SIR_model_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See updated lesson plan and source code https://github.com/jennifer-bio/SIR_workshop\n",
    "### Run code online with https://mybinder.org/v2/gh/jennifer-bio/SIR_workshop.git/master \n",
    "\n",
    "#### Notes on this notobook\n",
    "The code is writen in R which is a \"programming language and free software environment for statistical computing\"\n",
    "\n",
    "To run each grey block (cell), click on it and hit the 'run' button on the above tool bar - you need to run all of the cells otherwise the later cells will not work\n",
    "\n",
    "When there is a '#', this is a comment which is not run as code but is meant for the user to read \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "The following object is masked from 'package:reshape':\n",
      "\n",
      "    rename\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "options(warn = -1) # Suppress warnings\n",
    "\n",
    "#'library(X)' tells the compter to check all of the functions and terms in X referance library \n",
    "#this lets you use functions that you or others made \n",
    "library(reshape)\n",
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "\n",
    "options(repr.plot.width = 25, repr.plot.height = 8) # set plot size\n",
    "\n",
    "#feel free the ignore the red box that will pop up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Introduction to epidemiology and the  SIR model \n",
    "\n",
    "    Before getting started with the activity we will introduce a little bit of background\n",
    "    S: Susceptible (i.e. Healthy)\n",
    "    I: Infectious (i.e. Infected)\n",
    "    R: Removed (i.e. a person can be removed from population by vacine/dead/isolated quarantine)\n",
    "    \n",
    "    beta: transmission rate (susceptible -> infectious)\n",
    "    gamma: removal rate (infectious -> remove)\n",
    "    \n",
    "    R-repoductive number: beta*S/gamma\n",
    "    R0-Basic reporodcutive number: beta/gamma\n",
    "\n",
    "# What's what: setting up the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>S</th><th scope=col>I</th><th scope=col>R</th><th scope=col>city</th><th scope=col>beta</th><th scope=col>gamma</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>city_A</th><td>0.99</td><td>0.01</td><td>0.0</td><td>city_A</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_B</th><td>0.80</td><td>0.10</td><td>0.1</td><td>city_B</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_C</th><td>0.50</td><td>0.20</td><td>0.3</td><td>city_C</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_D</th><td>0.60</td><td>0.30</td><td>0.1</td><td>city_D</td><td>1.5</td><td>0.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & S & I & R & city & beta & gamma\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tcity\\_A & 0.99 & 0.01 & 0.0 & city\\_A & 1.5 & 0.2\\\\\n",
       "\tcity\\_B & 0.80 & 0.10 & 0.1 & city\\_B & 1.5 & 0.2\\\\\n",
       "\tcity\\_C & 0.50 & 0.20 & 0.3 & city\\_C & 1.5 & 0.2\\\\\n",
       "\tcity\\_D & 0.60 & 0.30 & 0.1 & city\\_D & 1.5 & 0.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 6\n",
       "\n",
       "| <!--/--> | S &lt;dbl&gt; | I &lt;dbl&gt; | R &lt;dbl&gt; | city &lt;chr&gt; | beta &lt;dbl&gt; | gamma &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| city_A | 0.99 | 0.01 | 0.0 | city_A | 1.5 | 0.2 |\n",
       "| city_B | 0.80 | 0.10 | 0.1 | city_B | 1.5 | 0.2 |\n",
       "| city_C | 0.50 | 0.20 | 0.3 | city_C | 1.5 | 0.2 |\n",
       "| city_D | 0.60 | 0.30 | 0.1 | city_D | 1.5 | 0.2 |\n",
       "\n"
      ],
      "text/plain": [
       "       S    I    R   city   beta gamma\n",
       "city_A 0.99 0.01 0.0 city_A 1.5  0.2  \n",
       "city_B 0.80 0.10 0.1 city_B 1.5  0.2  \n",
       "city_C 0.50 0.20 0.3 city_C 1.5  0.2  \n",
       "city_D 0.60 0.30 0.1 city_D 1.5  0.2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta  = 1.5   # transmission rate\n",
    "gamma = 0.2   # removal rate\n",
    "\n",
    "data = data.frame(\"S\" = as.double(), \"I\" = as.double(), \"R\" = as.double()) #make table with col S I and R called data\n",
    "\n",
    "#fills in table with 3 rows \n",
    "data[\"city_A\",] = c(0.99, 0.01, 0) \n",
    "data[\"city_B\",] = c(0.80, 0.10, 0.10)\n",
    "data[\"city_C\",] = c(0.50, 0.20, 0.30)\n",
    "data[\"city_D\",] = c(0.60, 0.30, 0.10)\n",
    "\n",
    "\n",
    "data$city  = rownames(data)\n",
    "\n",
    "#adds the same beta and gamma for each row\n",
    "data$beta  = beta\n",
    "data$gamma = gamma\n",
    "\n",
    "#prints table below\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling changes to the population over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#this is a function, we can call time_step(data) at anypoint after running this code \n",
    "#it will do the steps listed in the '{}'\n",
    "\n",
    "time_step = function(data){\n",
    "    #data$city is a list of the values (the city names) stored in the city col of the data table, \n",
    "    #here we are saying do everything in the '{}' for each city\n",
    "    #data['city_A'] refers to the row of data that is called 'city_A'\n",
    "    #data['city_A', \"S\"] refers to the value that is in 'city_A' row and 'S' col\n ",
    "    for (city in data$city){\n",
    "        S = data[city, \"S\"] # assign S\n",
    "        I = data[city, \"I\"] # assign I\n",
    "        R = data[city, \"R\"] # assign R\n",
    "        \n",
    "        beta  = data[city, \"beta\"] #saves the current value of beta for city as beta\n",
    "        gamma = data[city, \"gamma\"]\n",
    "        \n",
    "        data[city, \"S\"] = S - beta*S*I           # change in S\n",
    "        data[city, \"I\"] = I + beta*S*I - gamma*I # change in I\n",
    "        data[city, \"R\"] = R + gamma*I            # change in R\n",
    "    }\n",
    "    \n",
    "    return(data)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>S</th><th scope=col>I</th><th scope=col>R</th><th scope=col>city</th><th scope=col>beta</th><th scope=col>gamma</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>city_A</th><td>0.99</td><td>0.01</td><td>0.0</td><td>city_A</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_B</th><td>0.80</td><td>0.10</td><td>0.1</td><td>city_B</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_C</th><td>0.50</td><td>0.20</td><td>0.3</td><td>city_C</td><td>1.5</td><td>0.2</td></tr>\n",
       "\t<tr><th scope=row>city_D</th><td>0.60</td><td>0.30</td><td>0.1</td><td>city_D</td><td>1.5</td><td>0.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & S & I & R & city & beta & gamma\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tcity\\_A & 0.99 & 0.01 & 0.0 & city\\_A & 1.5 & 0.2\\\\\n",
