Epidemiology304.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70bfc846",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Epidemiology 304</h1>\n",
    "<h1>Demographics</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5d27c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from ipywidgets import interact\n",
    "\n",
    "import epidemik\n",
    "from epidemik import EpiModel\n",
    "\n",
    "import tqdm as tq\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a161ba6",
   "metadata": {},
   "source": [
    "We start by printing out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5f3cd76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: a3e44a3dcde157c72d6d80b7dd5db96a95391cbd\n",
      "\n",
      "numpy     : 1.26.4\n",
      "pandas    : 2.2.3\n",
      "tqdm      : 4.66.4\n",
      "watermark : 2.4.3\n",
      "epidemik  : 0.1.3.3\n",
      "sklearn   : 1.6.1\n",
      "matplotlib: 3.8.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff26bd8",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0466dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51eecc4",
   "metadata": {},
   "source": [
    "# Basic SIR model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b560b69",
   "metadata": {},
   "source": [
    "We load the usual SIR model from the model library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfbf9365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Epidemic Model with 3 compartments and 2 transitions:\n",
      "Name: SIR\n",
      "\n",
      "Parameters:\n",
      "  beta : 0.2\n",
      "  mu : 0.1\n",
      "\n",
      "\n",
      "Transitions:\n",
      "  - I -> R mu\n",
      "  - S + I = I beta\n",
      "\n",
      "# R0=2.00\n"
     ]
    }
   ],
   "source": [
    "SIR = EpiModel.download_model('SIR.yaml')\n",
    "print(SIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5b95c6",
   "metadata": {},
   "source": [
    "And update the infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "560923b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIR.params['beta'] = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1645270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Epidemic Model with 3 compartments and 2 transitions:\n",
      "Name: SIR\n",
      "\n",
      "Parameters:\n",
      "  beta : 0.3\n",
      "  mu : 0.1\n",
      "\n",
      "\n",
      "Transitions:\n",
      "  - I -> R mu\n",
      "  - S + I = I beta\n",
      "\n",
      "# R0=3.00\n"
     ]
    }
   ],
   "source": [
    "print(SIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067c5e33",
   "metadata": {},
   "source": [
    "We set the inital conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04d2814f",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10_000\n",
    "I0 = 1\n",
    "R0 = 0\n",
    "S0 = N - I0 - R0\n",
    "tmax = 365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289d9f2d",
   "metadata": {},
   "source": [
    "Integrate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea95619b",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIR.integrate(tmax, S=S0, I=I0, R=R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501360e3",
   "metadata": {},
   "source": [
    "And visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4bd19d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x35b0e6490>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 896x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "(SIR.S/N).plot(ax=ax) \n",
    "(SIR.I/N).plot(ax=ax)\n",
    "(SIR.R/N).plot(ax=ax)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Population')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8373c4df",
   "metadata": {},
   "source": [
    "# Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7896e95-62f8-49e7-a07e-25f9509b8f71",
   "metadata": {},
   "source": [
    "Traditional models as the one above, keeps the total population constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d784486-2057-4ae3-850b-c616dd88de23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.650000e+02\n",
       "mean     1.000000e+04\n",
       "std      2.167828e-12\n",
       "min      1.000000e+04\n",
       "25%      1.000000e+04\n",
       "50%      1.000000e+04\n",
       "75%      1.000000e+04\n",
       "max      1.000000e+04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIR.values_.sum(axis=1).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc171c3-51d9-445b-b184-8f87d148c56b",
   "metadata": {},
   "source": [
    "We can change this by including demographics (births and deaths) into the model. First we explore the demographics functionality in isolation by adding a birth rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c412148",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple = EpiModel()\n",
