{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\"Data
\n", "

Epidemiology 102

\n", "

All CoVID19 models are wrong, but some are useful

\n", "

Bruno Gonçalves
\n", " www.data4sci.com
\n", " @bgoncalves, @data4sci

\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "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 as mpl \n", "import matplotlib.pyplot as plt \n", "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import epidemik\n", "from epidemik import EpiModel\n", "\n", "import watermark\n", "\n", "%load_ext watermark\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by print out the versions of the libraries we're using for future reference" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python implementation: CPython\n", "Python version : 3.13.2\n", "IPython version : 8.32.0\n", "\n", "Compiler : Clang 16.0.0 (clang-1600.0.26.6)\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: 6d9f1a6eb4084a40d0e306ad0ba8a2eaa55e5c85\n", "\n", "matplotlib: 3.10.0\n", "numpy : 2.2.2\n", "watermark : 2.5.0\n", "pandas : 2.2.3\n", "epidemik : 0.1.2\n", "\n" ] } ], "source": [ "%watermark -n -v -m -g -iv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load default figure style and extract the color cycle for ease of reference" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "plt.style.use('./d4sci.mplstyle')\n", "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SIR Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our first step is to reimplement the SIR model from the previous post" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "beta = 0.2\n", "mu = 0.1\n", "\n", "SIR = EpiModel()\n", "SIR.add_interaction('S', 'I', 'I', beta=beta)\n", "SIR.add_spontaneous('I', 'R', mu=mu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily make sure that it looks correct" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Epidemic Model with 3 compartments and 2 transitions:\n", "\n", "Parameters:\n", " beta : 0.200000\n", " mu : 0.100000\n", "\n", "\n", "Transitions:\n", " - S + I = I beta\n", " - I -> R mu\n", "\n", "# R0=2.00\n" ] } ], "source": [ "print(SIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we integrate it using our standard parameter values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "N = 100000\n", "I0 = 10\n", "\n", "SIR.integrate(365, S=N-I0, I=I0, R=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And a quick visualization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax = SIR.plot(normed=True)\n", "ax.legend(['Susceptible', 'Infectious', 'Recovered'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will be the basis that we will build up on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Incubation Period - SEIR Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the main factors that is missing from the simple SIR model, is that a Latent or incubation period. We can add this feature to our model by creating a new compartment E(xposed) that is Infected, but not yet infectious. The parameter $\\epsilon$ determines the rate at which individuals develop the ability to infect others" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "epsilon = 0.4\n", "\n", "SEIR = EpiModel()\n", "SEIR.add_interaction('S', 'E', 'I', beta=beta)\n", "SEIR.add_spontaneous('E', 'I', epsilon=epsilon)\n", "SEIR.add_spontaneous('I', 'R', mu=mu)\n", "SEIR.name = \"SEIR\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a third transition" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Epidemic Model with 4 compartments and 3 transitions:\n", "Name: SEIR\n", "\n", "Parameters:\n", " beta : 0.200000\n", " epsilon : 0.400000\n", " mu : 0.100000\n", "\n", "\n", "Transitions:\n", " - S + I = E beta\n", " - E -> I mu\n", " - I -> R mu\n", "\n", "# R0=2.00\n" ] } ], "source": [ "print(SEIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which has a direct impact on the time line of the system" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "SEIR.integrate(365, S=N-I0, E=0, I=I0, R=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value of $R_0$ remains the same, but the epidemic takes place over a longer timeframe" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got both 'c' and 'color', which are aliases of one another\n" ] }, { "ename": "NotInitialized", "evalue": "You must call integrate() or simulate() first", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "File 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\u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepi_colors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 330\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues_\u001b[38;5;241m.\u001b[39mcolumns)\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_core.py:1030\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1028\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m-> 1030\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:501\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 500\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\n\u001b[0;32m--> 501\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1550\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 1548\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m-> 1550\u001b[0m newlines \u001b[38;5;241m=\u001b[39m \u001b[43mplotf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1551\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1552\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1553\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1554\u001b[0m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1555\u001b[0m \u001b[43m 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\u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1586\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m 1585\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m-> 1586\u001b[0m lines \u001b[38;5;241m=\u001b[39m \u001b[43mMPLPlot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1776\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLine2D\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/matplotlib/cbook.py:1790\u001b[0m, in \u001b[0;36mnormalize_kwargs\u001b[0;34m(kw, alias_mapping)\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m canonical \u001b[38;5;129;01min\u001b[39;00m canonical_to_seen:\n\u001b[0;32m-> 1790\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot both \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcanonical_to_seen[canonical]\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1791\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, which are aliases of one another\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1792\u001b[0m canonical_to_seen[canonical] \u001b[38;5;241m=\u001b[39m k\n", "\u001b[0;31mTypeError\u001b[0m: Got both 'c' and 'color', which are aliases of one another", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNotInitialized\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43mSEIR\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnormed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSusceptible\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExposed\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInfectious\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRecovered\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/epidemik/EpiModel.py:343\u001b[0m, in \u001b[0;36mEpiModel.plot\u001b[0;34m(self, title, normed, show, ax, **kwargs)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28mprint\u001b[39m(e)\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotInitialized(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must call integrate() or simulate() first\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNotInitialized\u001b[0m: You must call integrate() or simulate() first" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = SEIR.plot(normed=True, color=[colors[0], colors[4], colors[1], colors[2]])\n", "ax.legend(['Susceptible', 'Exposed', 'Infectious', 'Recovered'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily illustrate this by simply plotting the infected compartments of the SIR and SEIR models, side by side." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = (SEIR.I/N).plot(color=colors[1], linestyle='-', label='SEIR')\n", "(SIR.I/N).plot(color=colors[1], linestyle='--', label='SIR', ax=ax)\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Infectious')\n", "ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We clearly see that the presence of an Esposed compartment causes a delay in the number of infectious individuals as well as a flattening of the curve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Temporary Immunity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There have been recent reports that immunity might not be permanent for those who recover from the infection. We can easily add this feature to our model by including an extra transition from the *Recovered* compartment back to *Susceptible*" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "rho = 0.3\n", "\n", "SEIRS = EpiModel()\n", "SEIRS.add_interaction('S', 'E', 'I', beta=beta)\n", "SEIRS.add_spontaneous('E', 'I', epsilon=epsilon)\n", "SEIRS.add_spontaneous('I', 'R', mu=mu)\n", "SEIRS.add_spontaneous('R', 'S', rho=rho)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Naturally, the time scale of the system depend on the exact value of $\\rho$ chosen, but the main features will remain the same. Our model is now:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Epidemic Model with 4 compartments and 4 transitions:\n", "\n", "Parameters:\n", " beta : 0.200000\n", " epsilon : 0.400000\n", " mu : 0.100000\n", " rho : 0.300000\n", "\n", "\n", "Transitions:\n", " - S + I = E beta\n", " - E -> I rho\n", " - I -> R rho\n", " - R -> S rho\n", "\n" ] } ], "source": [ "print(SEIRS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's integrate the equations of the model and examine the result" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got both 'c' and 'color', which are aliases of one another\n" ] }, { "ename": "NotInitialized", "evalue": "You must call integrate() or simulate() first", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/epidemik/EpiModel.py:328\u001b[0m, in \u001b[0;36mEpiModel.plot\u001b[0;34m(self, title, normed, show, ax, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues_\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m--> 328\u001b[0m \u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues_\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomp\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m 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\u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1550\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 1548\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m-> 1550\u001b[0m newlines \u001b[38;5;241m=\u001b[39m \u001b[43mplotf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1551\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1552\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1553\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1554\u001b[0m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1555\u001b[0m \u001b[43m 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\u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1586\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m 1585\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m-> 1586\u001b[0m lines \u001b[38;5;241m=\u001b[39m \u001b[43mMPLPlot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot both \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcanonical_to_seen[canonical]\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1791\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, which are aliases of one another\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1792\u001b[0m canonical_to_seen[canonical] \u001b[38;5;241m=\u001b[39m k\n", "\u001b[0;31mTypeError\u001b[0m: Got both 'c' and 'color', which are aliases of one another", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNotInitialized\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[20], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m SEIRS\u001b[38;5;241m.\u001b[39mintegrate(\u001b[38;5;241m365\u001b[39m, S\u001b[38;5;241m=\u001b[39mN\u001b[38;5;241m-\u001b[39mI0, E\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, I\u001b[38;5;241m=\u001b[39mI0, R\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43mSEIRS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnormed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSusceptible\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExposed\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInfectious\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRecovered\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/epidemik/EpiModel.py:343\u001b[0m, in \u001b[0;36mEpiModel.plot\u001b[0;34m(self, title, normed, show, ax, **kwargs)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28mprint\u001b[39m(e)\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotInitialized(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must call integrate() or simulate() first\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNotInitialized\u001b[0m: You must call integrate() or simulate() first" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SEIRS.integrate(365, S=N-I0, E=0, I=I0, R=0)\n", "\n", "ax = SEIRS.plot(normed=True, color=[colors[0], colors[4], colors[1], colors[2]])\n", "ax.legend(['Susceptible', 'Exposed', 'Infectious', 'Recovered'])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "rho = 0.03\n", "\n", "SEIRS2 = EpiModel()\n", "SEIRS2.add_interaction('S', 'E', 'I', beta=beta)\n", "SEIRS2.add_spontaneous('E', 'I', epsilon=beta)\n", "SEIRS2.add_spontaneous('I', 'R', mu=mu)\n", "SEIRS2.add_spontaneous('R', 'S', rho=rho)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "SEIRS2.integrate(365, S=N-I0, E=0, I=I0, R=0)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got both 'c' and 'color', which are aliases of one another\n" ] }, { "ename": "NotInitialized", "evalue": "You must call integrate() or simulate() first", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/epidemik/EpiModel.py:328\u001b[0m, in \u001b[0;36mEpiModel.plot\u001b[0;34m(self, title, normed, show, ax, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues_\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m--> 328\u001b[0m \u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues_\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomp\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepi_colors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcomp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 330\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues_\u001b[38;5;241m.\u001b[39mcolumns)\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_core.py:1030\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1028\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m-> 1030\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:501\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 500\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\n\u001b[0;32m--> 501\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1550\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 1548\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m-> 1550\u001b[0m newlines \u001b[38;5;241m=\u001b[39m \u001b[43mplotf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1551\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1552\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1553\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1554\u001b[0m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1555\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumn_num\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1556\u001b[0m \u001b[43m \u001b[49m\u001b[43mstacking_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacking_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1557\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_errorbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_errorbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1558\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1559\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[38;5;241m0\u001b[39m], label)\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:1586\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m 1585\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m-> 1586\u001b[0m lines \u001b[38;5;241m=\u001b[39m \u001b[43mMPLPlot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1587\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_update_stacker(ax, stacking_id, y)\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/converter.py:95\u001b[0m, in \u001b[0;36mregister_pandas_matplotlib_converters..