       "\tcity\\_B & 0.80 & 0.10 & 0.1 & city\\_B & 1.5 & 0.2\\\\\n",
       "\tcity\\_C & 0.50 & 0.20 & 0.3 & city\\_C & 1.5 & 0.2\\\\\n",
       "\tcity\\_D & 0.60 & 0.30 & 0.1 & city\\_D & 1.5 & 0.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 6\n",
       "\n",
       "| <!--/--> | S &lt;dbl&gt; | I &lt;dbl&gt; | R &lt;dbl&gt; | city &lt;chr&gt; | beta &lt;dbl&gt; | gamma &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| city_A | 0.99 | 0.01 | 0.0 | city_A | 1.5 | 0.2 |\n",
       "| city_B | 0.80 | 0.10 | 0.1 | city_B | 1.5 | 0.2 |\n",
       "| city_C | 0.50 | 0.20 | 0.3 | city_C | 1.5 | 0.2 |\n",
       "| city_D | 0.60 | 0.30 | 0.1 | city_D | 1.5 | 0.2 |\n",
       "\n"
      ],
      "text/plain": [
       "       S    I    R   city   beta gamma\n",
       "city_A 0.99 0.01 0.0 city_A 1.5  0.2  \n",
       "city_B 0.80 0.10 0.1 city_B 1.5  0.2  \n",
       "city_C 0.50 0.20 0.3 city_C 1.5  0.2  \n",
       "city_D 0.60 0.30 0.1 city_D 1.5  0.2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAC7gAAAPACAMAAACSVcSAAAAAilBMVEUAAAAA7gAaGhozMzNN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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#run this cell with differnt paramters to see how things change due to parmaters\n",
    "\n",
    "# 1. Setting up the data (as before) -----------------\n",
    "\n",
    "beta  = 1.5   # transmission rate\n",
    "gamma = 0.2   # recovery rate\n",
    "\n",
    "data            = data.frame(\"S\" = as.double(), \"I\" = as.double(), \"R\" = as.double())\n",
    "data[\"city_A\",] = c(0.99, 0.01, 0) \n",
    "data[\"city_B\",] = c(0.80, 0.10, 0.10)\n",
    "data[\"city_C\",] = c(0.50, 0.20, 0.30)\n",
    "data[\"city_D\",] = c(0.60, 0.30, 0.10)\n",
    "\n",
    "data$city  = rownames(data)\n",
    "data$beta  = beta\n",
    "data$gamma = gamma\n",
    "\n",
    "data\n",
    "\n",
    "# 2. Model the dynamics over a given time -----------------\n",
    "\n",
    "master      = data            # save starting values to master\n",
    "master$time = 0               # add time column - starts at time 0 for each city \n",
    "\n",
    "for (i in 1:20){              # for each number in 1 though 20 time points - measured in months:\n",
    "    a      = time_step(data)  # call function 'time_step' and save updated data table to \"a\"\n",
    "    data   = a                # re-initialize \"data\"\n",
    "    a$time = i                # add time column\n",
    "    master = rbind(master, a) # save updated values (as recorded in a) to \"master\"\n",
    "}\n",
    "\n",
    "# 3. See the changes -----------------\n",
    "#dont worry about all the weirdness here just know it makes pretty pictures\n",
    "\n",
    "ggplot(melt(master[,c(\"S\", \"I\", \"R\", \"city\", \"time\")], id = c(\"time\", \"city\")), aes(x = time,  y = value, color = variable )) +  # assign x- and y-values\n",
    "    geom_line(size = 3) +  theme_bw() + facet_grid(~city) +                       # plot line graph with each city in separate plots\n",
    "    theme(axis.text    = element_text(size = 20),                         # increase axis text size     \n",
    "          axis.title   = element_text(size = 20),                         # increase axis title size\n",
    "          legend.text  = element_text(size = 20),                         # increase legent text size\n",
    "          legend.title = element_blank(),                                 # remove title in legend\n",
    "          strip.text   = element_text(size=25)) +                         # increase plot title size\n",
    "    ylab(\"Proportion of Population\") +                                    # set y-axis title\n",
    "    scale_color_manual(values = c(\"#00ee00\", \"#ff0000\", \"#c0c0c0\")) +     # set colors\n",
    "    guides(color = guide_legend(override = list(size = 20)))              # enlarge legend symbols\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Part 2: Shock to system\n",
    "    \n",
    "    Two types of events:\n",
    "        1. Shock      (i.e. one instance)\n",
    "        2. Behavorial (i.e. long-term change)\n",
    "    \n",
    "    Shock is a one-time event where transmission can occur (i.e. alters S and I)\n",
    "    \n",
    "        Example: concert\n",
    "        \n",
    "        - event_beta: the rate of transmission for one instance during event\n",
    "        \n",
    "            data[city, \"S\"] = data[city, \"S\"] - (events[event, \"event_beta\"] * data[city, \"S\"] * data[city, \"I\"])\n",
    "            data[city, \"I\"] = data[city, \"I\"] + (events[event, \"event_beta\"] * data[city, \"S\"] * data[city, \"I\"])\n",
    "        \n",
    "            1. = 0: no change\n",
    "            2. > 0: transmission during event\n",
    "            \n",
    "    Behavorial is an ongoing situation where tranmission/remove rates change (i.e. alters beta and/or gamma)\n",
    "    \n",
    "        Example: state opens, restaurants open\n",
    "    \n",
    "        - effect_of_beta: changes beta for the rest of simulation\n",
    "        \n",
    "            data[city, \"beta\"]  = data[city, \"beta\"]  * events[event, \"effect_on_beta\"]\n",
    "        \n",
    "            1. > 1: increases beta (i.e. higher tranmission rate)\n",
    "            2. < 1: decreases beta (i.e. lower transmission rate)\n",
    "            3. = 1: no change in beta\n",
    "            \n",
    "        - effect of gamma: changes gamma for rest of simulation\n",
    "        \n",
    "            data[city, \"gamma\"] = data[city, \"gamma\"] * events[event, \"effect_on_gamma\"]\n",
    "            \n",
    "            1. > 1: increases gamma (i.e. higher remove rate)\n",
    "            2. < 1: decreases gamma (i.e. lower remove rate) \n",
    "            3. = 1: no change in gamma\n",