    "simple.add_birth_rate('S', birth=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9190a16-80b7-4bfd-87d9-6443f615e6fe",
   "metadata": {},
   "source": [
    "meaning a (unrealistic) $10\\%$ growth per day. Integrating:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fd23d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple.integrate(160, S=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c372ec8c-dbd8-4aa2-b611-99785cc86aef",
   "metadata": {},
   "source": [
    "We can verify that it is correct by using a simple fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33e9edc6-c332-4b23-a9a8-5c114061368d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = simple.values_.index.values.reshape(-1, 1)\n",
    "# We take the log since we expect exponential growth\n",
    "y = np.log(simple.values_.values)\n",
    "\n",
    "lm  = LinearRegression()\n",
    "lm.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381f9920-2636-4b9a-97df-83adb11d28a7",
   "metadata": {},
   "source": [
    "Recovering the expected value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6baf34e-1961-48fd-b7bf-630fdbbede82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.10003492]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e1466f-aca4-4020-86ba-b3c54f3607f6",
   "metadata": {},
   "source": [
    "and visualizing. We can also add a death rate, which can by used to balance out the birth rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0c7a35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x35bb14e90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 896x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "ax.set_yscale('log')\n",
    "simple.S.plot(ax=ax)\n",
    "ax.scatter(X, np.exp(lm.predict(X)), c=colors[1], zorder=-1, s=50)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Population')\n",
    "ax.legend(['Model', 'Fit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6aebba6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple.add_death_rate('S', death=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09eb71cd-67ef-4dc5-88a2-8bf6707950c4",
   "metadata": {},
   "source": [
    "As a result, we expect that the population remains constant as before: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "65b9f4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple.integrate(tmax, S=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c3f1c4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Population')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 896x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "ax.set_yscale('log')\n",
    "simple.S.plot(ax=ax)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f363741d-193c-439e-a713-62f56fa7a639",
   "metadata": {},
   "source": [
    "# Fixed Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a624cf85-6233-4a77-93fc-03025f07e137",
   "metadata": {},
   "source": [
    "An alternative to a specific birth/death rate is to just have a fixed number of individuals being born at any given day. This could be a simple way to integrate the effect of case importation, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0cdeb85-b033-4f68-9ef0-91b1a29ce017",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed = EpiModel()\n",
    "\n",
    "new_individuals = 100\n",
    "\n",
    "fixed.add_birth_rate('S', rate=new_individuals, fixed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "68ee7857-8393-4a97-b444-c6232bb78ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed.integrate(160, S=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73230c9-7866-4e01-a29f-86dbd3df3065",
   "metadata": {},
   "source": [
    "As expected the population increases in a pre defined maner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbc5d836-9352-4a2d-82f9-1f969d3215fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>25500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>25600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>25700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>25800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>25900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           S\n",
       "1    10000.0\n",
       "2    10100.0\n",
       "3    10200.0\n",
       "4    10300.0\n",
       "5    10400.0\n",
       "..       ...\n",
       "156  25500.0\n",
       "157  25600.0\n",
       "158  25700.0\n",
       "159  25800.0\n",
       "160  25900.0\n",
       "\n",
       "[160 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed.values_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf43dea-5698-4c7f-a4ad-ee24e19ac40c",
   "metadata": {},
   "source": [
    "# SIR with demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98ec3f3",
   "metadata": {},
   "source": [