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pandas_converters():\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:981\u001b[0m, in \u001b[0;36mMPLPlot._plot\u001b[0;34m(cls, ax, x, y, style, is_errorbar, **kwds)\u001b[0m\n\u001b[1;32m 980\u001b[0m args \u001b[38;5;241m=\u001b[39m (x, y, style) \u001b[38;5;28;01mif\u001b[39;00m style \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (x, y)\n\u001b[0;32m--> 981\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1776\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLine2D\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/matplotlib/cbook.py:1790\u001b[0m, in \u001b[0;36mnormalize_kwargs\u001b[0;34m(kw, alias_mapping)\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m canonical \u001b[38;5;129;01min\u001b[39;00m canonical_to_seen:\n\u001b[0;32m-> 1790\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot both \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcanonical_to_seen[canonical]\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1791\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, which are aliases of one another\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1792\u001b[0m canonical_to_seen[canonical] \u001b[38;5;241m=\u001b[39m k\n", "\u001b[0;31mTypeError\u001b[0m: Got both 'c' and 'color', which are aliases of one another", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNotInitialized\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43mSEIRS2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnormed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSusceptible\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExposed\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInfectious\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRecovered\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/epidemiology101-UtPQcdCX-py3.13/lib/python3.13/site-packages/epidemik/EpiModel.py:343\u001b[0m, in \u001b[0;36mEpiModel.plot\u001b[0;34m(self, title, normed, show, ax, **kwargs)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28mprint\u001b[39m(e)\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotInitialized(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must call integrate() or simulate() first\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNotInitialized\u001b[0m: You must call integrate() or simulate() first" ] }, { "data": { "image/png": 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xjkIv2Hbaaaelqps0aVJYunRpQccCAFSuddddN5x11llJ1iftfX3UnsxQHQ5FRvEX7o477gj169ev9g6Gb7/9dnj88ceTYOX9998fxo4dW60g5DdiiDKmoeNW9ZUonqjGucmXuK1rXJHmvvvuS+Y+rmL0zjvvJO9JPtx2222hUtx6662p6uL/+RRK3AE1BpBziZ+RNH+MAQBq2m927NgxHH744QUbR9qeqhL6zV122aXa51D5tuGGG4Zf/OIXqWrjwilfffVVwccEAFQm/WZ2XXbZZcnO5msSFz4s5HejAABp6DdLv5L6z3/+85x1cbXzeP29qiqTt8YAAHWYfrP4vvzyy/DJJ5+kqt1zzz0LPp64KFynTp1y1q1evTpMnTq14OMBACB/ZIbyJ7Pf/MYdB//2t7+FRo0aFf3YPXr0SAJ+cYfJShWDi/FkKSvi+xF3psy62bNnh3HjxuWsi7ts7rHHHgUbR9yJNa5mlEsMtcbPCgBAofrN2JM0aNCgYOMYPHhwsuBJXek3y8GJJ56Y9Ju5xNUs13YzPADA2ug3s+ndd98N559//hr/fbyZ/aabbirJdQEAgG/Tb5bW3XffHd57772cdddee21o06ZNUcYEAJBP+s3iS3ujd5cuXUKHDh1CMey6666pQ7QAAGSDzFB+ZTYUGQ0bNixJnRYznLjffvuFl156qWgnNaUyYsSIkCVff/11uPfee0PWxdeQZvfMI488suBjiceoV69exX1WAIDykKaHiDc8x56/kGK/k6a3qpR+sxzEm5B23HHHVLVTpkwp+HgAgMqk38yeeJPX8ccfH5YtW7bGmpNOOil873vfK+q4AAD+F/1m6XcXz2WHHXYIQ4YMKcp4AADyTb9ZfIsWLUpVt/HGGxd8LNU9VtqxAwBQejJD+ZXpUOQ3X2TH3UOGDh1a0OM0b948XHXVVeGRRx5J/nOlJ4/feOONnHXxRuYrrrgiNGvWrKDj2XvvvVPVjRo1KmRd2tdw2GGHFXwsHTt2DP37989ZN3HixMyv8gQAlGe/OWDAgKJcUDj88MPrTL9ZTudxaUybNq3gYwEAKo9+M5viLj7jx49f47+P79VFF11U1DEBAPwv+s3Seuqpp8KkSZNy1p133nlFGQ8AQL7pN0sjBjvTKOZO5BtssEGqupUrVxZ8LAAA5IfMUH5lPhQZtW/fPtx///3hySefDNtss01en7tRo0bh2GOPDZMnTw6nnXZaqhRs1o0ePTpV3b777hvOOOOMZG7iHMW5yqdtt902uaAxcuTI0L1795z1zz77bPjqq69CVi1fvjw899xzOet69uwZOnfuXJQxDR48OK+fGQCA6vQO++yzTyiGbt261Yl+s5xsttlmqermz59f8LEAAJVHv5k9H374YfjVr3611pr/9//+X8UvWAgAZIN+s7RuvvnmnDU9evQIgwYNKsp4AADyTb9ZGq1atUpVt8466xR8LNU9VtqxAwBQWjJD+VcRochv7LHHHuGVV15JPiRHHHFEaNmyZY2fK35J/rvf/S7MmDEj3HLLLaFdu3ahrkj7Yf3mIkIMpcY5mj59evjtb38bNt988xofO56cDRs2LDz//PPh5ZdfDrvvvvt/HGttlixZstaVxMtdHPvSpUtz1hXz4k3aYz399NMFHwsAUHf7zWKoC/1mOWndunWqusWLFxd8LABA5dFvZs+JJ5641t5v6NCh4YADDijqmAAA1kS/WTqLFi0Kjz32WM664447rijjAQAoBP1maay//vqp6j799NNQLJ988kler78DAFBaMkP5V1GhyG8MGDAg3HnnncnJR1x95uKLLw5HHXVUsotkhw4dkrBkgwYNklVU4vbyXbt2Dbvttls45ZRTwo033hg++OCD8M4774TzzjsvbLTRRiV9LbfeemtYvXp1zp9Yly9pTkxbtGgR+vXr9x//W9u2bcP555+f7Bz5/vvvJ3P505/+NJnbLl26hDZt2iRzHuc+vgebbLJJ8p7E9ya+R/G9iidxd9xxR9hhhx3+47l32WWXvI29XKUde9q5yIf4/jRt2rSi5x0AKL6a9puFVBf6zXISzwnyWQcA8G36zWyJC+6t7QJKfK+uueaaoo4JAGBt9Jul88gjj6S6aeiggw4qyngAAApBv1ka8f7WJk2a5C2omA9pjxXvxQUAoPzJDOVfRd9hWr9+/bDjjjsmP6Qzf/78JBSaSzyhrlev3hr/fdyq9Yc//GFef9HSmDhxYsiqCRMmpKrbeuutQ7FUVVUl73WuLXpjCHbBggWhefPmRRsbAFC3+818qwv9Zjn54osvUtWlOdkGAPg2/Wa2zJkzJ/z85z9fa80ll1wS2rVrV7QxAQCsjX6ztB5++OGcNb169QrdunUryngAAPJNv1na+4233377MHbs2LXWxU1D4kId6667bsHH9Oqrr+asiffq+v4UACAbZIbyryJ3iqR2v2Rx58lcttpqq1BMcafJVq1aVfRJdZo/cK1btw6dOnUKxZTmD2r8zLz++utFGQ8AkG36TaIPP/wwVV3cjR4AoDr0m9nyk5/8JHz55Zdr/PcDBgwIJ554YlHHBACwNvrN0onz/swzz+Sss2g2AJBl+s3SStNLLlu2LPzjH/8o+Fji96YvvfRSzjr9LwBAdsgM5Z9QJP8h