    "    # Examples of events\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#Defining event effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>event_name</th><th scope=col>type</th><th scope=col>effect_on_beta</th><th scope=col>effect_on_gamma</th><th scope=col>event_beta</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>no_change</th><td>no_change            </td><td>behavorial</td><td>1.00</td><td>1.0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>concert</th><td>concert              </td><td>shock     </td><td>1.00</td><td>1.0</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>state_open</th><td>state_open           </td><td>behavorial</td><td>1.50</td><td>1.0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>hospital_full</th><td>hospital_full        </td><td>behavorial</td><td>1.00</td><td>0.8</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>improved_treatments</th><td>improved_treatments  </td><td>behavorial</td><td>1.00</td><td>2.0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>natural_disaster</th><td>natural_disaster     </td><td>shock     </td><td>1.00</td><td>1.0</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>distancing_compliance</th><td>distancing_compliance</td><td>behavorial</td><td>0.50</td><td>1.0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>vaccine</th><td>vaccine              </td><td>behavorial</td><td>0.05</td><td>1.0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>civil_unrest</th><td>civil_unrest         </td><td>shock     </td><td>1.00</td><td>1.0</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>schools_reopen_temp</th><td>schools_reopen_temp  </td><td>shock     </td><td>1.00</td><td>1.0</td><td>3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & event\\_name & type & effect\\_on\\_beta & effect\\_on\\_gamma & event\\_beta\\\\\n",
       "  & <chr> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tno\\_change & no\\_change             & behavorial & 1.00 & 1.0 & 0\\\\\n",
       "\tconcert & concert               & shock      & 1.00 & 1.0 & 2\\\\\n",
       "\tstate\\_open & state\\_open            & behavorial & 1.50 & 1.0 & 0\\\\\n",
       "\thospital\\_full & hospital\\_full         & behavorial & 1.00 & 0.8 & 0\\\\\n",
       "\timproved\\_treatments & improved\\_treatments   & behavorial & 1.00 & 2.0 & 0\\\\\n",
       "\tnatural\\_disaster & natural\\_disaster      & shock      & 1.00 & 1.0 & 4\\\\\n",
       "\tdistancing\\_compliance & distancing\\_compliance & behavorial & 0.50 & 1.0 & 0\\\\\n",
       "\tvaccine & vaccine               & behavorial & 0.05 & 1.0 & 0\\\\\n",
       "\tcivil\\_unrest & civil\\_unrest          & shock      & 1.00 & 1.0 & 3\\\\\n",
       "\tschools\\_reopen\\_temp & schools\\_reopen\\_temp   & shock      & 1.00 & 1.0 & 3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | event_name &lt;chr&gt; | type &lt;fct&gt; | effect_on_beta &lt;dbl&gt; | effect_on_gamma &lt;dbl&gt; | event_beta &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| no_change | no_change             | behavorial | 1.00 | 1.0 | 0 |\n",
       "| concert | concert               | shock      | 1.00 | 1.0 | 2 |\n",
       "| state_open | state_open            | behavorial | 1.50 | 1.0 | 0 |\n",
       "| hospital_full | hospital_full         | behavorial | 1.00 | 0.8 | 0 |\n",
       "| improved_treatments | improved_treatments   | behavorial | 1.00 | 2.0 | 0 |\n",
       "| natural_disaster | natural_disaster      | shock      | 1.00 | 1.0 | 4 |\n",
       "| distancing_compliance | distancing_compliance | behavorial | 0.50 | 1.0 | 0 |\n",
       "| vaccine | vaccine               | behavorial | 0.05 | 1.0 | 0 |\n",
       "| civil_unrest | civil_unrest          | shock      | 1.00 | 1.0 | 3 |\n",
       "| schools_reopen_temp | schools_reopen_temp   | shock      | 1.00 | 1.0 | 3 |\n",
       "\n"
      ],
      "text/plain": [
       "                      event_name            type       effect_on_beta\n",
       "no_change             no_change             behavorial 1.00          \n",
       "concert               concert               shock      1.00          \n",
       "state_open            state_open            behavorial 1.50          \n",
       "hospital_full         hospital_full         behavorial 1.00          \n",
       "improved_treatments   improved_treatments   behavorial 1.00          \n",
       "natural_disaster      natural_disaster      shock      1.00          \n",
       "distancing_compliance distancing_compliance behavorial 0.50          \n",
       "vaccine               vaccine               behavorial 0.05          \n",
       "civil_unrest          civil_unrest          shock      1.00          \n",
       "schools_reopen_temp   schools_reopen_temp   shock      1.00          \n",
       "                      effect_on_gamma event_beta\n",
       "no_change             1.0             0         \n",
       "concert               1.0             2         \n",
       "state_open            1.0             0         \n",
       "hospital_full         0.8             0         \n",
       "improved_treatments   2.0             0         \n",
       "natural_disaster      1.0             4         \n",
       "distancing_compliance 1.0             0         \n",
       "vaccine               1.0             0         \n",
       "civil_unrest          1.0             3         \n",
       "schools_reopen_temp   1.0             3         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Initialize table -----------------\n",
    "\n",
    "\n",
    "#make a tabel with col names of 'event_name', 'type', ect.\n",
    "events = data.frame(\"event_name\"      = as.character(\"no_change\"), \n",
    "                    \"type\"            = as.character(\"behavorial\"), \n",
    "                    \"effect_on_beta\"  = 1, \n",
    "                    \"effect_on_gamma\" = 1, \n",
    "                    \"event_beta\"      = 0)\n",
    "\n",
    "\n",
    "# 2. Add events -----------------\n",
    "\n",
    "\n",
    "\n",
    "temp <- data.frame(\"concert\"              , \"shock\"      , 1  , 1  , 2) #makes a row in a temporary table called temp\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\") #add names to that table \n",
    "                                                                                           # 'c()' collects everythin in brackets into a list\n",
    "                                                                                           # that list is saved as the col names of temp table\n",
    "events <- rbind(events, temp) #add temp table to main events table with 'rbind' (row bind)\n",
    "\n",
    "temp <- data.frame(\"state_open\"           , \"behavorial\" , 1.5, 1  , 0  )\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"hospital_full\"        , \"behavorial\" , 1  , 0.8, 0  )\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"improved_treatments\"  , \"behavorial\" , 1  , 2, 0  )\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"natural_disaster\"     , \"shock\"      , 1  , 1  , 4)\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"distancing_compliance\", \"behavorial\" , 0.5, 1  , 0  )\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"vaccine\"              , \"behavorial\" , 0.05, 1  , 0  )\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"civil_unrest\"         , \"shock\"      , 1  , 1  , 3)\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "temp <- data.frame(\"schools_reopen_temp\"  , \"shock\"      , 1  , 1  , 3)\n",