    "Now we build out our full SIR model with demographics. For simplicity, we set the birth and death rates to be the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d8996c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.01 # 1% turnover per day (!)\n",
    "\n",
    "SIR_demo = EpiModel()\n",
    "SIR_demo.add_interaction('S','I', 'I', beta=0.3)\n",
    "SIR_demo.add_spontaneous('I', 'R', mu=0.1)\n",
    "\n",
    "# Newborns start out in the S compartment\n",
    "SIR_demo.add_birth_rate('S', birth=gamma)\n",
    "# Death rate is the same for every compartment\n",
    "SIR_demo.add_death_rate(death=gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9c621ec-1054-46ab-8287-e218f922ec13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Epidemic Model with 3 compartments and 2 transitions:\n",
      "\n",
      "Parameters:\n",
      "  beta : 0.3\n",
      "  mu : 0.1\n",
      "  birth : 0.01\n",
      "  death : 0.01\n",
      "\n",
      "\n",
      "Transitions:\n",
      "  - I -> R death\n",
      "  - S + I = I beta\n",
      "\n",
      "\n",
      "Demographics:\n",
      "  - I ->: death # death rate\n",
      "  - -> S: birth # birth rate\n",
      "  - S ->: death # death rate\n",
      "  - R ->: death # death rate\n",
      "\n",
      "# R0=3.00\n"
     ]
    }
   ],
   "source": [
    "print(SIR_demo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cfbed45b-76e3-4174-a498-ae20f1eb6031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3ywGQQDU1NZoyZYoOHz5MeAPS3D333KOdO3eqvb2d8JaOmpqadOWVV36xxAogfdXX13+xXQJA+goGg2ppaVFxcbHbpSRNRoU3AAAA02XEnjcAAIB0QXgDAAAwCOENAADAIIQ3AAAAgxDeAAAADEJ4AwAAMAjhDQAAwCCENwAAAIMQ3gAAAAxCeAMAADAI4Q0AAMAghDcAAACDEN4AAAAMQngDAAAwCOENAADAIIQ3AAAAgxDeAAAADEJ4AwAAMAjhDQAAwCCENwAAAIMQ3gAAAAxCeAMAADAI4Q0AAMAghDcAAACDEN4AAAAMQngDAAAwCOENAADAIIQ3AAAAgxDeAAAADEJ4AwAAMAjhDQAAwCD/A07xK0CagQk/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x140 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 2))\n",
    "SIR_demo.draw_model(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "39b6582e",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIR_demo.integrate(365, S=S0, I=I0, R=R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc4159a-f0d5-4102-9672-6acafa6714ca",
   "metadata": {},
   "source": [
    "The impact of demographics is imediately visible:\n",
    "- As new `Susceptibles` are added into the system, the total number of `Susceptibles` can increase as the epidemic wanes\n",
    "- The death rate helps the `Infectious` population decrease quicker but remains non-zero thanks to the constant supply of `Susceptible` individuals\n",
    "- The constant supply of new individuals can result in a second epidemic wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06305b40-f769-41f0-aaa8-0c3f5e90ed33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x35bf83dd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 896x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "(SIR_demo.S/N).plot(ax=ax, label='S')\n",
    "(SIR_demo.I/N).plot(ax=ax, label='I')\n",
    "(SIR_demo.R/N).plot(ax=ax, label='R')\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Population')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07f7d60-d8d2-4598-bc8d-bf6c50287d69",
   "metadata": {},
   "source": [
    "We can make the effect obvious, by directly comparing this model with the original SIR model above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5df87d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Population')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 896x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "(SIR_demo.S/N).plot(ax=ax, label='S')\n",
    "(SIR.S/N).plot(ax=ax, linestyle='--', color=colors[0], lw=2)  # Original SIR for comparison\n",
    "(SIR_demo.I/N).plot(ax=ax, label='I')\n",
    "(SIR.I/N).plot(ax=ax, linestyle='--', color=colors[1], lw=2)\n",
    "(SIR_demo.R/N).plot(ax=ax, label='R')\n",
    "(SIR.R/N).plot(ax=ax, linestyle='--', color=colors[2], lw=2)\n",
    "\n",
    "# Create custom line objects for the legend\n",
    "custom_lines = [Line2D([0], [0], color=colors[0], linestyle='-'),\n",
    "                Line2D([0], [0], color=colors[1], linestyle='-'),\n",
    "                Line2D([0], [0], color=colors[2], linestyle='-'),\n",
    "                Line2D([0], [0], color=\"#757575\", linestyle='-'),\n",
    "                Line2D([0], [0], color=\"#757575\", lw=2, linestyle='--'),\n",
    "                ]\n",
    "\n",
    "# Add the custom lines to the legend\n",
    "ax.legend(custom_lines, ['S', 'I', 'R', 'Demographics', 'Original'], loc='upper right')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2948b11c",
   "metadata": {},
   "source": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}