "       kappa (Float64): alerting rate\n",
    "       t (Int32): number of time step\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Int32,1}): The new state of the contact network after t time steps.\n",
    "    \"\"\"      \n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Karat7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on N7_2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on Toulouse_neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 17 (code):</strong> Implement the <code>function Simulation_SAIR</code> (respect the header and the specifications).\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>predictions, taus = Simulation_SAIR(net,nbinf,betas0,betas1,alphas,kappas,t,nbsimu)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function Simulation_SAIR(net,nbinf,betas0,betas1,alphas,kappas,t,nbsimu)\n",
    "    \"\"\"Take a contact network, different diseases (defined by \n",
    "    different parameters alpha and beta), a number of initial\n",
    "    infected people and process nbsimu simulations of SAIR over\n",
    "    t time steps. You will provide the prediction of the \n",
    "    percentage of infected at each time t as well as the \n",
    "    spreading rate of each disease.\n",
    "    \n",
    "    PARAMS\n",
    "       net (LightGraph): graph representing the contact network\n",
    "       nbinf (Int32): number of infected at the start of each \n",
    "            simulation\n",
    "       betas0 (Array{Float64,1}): array of infection rate when not alert on edges\n",
    "       betas1 (Array{Float64,1}): array of infection rate when alert on edges\n",
    "       alphas (Array{Float64,1}): array of curing rate on vertices\n",
    "       kappas (Array{Float64,1}): array of alerting rate on edges\n",
    "       t (Int32): number of time step\n",
    "       nbsimu (Int32): number of simulations\n",
    "    \n",
    "    RETURNS\n",
    "        (Array{Float64,3}): the prediction of the percentage of \n",
    "            infected, the percentage of susceptible and the \n",
    "            percentage of recovered at each time step and for each \n",
    "            disease. The first dimension contains the time steps,\n",
    "            the second contains the diseases, and the third the status\n",
    "            (Infected: [:,:,1], Recovered: [:,:,2], Susceptible: [:,:,3])\n",
    "        (Array{Float64,1}): effective spreading rate for each \n",
    "            disease\n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 18 (written):</strong> Run the script below comparing the number of infected of SAIR and SIR and comment what you see.\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Script launching prediction on one disease on toulouse_neigh and plotting the percentage\n",
    "# of infected at each time step for SIR and SAIR.\n",
    "predictions1, taus1 = Simulation_SAIR(toulouse_neigh,2,[0.2],[0.1],[0.1],[0.4],100,1000)\n",
    "predictions2, taus2 = Simulation_SIR(toulouse_neigh,2,[0.2],[0.1],100,1000)\n",
    "\n",
    "Plots.plot([predictions1[:,:,1] predictions2[:,:,1]],\n",
    "           label=[\"SAIR\" \"SIR\"],xlabel=\"t\",ylabel=\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 19 (written):</strong> Of course the presented SIS, SIR, and SAIR models are limitated in their modelization of the reality. Formulate few of these limitations (at least 2). \n",
    "Propose few algorithm addons/ideas (at least 2) which would make the models more complex and more accurate in regards to the reality.\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong>Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "## Part 3 - Discover patient zero\n",
    "    \n",
    "<p style=\"text-align:justify;\"> In the two previous parts you may have realised that understanding and controlling the spread of epidemics on contact networks is an important task. However, information about\n",
    "the origin of the epidemic could be also extremely useful to reduce or prevent future outbreaks. Thus, in this part we will focus on algorithm solutions to answer this issue.</p>\n",
    "    \n",
    "<p style=\"text-align:justify;\"> The stochastic nature of infection propagation makes the estimation of the epidemic origin intrinsically hard: indeed, different initial conditions can lead to the same configuration at the observation time. Methods such as the distance centrality or the Jordan center try to approximate it. They both rely on spatial information by stating that the first infected is probably at the center of the cluster of infection. Mathematically:\n",