    "names(temp) <- c(\"event_name\" , \"type\", \"effect_on_beta\", \"effect_on_gamma\", \"event_beta\")\n",
    "events <- rbind(events, temp)\n",
    "\n",
    "\n",
    "\n",
    "# 3. Convert to numbers -----------------\n",
    "\n",
    "events$effect_on_beta  = as.double(events$effect_on_beta) #changes effect_on_beta that was saved as words into double, which is a type of number for computers\n",
    "events$effect_on_gamma = as.double(events$effect_on_gamma)\n",
    "events$event_beta      = as.double(events$event_beta)\n",
    "events$event_name      = as.character(events$event_name)\n",
    "rownames(events) = events$event_name\n",
    "\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling changes in beta and gamma \n",
    "#setting up event funtinos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function selects a random event and applies it to a random city, changing its corresponding parameters (i.e. S, I, beta, gamma). \n",
    "\n",
    "event = function(data, time){   \n",
    "    \n",
    "    \n",
    "    # You can use to get random events or pick the event by commenting (use '#') the line that you dont want to run\n",
    "    #event = \"vaccine\" # sets event as value, chosen from event_name: here it is vaccine\n",
    "    event = sample(events$event_name, 1) # Select random event the list of values in the event_name col of events\n",
    "    \n",
    "    # Select random city from the list of values in the city col of data\n",
    "    city  = sample(data$city, 1)\n",
    "    \n",
    "    if (events[event, \"type\"] == \"shock\") { message(\"Shock!\") }\n",
    "    else { message(\"Behavior change!\") }\n",
    "    \n",
    "    #if you do not want to see all the messages before the plots comment out (add '#') before all the lines with 'message'\n",
    "    message(paste(event, \"is happening in\", city))\n",
    "    \n",
    "    message(paste(\"effect on beta:\", events[event, \"effect_on_beta\"]))\n",
    "    message(paste(\"effect on gamma:\", events[event, \"effect_on_gamma\"]))\n",
    "    message(paste(\"event_beta:\", events[event, \"event_beta\"]))\n",
    "    \n",
    "    message(\"\\n\")\n",
    "    \n",
    "    # Change beta/gamma \n",
    "    \n",
    "    message(paste(\"At time\", time, \", if there had been no event: -------\"))\n",
    "    message(paste0(capture.output(data), collapse = \"\\n\"))\n",
    "    \n",
    "    data[city, \"beta\"]  = data[city, \"beta\"]  * events[event, \"effect_on_beta\"]\n",
    "    data[city, \"gamma\"] = data[city, \"gamma\"] * events[event, \"effect_on_gamma\"]\n",
    "    \n",
    "    data[city, \"S\"] = data[city, \"S\"] - (events[event, \"event_beta\"] * data[city, \"S\"] * data[city, \"I\"])\n",
    "    data[city, \"I\"] = data[city, \"I\"] + (events[event, \"event_beta\"] * data[city, \"S\"] * data[city, \"I\"])\n",
    "    \n",
    "    message(\"But with event: -------\")\n",
    "    message(paste0(capture.output(data), collapse = \"\\n\"))\n",
    "\n",
    "    return(data)\n",
    "\n",
    "}\n",
    "\n",
    "# This function allows us to see how this shock impacts the city over time\n",
    "time_step_event = function(data, time){\n",
    "    \n",
    "    data = event(data, time) #calls the event function with each time step (see above)\n",
    "    \n",
    "    for (city in rownames(data)){\n",
    "        S = data[city, \"S\"]\n",
    "        I = data[city, \"I\"]\n",
    "        R = data[city, \"R\"]\n",
    "\n",
    "        beta  = data[city, \"beta\"]\n",
    "        gamma = data[city, \"gamma\"]\n",
    "        \n",
    "        data[city, \"S\"] = S - beta*S*I\n",
    "        data[city, \"I\"] = I + beta*S*I - gamma*I\n",
    "        data[city, \"R\"] = R + gamma*I\n",
    "\n",
    "    }\n",
    "    \n",
    "    return(data)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Running the shocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Behavior change!\n",
      "hospital_full is happening in city_B\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 1 , if there had been no event: -------\n",
      "          S    I R beta gamma   city\n",
      "city_A 0.99 0.01 0  1.5   0.2 city_A\n",
      "city_B 0.99 0.01 0  1.5   0.2 city_B\n",
      "city_C 0.99 0.01 0  1.5   0.2 city_C\n",
      "city_D 0.99 0.01 0  1.5   0.2 city_D\n",
      "But with event: -------\n",
      "          S    I R beta gamma   city\n",
      "city_A 0.99 0.01 0  1.5  0.20 city_A\n",
      "city_B 0.99 0.01 0  1.5  0.16 city_B\n",
      "city_C 0.99 0.01 0  1.5  0.20 city_C\n",
      "city_D 0.99 0.01 0  1.5  0.20 city_D\n",
      "Behavior change!\n",
      "improved_treatments is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 2\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 2 , if there had been no event: -------\n",
      "             S       I      R beta gamma   city\n",
      "city_A 0.97515 0.02285 0.0020  1.5  0.20 city_A\n",
      "city_B 0.97515 0.02325 0.0016  1.5  0.16 city_B\n",
      "city_C 0.97515 0.02285 0.0020  1.5  0.20 city_C\n",
      "city_D 0.97515 0.02285 0.0020  1.5  0.20 city_D\n",
      "But with event: -------\n",
      "             S       I      R beta gamma   city\n",
      "city_A 0.97515 0.02285 0.0020  1.5  0.20 city_A\n",
      "city_B 0.97515 0.02325 0.0016  1.5  0.16 city_B\n",
      "city_C 0.97515 0.02285 0.0020  1.5  0.20 city_C\n",
      "city_D 0.97515 0.02285 0.0020  1.5  0.40 city_D\n",
      "Behavior change!\n",
      "vaccine is happening in city_A\n",
      "effect on beta: 0.05\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 3 , if there had been no event: -------\n",
      "               S          I       R beta gamma   city\n",
      "city_A 0.9417267 0.05170327 0.00657  1.5  0.20 city_A\n",
      "city_B 0.9411416 0.05353836 0.00532  1.5  0.16 city_B\n",
      "city_C 0.9417267 0.05170327 0.00657  1.5  0.20 city_C\n",
      "city_D 0.9417267 0.04713327 0.01114  1.5  0.40 city_D\n",
      "But with event: -------\n",
      "               S          I       R  beta gamma   city\n",
      "city_A 0.9417267 0.05170327 0.00657 0.075  0.20 city_A\n",
      "city_B 0.9411416 0.05353836 0.00532 1.500  0.16 city_B\n",
      "city_C 0.9417267 0.05170327 0.00657 1.500  0.20 city_C\n",
      "city_D 0.9417267 0.04713327 0.01114 1.500  0.40 city_D\n",
      "Behavior change!\n",
      "no_change is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 4 , if there had been no event: -------\n",
      "               S          I          R  beta gamma   city\n",
      "city_A 0.9380750 0.04501439 0.01691065 0.075  0.20 city_A\n",
      "city_B 0.8655609 0.12055298 0.01388614 1.500  0.16 city_B\n",
      "city_C 0.8686912 0.11439814 0.01691065 1.500  0.20 city_C\n",
      "city_D 0.8751467 0.09485995 0.02999331 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "               S          I          R  beta gamma   city\n",
      "city_A 0.9380750 0.04501439 0.01691065 0.075  0.20 city_A\n",
      "city_B 0.8655609 0.12055298 0.01388614 1.500  0.16 city_B\n",
      "city_C 0.8686912 0.11439814 0.01691065 1.500  0.20 city_C\n",
      "city_D 0.8751467 0.09485995 0.02999331 1.500  0.40 city_D\n",
      "Shock!\n",
      "civil_unrest is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 5 , if there had been no event: -------\n",
      "               S          I          R  beta gamma   city\n",
      "city_A 0.9349079 0.03917853 0.02591353 0.075  0.20 city_A\n",
      "city_B 0.7090420 0.25778343 0.03317461 1.500  0.16 city_B\n",
      "city_C 0.7196262 0.24058349 0.03979028 1.500  0.20 city_C\n",
      "city_D 0.7506222 0.18144053 0.06793728 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "               S          I          R  beta gamma   city\n",
      "city_A 0.9349079 0.03917853 0.02591353 0.075  0.20 city_A\n",
      "city_B 0.7090420 0.25778343 0.03317461 1.500  0.16 city_B\n",
      "city_C 0.7196262 0.24058349 0.03979028 1.500  0.20 city_C\n",
      "city_D 0.3420423 0.36762155 0.06793728 1.500  0.40 city_D\n",
      "Shock!\n",
      "civil_unrest is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 6 , if there had been no event: -------\n",
      "               S          I          R  beta gamma   city\n",
      "city_A 0.9321608 0.03408994 0.03374924 0.075  0.20 city_A\n",
      "city_B 0.4348731 0.49070698 0.07441996 1.500  0.16 city_B\n",
      "city_C 0.4599309 0.45216208 0.08790698 1.500  0.20 city_C\n",
      "city_D 0.1534291 0.40918613 0.21498590 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                 S          I          R  beta gamma   city\n",
      "city_A  0.93216082 0.03408994 0.03374924 0.075  0.20 city_A\n",
      "city_B  0.43487306 0.49070698 0.07441996 1.500  0.16 city_B\n",
      "city_C  0.45993095 0.45216208 0.08790698 1.500  0.20 city_C\n",
      "city_D -0.03491409 0.36632705 0.21498590 1.500  0.40 city_D\n",
      "Shock!\n",
      "civil_unrest is happening in city_B\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 7 , if there had been no event: -------\n",
      "                 S          I          R  beta gamma   city\n",
      "city_A  0.92977752 0.02965525 0.04056723 0.075  0.20 city_A\n",
      "city_B  0.11478019 0.73228673 0.15293308 1.500  0.16 city_B\n",
      "city_C  0.14798595 0.67367466 0.17833939 1.500  0.20 city_C\n",
      "city_D -0.01572913 0.20061127 0.36151672 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                 S          I          R  beta gamma   city\n",
      "city_A  0.92977752 0.02965525 0.04056723 0.075  0.20 city_A\n",
      "city_B -0.13737584 0.43049121 0.15293308 1.500  0.16 city_B\n",
      "city_C  0.14798595 0.67367466 0.17833939 1.500  0.20 city_C\n",
      "city_D -0.01572913 0.20061127 0.36151672 1.500  0.40 city_D\n",
      "Shock!\n",
      "concert is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 2\n",
      "\n",
      "\n",
      "At time 8 , if there had been no event: -------\n",
      "                  S          I          R  beta gamma   city\n",
      "city_A  0.927709561 0.02579216 0.04649828 0.075  0.20 city_A\n",
      "city_B -0.048667203 0.27290398 0.22181167 1.500  0.16 city_B\n",
      "city_C -0.001555626 0.68848130 0.31307433 1.500  0.20 city_C\n",
      "city_D -0.010995966 0.11563360 0.44176123 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                  S          I          R  beta gamma   city\n",
      "city_A  0.927709561 0.02579216 0.04649828 0.075  0.20 city_A\n",
      "city_B -0.048667203 0.27290398 0.22181167 1.500  0.16 city_B\n",
      "city_C -0.001555626 0.68848130 0.31307433 1.500  0.20 city_C\n",
      "city_D -0.008452959 0.11367871 0.44176123 1.500  0.40 city_D\n",
      "Shock!\n",
      "schools_reopen_temp is happening in city_B\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 9 , if there had been no event: -------\n",
      "                   S          I          R  beta gamma   city\n",
      "city_A  9.259150e-01 0.02242830 0.05165671 0.075  0.20 city_A\n",
      "city_B -2.874499e-02 0.20931713 0.26547631 1.500  0.16 city_B\n",
      "city_C  5.090313e-05 0.54917851 0.45077059 1.500  0.20 city_C\n",
      "city_D -7.011577e-03 0.06676584 0.48723271 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                   S          I          R  beta gamma   city\n",
      "city_A  9.259150e-01 0.02242830 0.05165671 0.075  0.20 city_A\n",
      "city_B -1.069453e-02 0.20260148 0.26547631 1.500  0.16 city_B\n",
      "city_C  5.090313e-05 0.54917851 0.45077059 1.500  0.20 city_C\n",
      "city_D -7.011577e-03 0.06676584 0.48723271 1.500  0.40 city_D\n",
      "Behavior change!\n",
      "no_change is happening in city_B\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 10 , if there had been no event: -------\n",
      "                   S          I          R  beta gamma   city\n",
      "city_A  9.243575e-01 0.01950014 0.05614237 0.075  0.20 city_A\n",
      "city_B -7.444442e-03 0.16693515 0.29789255 1.500  0.16 city_B\n",
      "city_C  8.970773e-06 0.43938474 0.56060629 1.500  0.20 city_C\n",
      "city_D -6.309376e-03 0.03935731 0.51393905 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                   S          I          R  beta gamma   city\n",
      "city_A  9.243575e-01 0.01950014 0.05614237 0.075  0.20 city_A\n",
      "city_B -7.444442e-03 0.16693515 0.29789255 1.500  0.16 city_B\n",
      "city_C  8.970773e-06 0.43938474 0.56060629 1.500  0.20 city_C\n",
      "city_D -6.309376e-03 0.03935731 0.51393905 1.500  0.40 city_D\n",
      "Behavior change!\n",
      "distancing_compliance is happening in city_D\n",
      "effect on beta: 0.5\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 11 , if there had been no event: -------\n",
      "                   S         I         R  beta gamma   city\n",
      "city_A  9.230056e-01 0.0169520 0.0600424 0.075  0.20 city_A\n",
      "city_B -5.580333e-03 0.1383614 0.3246022 1.500  0.16 city_B\n",
      "city_C  3.058342e-06 0.3515137 0.6484832 1.500  0.20 city_C\n",
      "city_D -5.936896e-03 0.0232419 0.5296820 1.500  0.40 city_D\n",
      "But with event: -------\n",
      "                   S         I         R  beta gamma   city\n",
      "city_A  9.230056e-01 0.0169520 0.0600424 0.075  0.20 city_A\n",