    "    \n",
    "* The jordan center is expressed as $\\min_{v\\in \\mathcal{I}}\\max_{n\\in \\mathcal{I}}d(v,n)$ where $\\mathcal{I}$ is a connected component of the original contact network containing all infected and recovered vertices, and where $d(\\cdot,\\cdot)$ is the distance (= the shortest path) between 2 vertices (if not weighted graph each edge accounts for 1 unit). \n",
    "* The distance centrality is expressed as $\\min_{v\\in \\mathcal{I}}\\sum_{n\\in \\mathcal{I}}d(v,n)(\\delta_{n,I} + \\delta_{n,R}/\\alpha)$, where $\\delta_{n,I}=1$ if the vertex n is infected ($=0$ otherwise), and where $\\delta_{n,R}=1$ if the vertex n is recovered ($=0$ otherwise). You may note that in distance centrality we increase the weight of the recovered vertices by a factor $1/\\alpha$, it translates the fact that recovered vertices tend to be closer to the origin of the epidemic since they probably got ill before.\n",
    "    \n",
    "    \n",
    "We formulate the problem as follow: given a contact network and a snapshot of epidemic spread at a certain time, determine the infection source. A snapshot is a given <code>state</code> array for a contact network.</p>\n",
    "\n",
    "<br>\n",
    "    \n",
    "<p style=\"font-size:0.9em\"> A. Y. Lokhov, M. Mézard, H. Ohta, and L. Zdeborová, <cite>\"Inferring the origin of an epidemic with a dynamic message-passing algorithm\"</cite>, Physical Review (2014)</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 20 (code):</strong> Implement the <code>function jordan</code> (respect the header and the specifications). You will need to use the function <code>dijkstra_shortest_paths</code> of the LighGraphs library, refer to the doc for more information. If there are multiple minimal vertices, then return the first one.\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>zero = jordan(g,state,alpha)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function jordan(g,state)\n",
    "    \"\"\"Find patient zero by mean of the jordan center method.\n",
    "    \n",
    "    PARAMS\n",
    "        g (LightGraph): graph representing the contact network\n",
    "        state (Array{Int32,1}): disease status of each vertex\n",
    "    \n",
    "    RETURNS\n",
    "        (Int32): the patient zero vertex number \n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "    \n",
    "<strong style=\"color:cornflowerblue\">Question 21 (code):</strong> Implement the <code>function distance</code> (respect the header and the specifications). You will need to use the function <code>dijkstra_shortest_paths</code> of the LighGraphs library, refer to the doc for more information. If there are multiple minimal vertices, then return the first one.\n",
    "    \n",
    "<span style=\"font-size:0.9em\">The corrector should be able to write <code>zero = distance(g,state,alpha)</code> with your code.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function distance(g,state,alpha=1.)\n",
    "    \"\"\"Find patient zero by mean of the distance centrality method.\n",
    "    \n",
    "    PARAMS\n",
    "        g (LightGraph): graph representing the contact network\n",
    "        state (Array{Int32,1}): disease status of each vertex\n",
    "        alpha (Float64): curing rate\n",
    "    \n",
    "    RETURNS\n",
    "        (Int32): the patient zero vertex number \n",
    "    \"\"\"\n",
    "    # TODO\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:75%;margin:0 auto;\">\n",
    "\n",
    "<strong style=\"color:cadetblue\">Question 22 (written):</strong> Run the 3 following scripts using your functions <code>jordan</code> and <code>distance</code> and comment on the results.\n",
    "    \n",
    "The contact network is karat7 for 2 different patient zero and a $50\\times 50$ grid. The real patient zero (\"Z\"), your jordan (\"J\") and distance (\"D\") approximations are appearing in <code>colorant\"lightblue\"</code>.\n",
    "    \n",
    "<br>\n",
    "    \n",
    "<div style=\"background-color:#E7F1D1\"> <strong> Answer:</strong></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading a snapshot of karat7\n",
    "@load \"karat7_Q22_1.jld2\" g state pat_zero alpha beta loc_x loc_y\n",
    "\n",
    "# Run the patient zero finding function\n",
    "cent_pat_zero = distance(g,state,alpha)\n",
    "jor_pat_zero = jordan(g,state)\n",
    "\n",
    "# Some display options \n",