      "city_B -5.580333e-03 0.1383614 0.3246022 1.500  0.16 city_B\n",
      "city_C  3.058342e-06 0.3515137 0.6484832 1.500  0.20 city_C\n",
      "city_D -5.936896e-03 0.0232419 0.5296820 0.750  0.40 city_D\n",
      "Shock!\n",
      "natural_disaster is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "event_beta: 4\n",
      "\n",
      "\n",
      "At time 12 , if there had been no event: -------\n",
      "                   S          I         R  beta gamma   city\n",
      "city_A  9.218321e-01 0.01473511 0.0634328 0.075  0.20 city_A\n",
      "city_B -4.422179e-03 0.11506544 0.3467400 1.500  0.16 city_B\n",
      "city_C  1.445768e-06 0.28121258 0.7187860 1.500  0.20 city_C\n",
      "city_D -5.833408e-03 0.01384165 0.5389787 0.750  0.40 city_D\n",
      "But with event: -------\n",
      "                   S          I         R  beta gamma   city\n",
      "city_A  8.674989e-01 0.06586587 0.0634328 0.075  0.20 city_A\n",
      "city_B -4.422179e-03 0.11506544 0.3467400 1.500  0.16 city_B\n",
      "city_C  1.445768e-06 0.28121258 0.7187860 1.500  0.20 city_C\n",
      "city_D -5.833408e-03 0.01384165 0.5389787 0.750  0.40 city_D\n",
      "Behavior change!\n",
      "hospital_full is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 13 , if there had been no event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.632135e-01 0.056978088 0.07660597 0.075  0.20 city_A\n",
      "city_B -3.658919e-03 0.095891708 0.36515047 1.500  0.16 city_B\n",
      "city_C  8.359159e-07 0.224970672 0.77502849 1.500  0.20 city_C\n",
      "city_D -5.772850e-03 0.008244434 0.54451540 0.750  0.40 city_D\n",
      "But with event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.632135e-01 0.056978088 0.07660597 0.075  0.16 city_A\n",
      "city_B -3.658919e-03 0.095891708 0.36515047 1.500  0.16 city_B\n",
      "city_C  8.359159e-07 0.224970672 0.77502849 1.500  0.20 city_C\n",
      "city_D -5.772850e-03 0.008244434 0.54451540 0.750  0.40 city_D\n",
      "Behavior change!\n",
      "hospital_full is happening in city_C\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 14 , if there had been no event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.595247e-01 0.051550414 0.08572247 0.075  0.16 city_A\n",
      "city_B -3.132629e-03 0.080022745 0.38049314 1.500  0.16 city_B\n",
      "city_C  5.538310e-07 0.179976819 0.82002263 1.500  0.20 city_C\n",
      "city_D -5.737154e-03 0.004910965 0.54781317 0.750  0.40 city_D\n",
      "But with event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.595247e-01 0.051550414 0.08572247 0.075  0.16 city_A\n",
      "city_B -3.132629e-03 0.080022745 0.38049314 1.500  0.16 city_B\n",
      "city_C  5.538310e-07 0.179976819 0.82002263 1.500  0.16 city_C\n",
      "city_D -5.737154e-03 0.004910965 0.54781317 0.750  0.40 city_D\n",
      "Behavior change!\n",
      "hospital_full is happening in city_B\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 15 , if there had been no event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.562015e-01 0.046625511 0.09397053 0.075  0.16 city_A\n",
      "city_B -2.756607e-03 0.066843083 0.39329678 1.500  0.16 city_B\n",
      "city_C  4.043159e-07 0.151180678 0.84881892 1.500  0.16 city_C\n",
      "city_D -5.716023e-03 0.002925448 0.54977756 0.750  0.40 city_D\n",
      "But with event: -------\n",
      "                   S           I          R  beta gamma   city\n",
      "city_A  8.562015e-01 0.046625511 0.09397053 0.075 0.160 city_A\n",
      "city_B -2.756607e-03 0.066843083 0.39329678 1.500 0.128 city_B\n",
      "city_C  4.043159e-07 0.151180678 0.84881892 1.500 0.160 city_C\n",
      "city_D -5.716023e-03 0.002925448 0.54977756 0.750 0.400 city_D\n",
      "Behavior change!\n",
      "hospital_full is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 16 , if there had been no event: -------\n",
      "                   S           I         R  beta gamma   city\n",
      "city_A  8.532075e-01 0.042159492 0.1014306 0.075 0.160 city_A\n",
      "city_B -2.480217e-03 0.058010779 0.4018527 1.500 0.128 city_B\n",
      "city_C  3.126288e-07 0.126991861 0.8730078 1.500 0.160 city_C\n",
      "city_D -5.703482e-03 0.001742727 0.5509477 0.750 0.400 city_D\n",
      "But with event: -------\n",
      "                   S           I         R  beta gamma   city\n",
      "city_A  8.532075e-01 0.042159492 0.1014306 0.075 0.128 city_A\n",
      "city_B -2.480217e-03 0.058010779 0.4018527 1.500 0.128 city_B\n",
      "city_C  3.126288e-07 0.126991861 0.8730078 1.500 0.160 city_C\n",
      "city_D -5.703482e-03 0.001742727 0.5509477 0.750 0.400 city_D\n",
      "Shock!\n",
      "schools_reopen_temp is happening in city_C\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 17 , if there had been no event: -------\n",
      "                   S           I         R  beta gamma   city\n",
      "city_A  8.505097e-01 0.039460887 0.1068270 0.075 0.128 city_A\n",
      "city_B -2.264398e-03 0.050369580 0.4092781 1.500 0.128 city_B\n",
      "city_C  2.530768e-07 0.106673223 0.8933265 1.500 0.160 city_C\n",
      "city_D -5.696027e-03 0.001038182 0.5516448 0.750 0.400 city_D\n",
      "But with event: -------\n",
      "                   S           I         R  beta gamma   city\n",
      "city_A  8.505097e-01 0.039460887 0.1068270 0.075 0.128 city_A\n",
      "city_B -2.264398e-03 0.050369580 0.4092781 1.500 0.128 city_B\n",
      "city_C  1.720873e-07 0.106673278 0.8933265 1.500 0.160 city_C\n",
      "city_D -5.696027e-03 0.001038182 0.5516448 0.750 0.400 city_D\n",
      "Behavior change!\n",
      "distancing_compliance is happening in city_D\n",
      "effect on beta: 0.5\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 18 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.479925e-01 0.0369270331 0.1118780 0.075 0.128 city_A\n",
      "city_B -2.093312e-03 0.0437511886 0.4157254 1.500 0.128 city_B\n",
      "city_C  1.445516e-07 0.0896055809 0.9103942 1.500 0.160 city_C\n",
      "city_D -5.691592e-03 0.0006184738 0.5520601 0.750 0.400 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.479925e-01 0.0369270331 0.1118780 0.075 0.128 city_A\n",
      "city_B -2.093312e-03 0.0437511886 0.4157254 1.500 0.128 city_B\n",
      "city_C  1.445516e-07 0.0896055809 0.9103942 1.500 0.160 city_C\n",
      "city_D -5.691592e-03 0.0006184738 0.5520601 0.375 0.400 city_D\n",
      "Behavior change!\n",
      "improved_treatments is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 2\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 19 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.456440e-01 0.0345489114 0.1166047 0.075 0.128 city_A\n",
      "city_B -1.955935e-03 0.0380136591 0.4213255 1.500 0.128 city_B\n",
      "city_C  1.251226e-07 0.0752687074 0.9247311 1.500 0.160 city_C\n",
      "city_D -5.690272e-03 0.0003697643 0.5523075 0.375 0.400 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.456440e-01 0.0345489114 0.1166047 0.075 0.128 city_A\n",
      "city_B -1.955935e-03 0.0380136591 0.4213255 1.500 0.128 city_B\n",
      "city_C  1.251226e-07 0.0752687074 0.9247311 1.500 0.160 city_C\n",
      "city_D -5.690272e-03 0.0003697643 0.5523075 0.375 0.800 city_D\n",
      "Behavior change!\n",
      "no_change is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 20 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.434528e-01 3.231786e-02 0.1210269 0.075 0.128 city_A\n",
      "city_B -1.844407e-03 3.303638e-02 0.4261913 1.500 0.128 city_B\n",
      "city_C  1.109959e-07 6.322573e-02 0.9367741 1.500 0.160 city_C\n",
      "city_D -5.689483e-03 7.316383e-05 0.5526033 0.375 0.800 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.434528e-01 3.231786e-02 0.1210269 0.075 0.128 city_A\n",
      "city_B -1.844407e-03 3.303638e-02 0.4261913 1.500 0.128 city_B\n",
      "city_C  1.109959e-07 6.322573e-02 0.9367741 1.500 0.160 city_C\n",
      "city_D -5.689483e-03 7.316383e-05 0.5526033 0.375 0.800 city_D\n",
      "Behavior change!\n",
      "hospital_full is happening in city_D\n",
      "effect on beta: 1\n",
      "effect on gamma: 0.8\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 21 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.414084e-01 3.022557e-02 0.1251636 0.075 0.128 city_A\n",
      "city_B -1.753008e-03 2.871633e-02 0.4304199 1.500 0.128 city_B\n",
      "city_C  1.004692e-07 5.310962e-02 0.9468903 1.500 0.160 city_C\n",
      "city_D -5.689327e-03 1.447667e-05 0.5526618 0.375 0.800 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.414084e-01 3.022557e-02 0.1251636 0.075 0.128 city_A\n",
      "city_B -1.753008e-03 2.871633e-02 0.4304199 1.500 0.128 city_B\n",
      "city_C  1.004692e-07 5.310962e-02 0.9468903 1.500 0.160 city_C\n",
      "city_D -5.689327e-03 1.447667e-05 0.5526618 0.375 0.640 city_D\n",
      "Shock!\n",
      "schools_reopen_temp is happening in city_C\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 22 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.395010e-01 2.826410e-02 0.1290325 0.075 0.128 city_A\n",
      "city_B -1.677498e-03 2.496513e-02 0.4340956 1.500 0.128 city_B\n",
      "city_C  9.246540e-08 4.461209e-02 0.9553878 1.500 0.160 city_C\n",
      "city_D -5.689296e-03 5.180714e-06 0.5526711 0.375 0.640 city_D\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.395010e-01 2.826410e-02 0.1290325 0.075 0.128 city_A\n",
      "city_B -1.677498e-03 2.496513e-02 0.4340956 1.500 0.128 city_B\n",
      "city_C  8.009017e-08 4.461210e-02 0.9553878 1.500 0.160 city_C\n",
      "city_D -5.689296e-03 5.180714e-06 0.5526711 0.375 0.640 city_D\n",
      "Behavior change!\n",
      "state_open is happening in city_C\n",
      "effect on beta: 1.5\n",
      "effect on gamma: 1\n",
      "event_beta: 0\n",
      "\n",
      "\n",
      "At time 23 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.377214e-01 2.642587e-02 0.1326503 0.075 0.128 city_A\n",
      "city_B -1.614680e-03 2.170677e-02 0.4372912 1.500 0.128 city_B\n",
      "city_C  7.473069e-08 3.747417e-02 0.9625257 1.500 0.160 city_C\n",
      "city_D -5.689285e-03 1.854004e-06 0.5526744 0.375 0.640 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.377214e-01 2.642587e-02 0.1326503 0.075 0.128 city_A\n",
      "city_B -1.614680e-03 2.170677e-02 0.4372912 1.500 0.128 city_B\n",
      "city_C  7.473069e-08 3.747417e-02 0.9625257 2.250 0.160 city_C\n",
      "city_D -5.689285e-03 1.854004e-06 0.5526744 0.375 0.640 city_D\n",
      "Shock!\n",
      "natural_disaster is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 4\n",
      "\n",
      "\n",
      "At time 24 , if there had been no event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  8.360611e-01 2.470367e-02 0.1360328 0.075 0.128 city_A\n",
      "city_B -1.562105e-03 1.887573e-02 0.4400696 1.500 0.128 city_B\n",
      "city_C  6.842963e-08 3.147831e-02 0.9685216 2.250 0.160 city_C\n",
      "city_D -5.689281e-03 6.634860e-07 0.5526756 0.375 0.640 city_D\n",
      "But with event: -------\n",
      "                   S            I         R  beta gamma   city\n",
      "city_A  7.534460e-01 9.915521e-02 0.1360328 0.075 0.128 city_A\n",
      "city_B -1.562105e-03 1.887573e-02 0.4400696 1.500 0.128 city_B\n",
      "city_C  6.842963e-08 3.147831e-02 0.9685216 2.250 0.160 city_C\n",
      "city_D -5.689281e-03 6.634860e-07 0.5526756 0.375 0.640 city_D\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAC7gAAAPACAMAAACSVcSAAAAAn1BMVEUAAAAaGhozMzNNTU1U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\nHGdAHgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All of the cities have the same starting conditions, just differnt events \n",
    "# to see the plots you need to scoll all the way to the bottom - see comment in cell above on how to remove\n",
    "\n",
    "# 1. Initialize our data (same as Part 1) -----------------\n",
    "\n",
    "beta  = 1.5  # initialize beta\n",
    "gamma = 0.2  # initialize gamma\n",
    "\n",
    "data            = data.frame(\"S\" = as.double(), \"I\" = as.double(), \"R\" = as.double())\n",
    "data[\"city_A\",] = c(0.99, 0.01, 0)\n",
    "data[\"city_B\",] = c(0.99, 0.01, 0)\n",
    "data[\"city_C\",] = c(0.99, 0.01, 0)\n",
    "data[\"city_D\",] = c(0.99, 0.01, 0)\n",
    "\n",
    "\n",
    "data$beta       = beta\n",
    "data$gamma      = gamma\n",
    "data$city       = rownames(data)\n",
    "\n",
    "original.data   = data         # save this data - we will need this again\n",
    "\n",
    "#3. Model over time( WITH event) -----------------\n",
    "\n",
    "data = original.data\n",
    "\n",
    "yes_event      = data                           # save starting values to master\n",
    "yes_event$time = 0                              # add time column \n",
    "\n",
    "for (month in 1:24){                                # for a given time:\n",
    "    a = time_step_event(data, month)\n",
    "    data   = a                                  # re-initialize \"data\"\n",
    "    a$time = month                                  # add time column\n",
    "    yes_event = rbind(yes_event, a)             # save new values to \"master\"\n",
    "}\n",
    "  \n",
    "yes_event$has_event = TRUE\n",
    "\n",
    "\n",
    "# 4. Visualize our results -----------------\n",
    "#again do not worry about this plotting nonsence\n",
    "\n",