    "labels = Array{String, 1}(undef,nv(g))\n",
    "for k=1:nv(g)\n",
    "    if state[k]==1\n",
    "        labels[k]=\"I\"\n",
    "    elseif state[k]==2\n",
    "        labels[k]=\"R\"\n",
    "    else\n",
    "        labels[k]=\"S\"\n",
    "    end\n",
    "end\n",
    "\n",
    "if cent_pat_zero==jor_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J+Z\"\n",
    "elseif cent_pat_zero==jor_pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "elseif cent_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+Z\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "elseif jor_pat_zero==pat_zero\n",
    "    labels[jor_pat_zero]=\"J+Z\"\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "else\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "end\n",
    "\n",
    "nodecolor = [colorant\"lightseagreen\", colorant\"orange\", colorant\"purple\"]\n",
    "colors = nodecolor[state + ones(Int32,nv(g))]\n",
    "colors[pat_zero] = colorant\"lightblue\"\n",
    "colors[cent_pat_zero] = colorant\"lightblue\"\n",
    "colors[jor_pat_zero] = colorant\"lightblue\"\n",
    "\n",
    "# Display\n",
    "draw(PNG(\"karat7_Q22_1.png\", 20cm, 20cm), gplot(g,loc_x,loc_y,nodefillc=colors,nodelabel=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading a snapshot of karat7\n",
    "@load \"karat7_Q22_2.jld2\" g state pat_zero alpha beta loc_x loc_y\n",
    "\n",
    "# Run the patient zero finding function\n",
    "cent_pat_zero = distance(g,state,alpha)\n",
    "jor_pat_zero = jordan(g,state)\n",
    "\n",
    "# Some display options \n",
    "labels = Array{String, 1}(undef,nv(g))\n",
    "for k=1:nv(g)\n",
    "    if state[k]==1\n",
    "        labels[k]=\"I\"\n",
    "    elseif state[k]==2\n",
    "        labels[k]=\"R\"\n",
    "    else\n",
    "        labels[k]=\"S\"\n",
    "    end\n",
    "end\n",
    "\n",
    "if cent_pat_zero==jor_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J+Z\"\n",
    "elseif cent_pat_zero==jor_pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "elseif cent_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+Z\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "elseif jor_pat_zero==pat_zero\n",
    "    labels[jor_pat_zero]=\"J+Z\"\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "else\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "end\n",
    "\n",
    "nodecolor = [colorant\"lightseagreen\", colorant\"orange\", colorant\"purple\"]\n",
    "colors = nodecolor[state + ones(Int32,nv(g))]\n",
    "colors[pat_zero] = colorant\"lightblue\"\n",
    "colors[cent_pat_zero] = colorant\"lightblue\"\n",
    "colors[jor_pat_zero] = colorant\"lightblue\"\n",
    "\n",
    "# Display\n",
    "draw(PNG(\"karat7_Q22_2.png\", 20cm, 20cm), gplot(g,loc_x,loc_y,nodefillc=colors,nodelabel=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading a snapshot of grid50\n",
    "@load \"grid50_Q22.jld2\" g state pat_zero alpha beta loc_x loc_y\n",
    "\n",
    "# Run the patient zero finding function\n",
    "cent_pat_zero = distance(g,state,alpha)\n",
    "jor_pat_zero = jordan(g,state)\n",
    "\n",
    "# Some display options \n",
    "labels = Array{String, 1}(undef,nv(g))\n",
    "for k=1:nv(g)\n",
    "    if state[k]==1\n",
    "        labels[k]=\"I\"\n",
    "    elseif state[k]==2\n",
    "        labels[k]=\"R\"\n",
    "    else\n",
    "        labels[k]=\"S\"\n",
    "    end\n",
    "end\n",
    "\n",
    "if cent_pat_zero==jor_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J+Z\"\n",
    "elseif cent_pat_zero==jor_pat_zero\n",
    "    labels[cent_pat_zero]=\"C+J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "elseif cent_pat_zero==pat_zero\n",
    "    labels[cent_pat_zero]=\"C+Z\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "elseif jor_pat_zero==pat_zero\n",
    "    labels[jor_pat_zero]=\"J+Z\"\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "else\n",
    "    labels[cent_pat_zero]=\"C\"\n",
    "    labels[jor_pat_zero]=\"J\"\n",
    "    labels[pat_zero]=\"Z\"\n",
    "end\n",
    "\n",
    "nodecolor = [colorant\"lightseagreen\", colorant\"orange\", colorant\"purple\"]\n",
    "colors = nodecolor[state + ones(Int32,nv(g))]\n",
    "colors[pat_zero] = colorant\"lightblue\"\n",
    "colors[cent_pat_zero] = colorant\"lightblue\"\n",
    "colors[jor_pat_zero] = colorant\"lightblue\"\n",
    "\n",
    "# Display\n",
    "draw(PNG(\"grid50_Q22.png\", 100cm, 100cm), gplot(g,loc_x,loc_y,nodefillc=colors,nodelabel=labels))"
   ]
  }
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