    "ggplot(melt(yes_event[,c(\"city\", \"has_event\", \"time\", \"S\", \"I\", \"R\")], id = c(\"city\", \"has_event\", \"time\")), \n",
    "       aes(x = time,  y = value, color = variable)) +  # assign x- and y-values\n",
    "    geom_line(alpha = 0.6, size = 3) + \n",
    "    facet_grid(~city) + theme_bw() +                             # plot line graph with each city in separate plots\n",
    "    theme(axis.text    = element_text(size = 20),                            # increase axis text size     \n",
    "          axis.title   = element_text(size = 20),                            # increase axis title size\n",
    "          legend.text  = element_text(size = 20),                            # increase legent text size\n",
    "          legend.title = element_blank(),                                    # remove title in legend\n",
    "          strip.text   = element_text(size=25)) +                            # increase plot title size\n",
    "    ylab(\"Proportion of Population\") +                                       # set y-axis title\n",
    "    scale_color_manual(values = c(\"#00ee00\", \"#ff0000\", \"#c0c0c0\"),\n",
    "                       labels = c(\"Susceptible\", \"Infected\", \"Removed\")) +        # set colors\n",
    "    guides(color = guide_legend(override = list(size = 20))) +               # enlarge legend symbols\n",
    "    scale_linetype_manual(values=c(\"solid\", \"dotted\"),\n",
    "                          labels = c(\"yes_event\")) + xlab(\"Months\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Explore on your own time - how one event makes changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Shock!\n",
      "civil_unrest is happening in city_A\n",
      "effect on beta: 1\n",
      "effect on gamma: 1\n",
      "event_beta: 3\n",
      "\n",
      "\n",
      "At time 5 , if there had been no event: -------\n",
      "                  S         I          R beta gamma   city\n",
      "city_A 7.196262e-01 0.2405835 0.03979028  1.5   0.2 city_A\n",
      "city_B 3.746293e-02 0.6195179 0.34301920  1.5   0.2 city_B\n",
      "city_C 2.401142e-02 0.4030447 0.57294388  1.5   0.2 city_C\n",
      "city_D 6.668904e-05 0.4848059 0.51512737  1.5   0.2 city_D\n",
      "But with event: -------\n",
      "                  S         I          R beta gamma   city\n",
      "city_A 2.002357e-01 0.3851037 0.03979028  1.5   0.2 city_A\n",
      "city_B 3.746293e-02 0.6195179 0.34301920  1.5   0.2 city_B\n",
      "city_C 2.401142e-02 0.4030447 0.57294388  1.5   0.2 city_C\n",
      "city_D 6.668904e-05 0.4848059 0.51512737  1.5   0.2 city_D\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAC7gAAAPACAMAAACSVcSAAAAA81BMVEUAAAAaGhom7iYp8Skz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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Initialize our data (same as Part 1) -----------------\n",
    "\n",
    "beta  = 1.5  # initialize beta\n",
    "gamma = 0.2  # initialize gamma\n",
    "\n",
    "data            = data.frame(\"S\" = as.double(), \"I\" = as.double(), \"R\" = as.double())\n",
    "data[\"city_A\",] = c(0.99, 0.01, 0)\n",
    "data[\"city_B\",] = c(0.80, 0.10, 0.10)\n",
    "data[\"city_C\",] = c(0.50, 0.20, 0.30)\n",
    "data[\"city_D\",] = c(0.60, 0.30, 0.10)\n",
    "\n",
    "data$beta       = beta\n",
    "data$gamma      = gamma\n",
    "data$city       = rownames(data)\n",
    "\n",
    "original.data   = data         # save this data - we will need this again\n",
    "\n",
    "\n",
    "# 2. Model over time ( WITHOUT event) -----------------\n",
    "\n",
    "no_event      = data                            # save starting values to master\n",
    "no_event$time = 0                               # add time column \n",
    "\n",
    "for (i in 1:20){                                # for 20 time points:\n",
    "    a      = time_step(data)                    # call function and save new values to \"a\"\n",
    "    data   = a                                  # re-initialize \"data\"\n",
    "    a$time = i                                  # add time column\n",
    "    no_event = rbind(no_event, a)               # save new values to \"master\"\n",
    "}\n",
    "no_event$has_event  = FALSE\n",
    "\n",
    "\n",
    "#3. Model over time( WITH event) -----------------\n",
    "\n",
    "data = original.data\n",
    "\n",
    "yes_event      = data                           # save starting values to master\n",
    "yes_event$time = 0                              # add time column \n",
    "\n",
    "for (i in 1:20){                                # for a given time:\n",
    "    if (i == 5){ a = time_step_event(data, i) } # have event happen @ time = 5\n",
    "    else { a = time_step(data) }                # other times happen normally\n",
    "    data   = a                                  # re-initialize \"data\"\n",
    "    a$time = i                                  # add time column\n",
    "    yes_event = rbind(yes_event, a)             # save new values to \"master\"\n",
    "}\n",
    "  \n",
    "yes_event$has_event = TRUE\n",
    "\n",
    "\n",
    "# 4. Visualize our results -----------------\n",
    "\n",
    "master = rbind(no_event, yes_event)\n",
    "\n",
    "ggplot(melt(master[,c(\"city\", \"has_event\", \"time\", \"S\", \"I\", \"R\")], id = c(\"city\", \"has_event\", \"time\")), \n",
    "       aes(x = time,  y = value, linetype = has_event, color = variable)) +  # assign x- and y-values\n",
    "    geom_line(alpha = 0.6, size = 3) + \n",
    "    facet_grid(~city) + theme_bw() +                             # plot line graph with each city in separate plots\n",
    "    theme(axis.text    = element_text(size = 20),                            # increase axis text size     \n",
    "          axis.title   = element_text(size = 20),                            # increase axis title size\n",
    "          legend.text  = element_text(size = 20),                            # increase legent text size\n",
    "          legend.title = element_blank(),                                    # remove title in legend\n",
    "          strip.text   = element_text(size=25)) +                            # increase plot title size\n",
    "    ylab(\"Proportion of Population\") +                                       # set y-axis title\n",
    "    scale_color_manual(values = c(\"#00ee00\", \"#ff0000\", \"#c0c0c0\")) +        # set colors\n",
    "    guides(color = guide_legend(override = list(size = 20))) +               # enlarge legend symbols\n",
    "    scale_linetype_manual(values=c(\"solid\", \"dotted\"),\n",
    "                          labels = c(\"no_event\", \"yes_event\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}