7Ye0T58+odjiio65xFWS4mpJWdwpZ8aMGTnrevfuXZTx1OSY5fgHDgAoP/pNqrPiUffu3Qs+FgCgsug3s+Pee+9d604/jRo1CjfddFNRV7wHAMhFv1k6kyZNCp9++mnOuv79+xdlPAAAhaDfLK2DDz449XebhfbAAw+Er7/+OmfdQQcdVPCxAABQezJDhSEUyX+YOnVqqrpu3bqFYkt7Q/S0adNC1lTCvKd9DQBA3VYJfU8W+81yEi/cjBkzJlXtFltsUfDxAACVRb+ZDZ999lk45ZRT1lpzzjnnhB49ehRtTAAAaeg3SyfNLpHV3TVp5syZYdSoUeHyyy8PZ599drKT+QknnBBOPfXU8Ktf/SoMHz48Wcjj7bffDqtWrarF6AEA0tFvllYMm+622245626//fYwZcqUgo0j7kZ5/vnn56zr3Llz2H///Qs2DgAA8qcSev2pZZgZalDqAVBe4ko95bpjS9pjxtdQnQsd5aBS5h0AoK70PVnrN8vt5qU0K7p36tQpbLrppkUZEwBQOfSb2XDaaaeFjz/+eI3/vmfPnslN6AAA5Ua/WTppViGvqqrKedPQq6++Gu66667w2GOPVesmnubNm4ftttsu7LfffmHIkCFh4403Tv1YAIC09Juld+aZZ+Zc5HflypXJQhojR45MetB8+8Mf/pAs4JHLGWecUZDjAwCQf5XS65cb3TDV/pA2atQobLTRRqHYOnTokKru/fffD1mT9o/DJptsEoqtbdu2oUGDBhU57wBA8ek3ufbaa1PV7bHHHgUfCwBQefSb5e/xxx8Pd9555xr/fb169cJNN92UvE8AAOVGv1k6b731Vqrr6euss84a+9ABAwaEbbfdNlx55ZXVXtV8wYIF4emnn05ufo/H2WeffcK4ceOq9RwAALnoN0tvzz33DEcccUTOuieffDJZ/C3fbr755nDhhRfmrIt9bdzpHACAbJAZKgyhSP7D9OnTc9ZsuOGGyY0ppfhFy9drKDdpx5x2DvIpriS0wQYbVOS8AwDFp9+s26ZMmRIeffTRVLXDhg0r+HgAgMqj3yxv8Ubyk046aa01P/rRj8IOO+xQtDEBAFSHfrM0Vq9eHd5+++2cdf9r98Z4o86+++6b/IwfPz5v44m7Ag0cODDsvvvuqcYGAJCGfrM8XHPNNaFdu3apFgSO32cuXbo0Lz3mpZdemvP706hx48bhb3/7W6hfv36tjwsAQHHIDBWGUCT/tnjx4lQnZ6X4JavOcT/55JOQNWnHXM5zn/bzAwDUXfpNfvGLX4RVq1blrOvWrVvYaaedijImAKBy6DfL35lnnhk++uijNf779u3bh4svvrioYwIASEu/WTozZ84MS5YsSRUQ+LaHH3449OvXL9klslBGjx4dvvOd74RLLrkkuZEdAKCm9JvlY/311w+jRo1K/pnLjTfeGLbZZpswZsyYGh/v9ddfTxbbOOuss8LKlSvXWht38BkxYkTo2bNnjY8HAEDxyQwVhlAk//bZZ5+lqmvTpk0ohTTJ4+q8jnJi7gGAukDPU7c9+eST4bHHHktVe/bZZ5dkdVMAINv0m+Vt3Lhx4aabbsq5AnuLFi2KNiYAgOrQb5bOrFmzqj0HMaQ4ZMiQZLfyQos3rsfvNPfbb7/kxiAAgJrQb5aXuPDFU089FVq2bJmzNu4cPmjQoNC3b9/kO85JkyblXCx46tSp4dZbbw277LJLspBHmlBl3MHn9ttvDwcddFC1XgsAAKWn3y+MBgV6XjLo888/T1XXrFmzgo+lNsdN+zrKSSXNfYcOHQo+HgAgmyqp56F65s+fH0444YRUtZ07dw5HH310wccEAFQe/Wb5irv6/PCHP1zrzjkHHnhgctM6AEC50m+Wzpw5c1LVrbfeesk/L7300iSkWGxxR8q4w8/IkSNT3TwPAPBt+s3ys/XWW4cJEyaEYcOGhRdffDFn/VtvvRVOPfXU5D83b948bLbZZqFVq1bJT/369cMXX3yR/Lz//vvV3lFz4403DnfccUcSogQAIHsqqd/vUEaZIaFIqv1LFk/WSqFp06bJSje5VtDJ4kl1mjHHk+ImTZqEUkj7nmdx7gGA4tFv1l0//elPw0cffZSq9sorrwwNGzYs+JgAgMqj3yxfv/71r8O0adPW+p5ce+21RR0TAEB16TfLPxTZqFGjMGLEiHDWWWeFUok3yw8ePDjZKb1x48YlGwcAkD36zfIUF/V97rnnkp3I4+IbcUHgNOKO5a+++mqtj1+vXr1w2GGHJTtQtm7dutbPBwBAacgMFUZVgZ6XjK7WnfbktlTSHDvt6ygnacZcynlPm/rO4twDAMWj36ybbrrppmTFyjT23nvvcMABBxR8TABAZdJvlqeXXnopDB8+fK01F110UbLSOQBAOdNvlk7aXXTee++9ZIfyNHbYYYdwwQUXhDFjxoTp06eHRYsWhZUrVyY3r0+dOjXZ7fGcc84JW2yxRY2Ckccee2y1HwcA1G36zfIVb06PveGHH34Yfve734UNNtig4MeMAdSDDz44TJo0Kdx1110CkQAAGSczVBhCkfzbihUrUtWVcteWNMf+6quvQiXOfbnPe1bnHgAoHv1m3TNhwoRwyimnpKpt2bJluPHGGws+JgCgcuk3y098Lccff/xaV4/v379/+PGPf1zUcQEA1IR+s3SWLl2aqu6hhx4KixcvXusuO0cddVR49913w/PPPx/OPffcMHDgwNCxY8ew3nrrJTe7x5t/unbtmizg9sc//jG88cYbyUIfe+21V7XGHHesvOWWW6r1GACgbtNvlr94Tfu8885LdjKPO4Mfd9xxSXgxn3bZZZdwww03JMe47777Qp8+ffL6/AAAlIbMUGEIRVLtD2cpf9EaNGiQty8Hsjb35T7vWZ17AKB49Jt1y9y5c8NBBx0Uli9fnqr+2muvDR06dCj4uACAyqXfLD9x551//etfa/z3jRo1SnYWjzenAwCUO/1m6SxbtqzWzxF3Jh87dmy47bbbwmabbVatx373u98No0aNSnboSbtienTGGWeEmTNn1mC0AEBdpN/Mhk8//TT85S9/CX/605+S/nBtC8LVxKuvvhqeeuqp8MQTT4TPP/88r88NAEDpyAwVhlAk1f5wpv2wF0KlrjSUZu7Lfd6zOvcAQPHoN+uOuHL7/vvvH2bMmJGq/sgjjwzDhg0r+LgAgMqm3ywvkyZNChdffPFaa84666zQq1evoo0JAKA29JvZDUXG3XVefvnlZNed2jj88MPD+PHjw0YbbZSqfsGCBeE3v/lNrY4JANQd+s3y9tFHH4XTTz89dOrUKZx00klh5MiReVm84/9atGhReOCBB8IPfvCDsMkmmyTHjMcGACDbZIYKQyiSf0u7Yk0pV+2uqsr9kc33yjvFkGbM5T7vWZ17AKB49Jt1Q5yfGHJ85ZVXUtX37ds33HDDDQUfFwBQ+fSb5ePrr78Oxx133Fov7Gy++ebh3HPPLeq4AABqQ79ZOrVZfTzetP7000+H9u3b52Us8fvM+HxNmzZNVX/77beHd955Jy/HBgAqm36zfHvRX/3qV6Fr165h+PDhYcmSJUU7djxWPGY8dgxHxsWJAQDIJpmhwhCKpNrJ3pUrV4ZyvthRyi1jayrNmMt93rM69wBA8eg364bTTjstPPjgg6lqmzdvnqxy2aRJk4KPCwCofPrN8nHZZZeF1157ba0Xc2688cbQuHHjoo4LAKA29JulU79+/Ro9Lvadt956a2jbtm1exxODkbHnTXuT0NVXX53X4wMAlUm/WX4mT54ctt9++3DxxReXdEeceOwYjtx6663DxIkTSzYOAABqTmaoMIQiydRJdZpjN2rUKGRNuf+BS3vsLM49AFA8+s3Kd/7554drr702VW2cxxie7N69e8HHBQDUDfrN8jBlypRw3nnnrbXmhBNOCDvttFPRxgQAkA/6zdKp6ZhPPfXUsMsuu4RC+NGPfhT22GOPVLV33HFHWLhwYUHGAQBUDv1meRk/fnzYdtttw4QJE1I/pkGDBmG77bYLv/jFL8IjjzySBBinT58eFi1aFJYtWxZmzZoV3nzzzWTn8d///vdh9913D+utt17q5487kMeQ5kMPPVTDVwUAQKnIDBVGgwI9LxmU9sNZ7r9o5fZLlkYc8+LFizM971mdewCgePSblS2udp7r5vdvr+x+5513ht12263g4wIA6g79ZumtXr06HH/88ckNPmsSd+m59NJLizouAIB80G+WTk3G3Lx583DBBReEQoq79fTs2TNnXbwJfuTIkeH73/9+QccDAGSbfrN8vPjii2HvvfdO+rg0mjVrFk488cRw+umnhw4dOqyxrn379slPnz59wqBBg5L/LR7jL3/5S7jyyivDjBkzUu0aGfvK+++/P+y///7VeFUAAJSSzFBh2CmSf2vcuHGquiVLloRSyfVHoBx/yfI19+U+71mdewCgePSblevmm29OLvCkUa9evXDjjTeGgw8+uODjAgDqFv1m6V133XXh+eefX2vNNddcE1q2bFm0MQEA5It+s/zn/tuOPvro0LRp01BIPXr0CAMHDkxV+/jjjxd0LABA9uk3y8MHH3yQBCLT7vQ9ZMiQZDfIyy67bK2ByDWJPWu81j5t2rRw/vnnh6qq3Ld1r1ixIhxyyCHhueeeq/bxAAAoDZmhwhCK5N9atGiRqi7tyV6+xRVu4k8uWbyhJs3cx9XVS5X8TvueZ3HuAYDi0W9WphEjRiSrXsZdgdKIK1wed9xxBR8XAFD36DdLK974c/bZZ6+1Zr/99rM4BgCQWfrN0qlJuPEnP/lJKIaTTz45Vd3o0aMLPhYAINv0m6UXr3kfe+yxYf78+Tlr69evn+wc/sADD4RWrVrV+tgNGjQIv/3tb5O+cYMNNshZH9+LH/zgB6l3swQAoLRkhgpDKJJ/a926daq6BQsWFHwstTlu2tdRTsw9AFAX6Hkqz0MPPZSsuL5q1apU9X/84x/DaaedVvBxAQB1k36ztH70ox+t9QacZs2aJTtJAgBklX6zdNq0aVPtHRx79uwZimHfffdNtcr7nDlzwuzZs4syJgAgm/SbpXf11VeHf/zjH6lrTz311LyPYdddd012GV933XVT7Wr5s5/9LO9jAAAg//T7hSEUyb+tv/76qeq+/PLLgo+lNsdN+zrKibkHAOoCPU9liRdiDjvssNQrE51zzjnJDwBAoeg3S+fWW28NTz755FprLrzwwtChQ4eijQkAIN/0m6VT3Rtttt9++1AsjRo1CltuuWWq2gkTJhR8PABAduk3SyvuynPBBRekqo1hyELuTL7tttuG22+/PVXtX/7ylzBlypSCjQUAgPzQ7xeGUCT/1qpVq1BVlfsjMXfu3KKMp6bHLbfkcT7HXM5zX79+/bLbChcAKC/6zcrx1FNPhaFDh4avvvoqVX3cHTLuEgkAUEj6zVCy15VrNfJ4U3ohbxICACgG/WZ2doosZigy2m677VLVTZs2reBjAQCyS79ZWnfccUf49NNPc9ZtvPHG4ZJLLin4eOL1+AMPPDBn3erVq8OVV15Z8PEAAFA7MkOFIRTJv8UT6nbt2uWsmzdvXiiFtL/cWVxtPO2Yy3nu27dvH+rVq1eU8QAA2aTfrAzjxo1LLr4sX748Vf2JJ54YrrrqqoKPCwBAv1kaMez4xRdfrPHfN2zYMNx4442pbugCAChn+s3SideiqyPtzo350q9fv1R1M2fOLPhYAIDs0m+W1jXXXJOq7te//nVYZ511QjHEnSvTfK962223rfU7WgAASk9mqDDchcB/6Ny5c86aBQsWhMWLF4dimzNnTt5eQ7lJO+bZs2eHYlu4cGGq9zuL8w4AFJ9+M9uee+65sN9++4WlS5emqj/66KPD9ddfX/BxAQB8Q79ZXPfdd1946KGH1lrzy1/+MvTt27doYwIAKCT9Zmlsuumm1brZpro7SxZrlfe07xEAUHfpN0sjvrZJkyal2s3z+OOPD8XSu3fvsPfee+esW7JkSbK4MQAA5UtmqDCEIvmviwlpTJs2LRTb1KlTU9WV4y9aLuYdAKgr9D3Z9eKLL4Z99tkn9QW273//++GWW26xIxAAUFT6zeK666671vrvu3fvnqycDgBQKfSbpdG4ceNq7RbZsmXLUEzx5vg0ShFeAACyRb9ZusWB0xg4cGBo2LBhKKY999wzVd2zzz5b8LEAAFBzev3CaFCg5yWjunTpkqruvffeC1tssUUopiz/ouVz3outkucdACg+/WY2vfzyy2GvvfZKVgRK48ADDwx33HFHqF+/fsHHBgDwbfrN4lq9enXOm9FPOumkgo5h8uTJqep+8YtfhKZNm+asO+KII8Iee+yRh5EBAJVIv1k6Xbt2DbNmzcpZF3eULNdQ5NKlSws+FgAg2/SbpfH888+nqivF94a77757XoOdAACUhsxQYQhF8h/69OmTqu7dd98t+FhqcszmzZuHjh07hiymvuMNOYsWLcrkvEd9+/Yt+FgAgOzTb2bPa6+9lqw+uWDBglT1e++9d7jnnntCgwZONwGA4tNvlpdXXnkl+SkHDzzwQKq6LbfcUigSAFgj/Wbp9O7dO9XuN1VVVUVfrC3tbkFff/11wccCAGSbfrM0ZsyYkfq7w2Lr0aNHWGeddcKyZcvWWvfhhx8WbUwAAFSfzFBhVBXoecmorbbaKlXdhAkTQjHNnz8/vP/++6lOOuPKj1kTx5zmhDkmsNPuzpMvad/rfv36FXwsAED26Tez5fXXX09Wnvzyyy9T1Q8aNCg8+OCDoVGjRgUfGwDA/6LfBACgkPSbpbP11lunDh4W+5p62u9PmzRpUvCxAADZpt8sjc8++yxVXZs2bQo+lpoeN/akq1atKsp4AACoPpmhwhCK5L+2ZG3RokXZnVTH3WlWr16dty8FylGascc5mDhxYiimV199NWdNq1atynIrXACg/Og3s+PNN99MApFffPFFqvqdd945PPLII8kqlQAApaLfBACgkPSbpbPNNtukrk37nWa+pD3euuuuW/CxAADZpt8s71DkBhtsUPCx1PS4MRBZ7D4YAIDqkRnKP6FI/it9nOZiwgcffBDmzp0biuXFF1/M+4WQcrPtttumqnvhhRdCscyaNSvMnDkzb6tyAgDoN7Ph7bffTnZ9/PTTT1PV9+/fP/z973+30jkAUHL6TQAACkm/WTq9e/cO6623Xl53bsyXtDefb7TRRgUfCwCQbfrN0lixYkWqusaNG4dSSHsdftmyZQUfCwAANSczlH9CkfyX3XbbLVXd6NGjQ7E8/fTTeR17ORo4cGCqOvMOAGSdfrO8vfvuu8nr/Pjjj1OfqI8aNSo0bdq04GMDAEhDvwkAQCHpN0ujQYMGYeedd079HWcxTZkyJVVdp06dCj4WACD79JvFl3ZH77SLCufbkiVLUtXZmRwAoLzJDOWfUCT/Je4Ik8ZTTz0VimHRokWpVhrq27dvaNu2bciq9u3bh169euWsGz9+fOqT3GL9gdtjjz0KPhYAoHLoN8vX1KlTkxPvtKuK9uvXLzz55JOhefPmBR8bAEBa+k0AAApJv1k6e+21V6q6l156KRTTP//5z1R1nTt3LvhYAIDs028WX9odyUsVipw9e3ZeXwcAAKUhM5R/QpH8z21N119//Zx1jz76aFi+fHnBx/Pwww+Hr776Kmfd7rvvHrIuzR+KZcuWhccee6zgY1m6dGmq47Rp0ya5GR4AIC39Znn64IMPwq677pr6gkq8qBVPiFu1alXwsQEAVId+EwCAQtJvln8oMm1IMV9efvnlVHVbbbVVwccCAGSffrP4Nt5441R1H374YSi2BQsWhI8//jjVfZyNGzcuypgAAKg5maH8Eorkv1RVVYWDDjooZ938+fPDqFGjCj6eESNGpKo7+OCDQ9YNHTo0r3NSG3//+9/DwoULc9YNGTIk1KtXr+DjAQAqh36z/MyYMSMJRH700Uep6nv27BlGjx4dWrduXfCxAQBUl36zeOINUatXry7pz84775x6EZA0z3f66acXfN4AgGzTb5ZO9+7dk8Xacnn11VfDl19+WZQxTZw4MXz22Wc56zbccMOw6aabFmVMAEC26TeLL+2O3vEaebHFBT/i95a56DUBALJBZii/hCL5n4488shUddddd11Bx/H++++HJ554Imdd165dw/e+972QdTvssEOqE+zHH388uXG9kNK+t2k/KwAA36bfLB+zZs0KAwcODNOnT09Vv9lmm4UxY8YkN/EAAJQr/SYAAIWk3yydY445JmdN3DHp1ltvLcp4rr/++lR1u+22W8HHAgBUDv1mcfXu3TtV3dNPPx2KLe4ImkavXr0KPhYAAGpPZii/hCL5n+Lq2h07dkx1kjdp0qSCjeOqq64Kq1atyll3xBFHhEoQ09PDhg3LWbdy5cpkbgrltddeC88880zOuk6dOoUBAwYUbBwAQOXSb5aHuXPnJoHIadOmparv0qVLEohs165dwccGAFAb+k0AAApJv1k68QacBg0a5Kz785//nGpHndqIuzPdddddqWoPPPDAgo4FAKgs+s3iShvonDx5cnjppZdCsSxZsiTcc889qWr79+9f8PEAAFB7MkP5JRTJGn/RfvzjH6eqPfvsswu2ytCNN96Ys65hw4bhxBNPDJUivpb4mtKsOJl2N5/qSvue/uQnPynbbXABgPKm3yy9Tz75JAlETpkyJVV9PLkdO3Zs6NChQ8HHBgBQW/pNAAAKSb9ZOhtuuGE49NBDc9a9++67qW8gr6krrrgiLF68OGdds2bNwuDBgws6FgCgsug3i2uzzTZLvTDwr3/961AscaGPTz/9NHWQFgCAbJAZyh+hSNYonlTHL+dzGTVqVHjsscfyfvwzzjgjLF++PNUqQ5V0Y/Ymm2wSDjvssJx1y5YtCz/72c/yfvyHHnoojB49Omdd8+bNw0knnZT34wMAdYd+s3Q+++yzMGjQoPDOO++kqo+vPwYiYzASACAr9JsAABSSfrN0zjnnnFQ34vz0pz8N8+bNK8gYJk6cGC666KJUtT/4wQ9C06ZNCzIOAKBy6TeLJ/aWQ4cOTVUb762Mc15oM2fODOeff36q2l69eoUePXoUfEwAAOSHzFD+CEWyRi1atEj9AT7hhBPCnDlz8rrCzaOPPpqzrqqqKpx55pkh34455pjkRDfXT6wrhF/+8pepLuI8+OCD4eabb87bcWfNmpV61ab4pUv8IwcAUFP6zdL0m19++WXYY489wqRJk1LVxxUxYyCyS5cueR8LAEAh6TdL9/0mAEBdoN8sXb/Zu3fvcNBBB6VaHC5e/169enVejx9vRopBxxUrVuSsbdCgQTj11FPzenwAoG7Qbxa33zzyyCNT18Yg6OTJk0OhLF26NNkdfcGCBanqhw0bVrCxAABUqlJ/xykzlB9CkazVr371q9CmTZucdR9//HE48MADw8KFC2t9zDFjxoTTTz89Ve3xxx+fXPCoNH369AnHHXdcqtpTTjkl/OMf/6j1MefPnx8OOOCA8Omnn+as3WCDDVJvlwsAsDb6zeKK87fXXnuFCRMmpKrfaKONkkBk9+7dCz42AIBC0G8CAFBI+s3SueSSS8I666yTsy7ezB93OcqXGIQ85JBDwptvvpmqPt5g5PtVAKCm9JvFs91224X+/funXoh4n332CdOnT8/7OOJ7GBcA+ec//5mqfr311iv73XsAAPhvMkP5IRTJWrVq1SpcdNFFqWpffvnl5AbreIJdU48//njYf//9w/Lly1ON7cILLwyVKs57fI1pVgXad999wxNPPFHjY82dOzfsueee4bXXXks9tpYtW9b4eAAA39BvFs+SJUuSCzMvvfRSqvp4cS1e8OrRo0fBxwYAUCj6TQAACkm/WTrdunUL55xzTqra4cOHh6OPPjrZ4bE24s6T8br63//+91T166+/fjjvvPNqdUwAoG7TbxbXb3/729S177//fujXr1947LHH8nb8N954I2y99dbhySefTP2Yk08+Oek7AQDIHpmh2hOKJNVqPjvvvHOq2hdeeCE50Ut7EeDbq9vE7V/322+/5GbtNC6//PJUqyBlVUxWX3bZZalqFy1alNzgHpPYixcvrtZxHnnkkeQ9S3tz/C677JI6kQ4AkIZ+s/DizT7x4tVzzz2Xqj5eNBk9enTFrOoJANRt+k0AAApJv1k6Z511VvjOd76Tqvb2228PW265ZfK9Z3WtXr06jBgxIvm+dNy4cakfd+ONNybX/QEAakO/WTzxJvGhQ4emrv/iiy+SnXbiAhxvvfVWjY87e/bspLfdfvvtw3vvvZf6cZtsskm1gpwAAJQXmaHaE4okp3r16oW77ror9Zf18QQtnhzHE/E777wz+eVbk3gCF1dGjKs4/ulPf0ouJqRx1FFHhWOPPTZUuviHZNiwYalqV61aFS655JJkLn//+9+HadOmrbF2wYIFyUWfAQMGhAMPPDBJfaex4YYbJp+F+JkAAMgX/Wbh/fOf/0x2fUzr888/T24Qiu9NqX8AAGpLvwkAQCHpN0unUaNG4YEHHki9Yvm7774bdt9999C/f//wl7/8JXkv1mbq1KnhiiuuSMKQRxxxRJg3b17qsZ100knVuqEeAGBN9JvFde2114Z27dqlro9zFu/F3GKLLcLee+8dbrjhhvDOO+/kfNyMGTOS9yfOZefOncOll15arZ3Nq6qqws033xzWW2+91I8BAFiTY445plb3+J1//vmpjrPrrrvW6ji33nprqDQyQ7XToJaPp45o37598sEePHhwWLFiRarHPPvss8lPPPnq0aNHcuLWokWL8NVXXyU3WceVcT7++ONqj6Vv377h+uuvD3XFn//85/DGG2+kXkko/rH63e9+l/xstNFGoU+fPslOPw0bNgxffvll+OCDD5KLPfEPYnXEx8fPQHVO+AEA0tJvAgBQSPpNAAAKSb9ZOl27dk1uJo879KxcuTLVY1588cXk55vddbp3757sctS4ceOwdOnSJPwYr6nXZP6jeDP8NddcU6PHAgD8L/rN4mnbtm247777wsCBA5O5qk448oknnkh+otatW4eOHTsm927Gn/r16yfzHn/mzJkTZs2aVatxXnTRRcmCHwAAZJ/MUM0JRZLaoEGDwh133JGsgPj111+nflz8RXr77beTn9rabLPNwtNPP12nVrdp2rRp8pp32mmnZGWm6ogXa6qzWuWaxBPy+Mdtt912q/VzAQCsiX4TAIBC0m8CAFBI+s3SieGAeD07zn3aYOQ3Zs6cmfzkS7ymHm+ib9DA7TgAQH7pN4tnhx12SHYkP/jgg8Py5ctr9ByfffZZ8lMIZ599dvjlL39ZkOcGAKD4ZIZqrqoWj6UOOvTQQ8Pf/va30KhRo6IfO65WNGbMmCTJXNfE1Yfia998882Lfuz4Xt92223JCT4AQKHpNwEAKCT9JgAAhaTfLJ1DDjkk3H333WHdddct6Rgef/zxig8JAAClo98snn333Tfp7eKOO+Ui7vp58cUXJ7tEAgBQWWSGakYokmobNmxYGDt2bFFPbvfbb7/w0ksvhQ4dOoS6apNNNknmYJ999inqH9Zx48Ylq0sBABSLfhMAgELSbwIAUEj6zdIZOnRoePHFF0PXrl2LetyGDRuGyy+/PNxzzz2hcePGRT02AFD36DeLJ+6S89prr4Xtt9++1EMJ7dq1C08++WQ466yzSj0UAAAKRGao+oQiqZEddtghTJgwIbmoUEjNmzcPV111VXjkkUeS/1zXtWjRIjz66KPhiiuuCM2aNSv4KpbxPe7fv39BjwMA8L/oNwEAKCT9JgAAhaTfLJ3vfOc7yY3rP/7xj0P9+vULfrwBAwaEl19+OfzsZz8L9erVK/jxAAAi/WbxbLrppmH8+PHhuuuuC61bty768eMCHKeeemqYPHlyGDRoUNGPDwBAcckMVY9QJDXWvn37cP/99yerz2yzzTZ533712GOPTU7kTjvtNBcPvqWqqiqcccYZydzEOYpzlU/bbrtteOqpp8K9996brC4EAFAq+k0AAApJvwkAQCHpN0t741C8aX3ixInJrkbxGnu+xfc0XlN/7rnnwpZbbpn35wcAyEW/WTyxn4yLbnz44YfhsssuCx07diz4MeMN8KecckqYOnVqGD58eJ0NpQIA1EUyQ+kJRVJre+yxR3jllVeSL/vjlqktW7as8XP16NEj/O53vwszZswIt9xyS+Z/wQr9pUaco+nTp4ff/va3YfPNN6/xc7Vq1SoMGzYsPP/888kqlrvvvntexwoAUBv6TQAACkm/CQBAIek3S6dv377JqurvvfdeOPPMM0PXrl1r9Xxt27ZNboaP19XjexpXUgcAKDX9ZvE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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = SEIRS2.plot(normed=True, color=[colors[0], colors[4], colors[1], colors[2]])\n", "ax.legend(['Susceptible', 'Exposed', 'Infectious', 'Recovered'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Population')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = (SEIRS2.E/N).plot(color=colors[4])\n", "(SEIRS2.I/N).plot(color=colors[1], ax=ax)\n", "ax.legend(['Exposed', 'Infectious'])\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Population')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Asymptomatic cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "rbeta = 0.75\n", "pa = 0.4\n", "R0 = 2.0\n", "\n", "beta = R0*mu/(pa*rbeta+(1-pa))\n", "\n", "SEIIR = EpiModel()\n", "SEIIR.add_interaction('S', 'E', 'Ia', rate=rbeta*beta)\n", "SEIIR.add_interaction('S', 'E', 'Is', beta=beta)\n", "SEIIR.add_spontaneous('E', 'Ia', a_epsilon=epsilon*pa)\n", "SEIIR.add_spontaneous('E', 'Is', na_epsilon=epsilon*(1-pa))\n", "SEIIR.add_spontaneous('Ia', 'R', mu=mu)\n", "SEIIR.add_spontaneous('Is', 'R', mu=mu)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Epidemic Model with 5 compartments and 6 transitions:\n", "\n", "Parameters:\n", " rate : 0.166667\n", " beta : 0.222222\n", " a_epsilon : 0.160000\n", " na_epsilon : 0.240000\n", " mu : 0.100000\n", "\n", "\n", "Transitions:\n", " - S + Ia = E rate\n", " - S + Is = E beta\n", " - E -> Ia mu\n", " - E -> Is mu\n", " - Ia -> R mu\n", " - Is -> R mu\n", "\n", "# R0=2.00\n" ] } ], "source": [ "print(SEIIR)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "SEIIR.integrate(365, S=N-I0, Ia=0, Is=I0, E=0, R=0)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Population')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "(SEIIR.S/N).plot(color=colors[0], linestyle='-', label='Susceptible', ax=ax)\n", "(SEIIR.E/N).plot(color=colors[4], linestyle='-', label='Exposed', ax=ax)\n", "(SEIIR.Ia/N).plot(color=colors[1], linestyle=':', label='Asymptomatic', ax=ax)\n", "(SEIIR.Is/N).plot(color=colors[1], linestyle='--', label='Symptomatic', ax=ax)\n", "(SEIIR.R/N).plot(color=colors[2], linestyle='-', label='Recovered', ax=ax)\n", "ax.legend()\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Population')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Population')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "(SEIIR.E/N).plot(color=colors[4], linestyle='-', label='Exposed', ax=ax)\n", "(SEIIR.Ia/N).plot(color=colors[1], linestyle=':', label='Asymptomatic', ax=ax)\n", "(SEIIR.Is/N).plot(color=colors[1], linestyle='--', label='Symptomatic', ax=ax)\n", "\n", "Emax = np.argmax(SEIIR.E)\n", "Imax = np.argmax(SEIIR.Ia)\n", "\n", "ax.axvspan(xmax=Imax, xmin=Emax, alpha=0.3, color=colors[3], zorder=-1)\n", "\n", "ax.legend()\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Population')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mortality" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "pd = 0.10\n", "\n", "SEIIRD = EpiModel()\n", "SEIIRD.add_interaction('S', 'E', 'Ia', rbeta=rbeta*beta)\n", "SEIIRD.add_interaction('S', 'E', 'Is', beta=beta)\n", "SEIIRD.add_spontaneous('E', 'Ia', epsilon_a=epsilon*pa)\n", "SEIIRD.add_spontaneous('E', 'Is', epsilon_s=epsilon*(1-pa))\n", "SEIIRD.add_spontaneous('Ia', 'R', mu=mu)\n", "SEIIRD.add_spontaneous('Is', 'R', mu_nd=(1-pd)*mu)\n", "SEIIRD.add_spontaneous('Is', 'D', mu_d=pd*mu)\n", "SEIIRD.name='SEIIRD'" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Epidemic Model with 6 compartments and 7 transitions:\n", "Name: SEIIRD\n", "\n", "Parameters:\n", " rbeta : 0.166667\n", " beta : 0.222222\n", " epsilon_a : 0.160000\n", " epsilon_s : 0.240000\n", " mu : 0.100000\n", " mu_nd : 0.090000\n", " mu_d : 0.010000\n", "\n", "\n", "Transitions:\n", " - S + Ia = E rbeta\n", " - S + Is = E beta\n", " - E -> Ia mu_d\n", " - E -> Is mu_d\n", " - Ia -> R mu_d\n", " - Is -> R mu_d\n", " - Is -> D mu_d\n", "\n", "# R0=2.00\n" ] } ], "source": [ "print(SEIIRD)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "SEIIRD.save_model('SEIIRD.yaml')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "SEIIRD.integrate(365, S=N-I0, Ia=0, Is=I0, E=0, R=0, D=0)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Population')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qUuo4VM3EIYCuzjrrrHj66af7LHfkkUfGhAkT8q5PX6hvYhBQLUodg6qZGAT0R9+figGgCiUzYxbyA24hZa0z2Yett9666O0BoM306dPjzjvvjPvuuy8dieuFF16I119/PR0JurGxMUaMGBEjR46MsWPHxpprrhlrrbVW+uVMcq5OluTCRTm9+eabaVsrMfYlF3TWXXfdPr9czBq/AVhetceBodIHBKgmuVwuHn744bjnnnvigQceSOPEiy++GLNmzVoa05I+WLIks+4kfbBkSUYn7uiHjRs3rty7IQYBdHL55ZdnKrfLLrvkXZc+kPgDUM44NFD6QcUnDgGl9sgjj8Spp57aZ7kk8fL444/Puz59ITEIoFwxaKD0g4pPDILKI9kTAAb4gXTYsGExefLkKLWkE5LFc889V/S2ABDx5JNPpl8KPfTQQ+mXS71ZuHBhusycOTNNBO0uuWixzz77xAEHHBB77LFH1NTURCll/TJmypQpUQ5J/Ovros78+fPjrbfeilVWWaVk7QIYKqo9DlTy/usDAizvZz/7WZx00knxxhtvrLBcU1NTzJ07Ny33xBNPLHfz2Hve85744Ac/GIccckjaJysHMQhg2Q3Hf/rTnzKV/cAHPjCkz78JfaC+iT/AYI5DA6EfVBriEFBKyUDSSVzJknz53//93wWZ6auSz8EJfaG+iUHAYI1BA6EfVBpiEFSe2nI3AAAqUTLqS19WXXXVkifiJFZbbbWC7QMA+Uu+JHrwwQdXmOiZ9QudX/ziF7HXXnvF+uuvH6effnrMmzcvSiVr3MgahwpN/AMormqPA5W8/2IgwPKSkZv7urDfl+bm5vj73/8e3/rWt9I+2Pvf//64/vrro9TEIIA2P/3pT6OxsbHPcsnNWO9973uH9Pm3P/XqAwEMzjg0EPpBpSEOAaWQJKT87//+b+y6667prGR9SRJUTj755CF/Du5PvfpCAIMvBg2EflBpiEFQeSR7AkA3yUg1ixYtGvRfaiUjmAEwOCWJn8kXZclNA2eeeWb6RVuxZY0b4h/A0FTtcaCS93/YsGExceLEPsuJgQADlwzec9NNN8WHPvSh2GGHHeL2228vWd1iEEDEK6+8Ej/5yU8ylT3qqKOG/Pm3P/XqAwEMzjhUCfSDeiYOAcWUnDuSAaCnTp0a3/zmNzPNppaUveaaa6Kurq5gbchCX0gMAoaWSohBlUA/qGdiEFSe+nI3AAAqzcyZMzOVW3nllaMcVllllYLuBwCVKzmXf/3rX4+LL744zj///Nh6662LWlcW4h/A0FTtcSDL+0aMGBGjR4+Ocu3/22+/vcIyWUZeBaBv99xzT+y2225x7LHHxg9/+MMYO3ZsUesTgwAi/f5r4cKFfZZLzsnHHXdcQerUBxJ/AMoZhyqNflBX4hCQ7+xhc+fOjXnz5qXnkieeeCIefvjhuP/++9OEkuTnWW255ZZpQkoh+yX6QmIQMHRVegyqNPpBXYlBUFkkewLAAD+MFvuDfb71+lANMHQ88sgjsf3228dPf/rT+PznP1+UOsQ/gOpW7XEgy/vKte9Z6+64eDdu3LiStAlgKEtGdv71r38dN998c1x77bWxySabFK0uMQiodn/+85/jkksuyVT2y1/+cqYR5rPQBxJ/AMoZhyqRflD/6haHoDrttNNOceeddxa9npqamvjSl74UZ5xxRpr0UUj6QmIQMDgNhRhUifSD+le3GASlU1vCugBgUMj6ZVC5PqyOGTMmamv7DuGSXQCGluTLki984QvpF2rJF03VFv+y1iv+AQxMNceBlpaW9IJEodpQDOIgQHk888wzscMOO8S0adOKsn0xCKh206dPj8997nOZyq611lpx8sknF6xufSDxB6CccaiS6QeJQ0B5JefgJOHk//7v/4qSZKMvJAYBlCsGVTL9IDEIKo1kTwDoZuHChZmTLsslS91Z9wOAweXnP/95mvRZbfEv68hl4h/AwFRzHFi0aFGmgRTK2QcUBwHKZ86cOfHhD384br311oJvWwwCqllyg9MhhxwSM2bMyFT+Rz/6UYwePbpg9esDiT9AdSt3HKp0+kHiEFBaw4YNi3333TdNMLn77rtjt912K1pd+kJiEEC5YlCl0w8Sg6CS1Je7AQBQaZqamjKVa2hoKHpb8ql7yZIlJWkLQDUbP358bLHFFrHZZpuly6abbhqrrLJKOtJVx5LMyJl8YZOMEP3KK6/EY489Fvfff386Etrrr78+oHrPPvvsmDhxYpx22mlVE/+y1iv+AQxMNceBSt/3/tQtDgLVYN11143NN998aT9sww03TPtmHX2wkSNHxuLFi2P+/Pnx6quvxgsvvBAPPfRQepPC3//+92hsbOx3nUmfbr/99kvfv9VWWxVsX8QgoJqddNJJcfvtt2cqe/DBB8fHPvaxgtZf6edgfSDxBxjacai/9INKSxwCSiWZNe2b3/xmfPWrXy3ZTF6Vfh7WFxKDgKEbg/pLP6i0xCCoLJI9AWCAH0TL+aG6vr6+YJ0DALKrqamJLbfcMj74wQ/G3nvvHe95z3v6PCfX1dXF8OHDY8KECbHJJpvEnnvuufRn99xzT5x77rlx8cUXp18+9cfpp58e22+/fey///5RDfEvS+xLiH8AA1PNcaDS9z0hDgLVLJk9Z/fdd0/7YElfLLm4n+U9yTJ58uTYeuut46CDDlo62vAVV1wRv/71r+Mf//hHv9qxYMGC9CbvBx54IO3fFYIYBFSr3//+93HmmWdmKrvaaqulA58VWqWfg/WBxB9gaMehvugHiUNAdUjuEfif//mf9H6B5Hz/6U9/Orbbbrui1lnp52F9ITEIGLoxqC/6QWIQsExtp+cAQD8+iGb9YFsMZvYEKK011lgj/t//+3/x3HPPpV/kfP/734+dd94571iQJGuec8456XaPOeaYNJk0q1wuF0cccUQ6Y2g1xD+jhwEUVzXHgUrf94Q4CFSjHXfcMS688MJ466234pprrokvfOELmS7sr8ioUaPisMMOS0dkvvnmm+Nd73pXv97//PPPx+c+97koFDEIqEbJzVVHH310prK1tbVpLFh55ZUL3o5KPwfrA4k/wNCOQ73RD1pGHAKqSXK/wC9+8Yv0/oFdd901/vKXvxStrko/D+sLiUHA0I1BvdEPWkYMAjpI9gSAblpbWzOV609CTqElF5YKtR8A9G7KlClx5ZVXxosvvhjf+973Yp111ilKPauvvnqa9Hnrrbemz7OaPXt2fO1rX6uK+Jcl9iXEP4CBqeY4UOn7nhAHgWqSjJb8+OOPpzdhJyNJjxw5sij1JKND33vvvfFf//Vf/TrHX3rppTFt2rSCtEEMAqrNM888EwceeGA0NjZmKn/qqafG+9///qK0pdLPwfpA4g8wtONQd/pByxOHgGp1++23x7777hsf+tCHCjbw82A6D+sLiUHA0I1B3ekHLU8MAjqUL/UbACpU1tFJmpubo1yyjPKSdT8A6N3666+fLqWyyy67xH333Rf77LNPPPzww5ne8/vf/z6+/OUvx3bbbTek41/WEc7EP4CBqeY4UOn7nhAHgWqy3377layu5Lz53e9+N7beeus45JBDMo9GfPzxx8ejjz6a90V3MQioJm+88UbsvffeMWPGjEzlk+/HkhuwiqXSz8H6QOIPMLTjUHf6QcsTh4BKlMSHDTbYYIVl5s2bF3PmzEkHbk7iTjKw9EBcf/31MXXq1DjvvPMKGicq/TysLyQGAUM3BnWnH7Q8MQjoINkTAAbhh+osdQ8bNqwkbQGgsNZYY424+eab08TPZPSyLM4444z485//PKTjX9Z6xT+AganmOFDp+96fusVBgIFJZvdJ+lQHHHBAtLS09Fk+6atdd911ed+IIAYB1SK5ySxJsHn22Wczld9yyy3jj3/8Y1FHsq/0c7A+kPgDDO04VAn0g/KvWxyC6vOtb32r3+95++230wGfkxnFLr/88njwwQczvzdJ1EnO18kA0B//+MejECr9PKwvJAYBQzcGVQL9oPzrFoOgNLLNtQsAVSTrB9FK/1DtAzXA4LXSSivF1VdfHRMmTMhU/qqrrooXXnhhSMc/XygBFFc1x4FK3/f+1C0OAgzchz70oXQgnax+8pOf5F2nGARUg4ULF8a+++4bDz30UKbyG264Yfz1r3+NcePGFbVdlX4O1gcSf4ChHYcqhX5QfnWLQ0AWEydOjL322itN0nnggQfSpJtjjjkm6uuzzdWTJKIceuihcdlllxWkPZV+HtYXEoOAoRuDKoV+UH51i0FQGpI9AaCb4cOHZ74wVC4LFizos4wP1ACD2/rrrx9nnnlmprKtra1x6aWXDun4lyX2JcQ/gIGp5jhQ6fueEAcBSuOrX/1q7LzzzpnK3n777fH666/nVZ8YBAx1jY2N6Sj5d955Z6byU6ZMiWnTpsWqq65a9LZV+jlYH0j8AYZ2HKok+kHLE4eAYtpmm23inHPOiXvuuSfe9a53ZU62Ofzww+PJJ5/Mu/5KPw/rC4lBwNCNQZVEP2h5YhBUFsmeANDN+PHjM5WbN29elMOSJUvSpS9ZZ4MDoHIdddRR6RdtWVx++eVDOv5lrVf8AxiYao4DyYWVLBdXyrXv/albHATIT01NTfzsZz9LH7MMunPFFVfkVZ8YBAxlTU1N8bGPfSxuuummTOUnT56cJtisvfbaUQr6QOIPMLRVehyqJPpBA69bHALysfXWW8e9994bX/nKVzKVX7x4cXz605/Oe8YvfSExCKBcMaiS6AcNvG4xCEpDsicAdLPSSitlKjd37tyityWferPuBwCVK/lC6aSTTspU9oEHHsgrNol/ANWt2uNAlveVa9/7U/ekSZOK3haAoS4ZzXqfffbJVPbWW2/Nuz4xCBiKkhH/DzvssLjmmmsyn0OSZJyNNtooSkUfSPwBhq7BEIcqjX7QwOoWh4B8NTQ0xFlnnRUnn3xypvJJYs5Pf/rTvOrUFxKDAMoVgyqNftDA6haDoDQkewLAAD+Izp49u+htyadeH6gBhob9998/Jk6cmGkUsbvvvnvA9Yh/ANWt2uNAlvfNnz+/bKOVZtn/cePGRX19fUnaAzDUHXHEEZnK3XXXXXnXJQYBQ03yHdWRRx4Zl112WabyyTnkr3/9a0ydOjVKSR9I/AGGpsEShyqRftAy4hBQaqeddloccMABmcomiTn5nB/1hcQggHLFoEqkH7SMGASVRbInAHSTJNTU1vYdIqdPn16S9gy0XjObAQydkdT23XffTGUffPDBAdeTNW6IfwBDU7XHgSzvy+Vy8eabb0apZa1XDAQonKQPNmzYsD7Lvf7663nHRjEIGEqS88axxx4bF198cabyo0ePjr/85S+x7bbbRqnpA4k/wNAzmOJQJdIP6l+94hBQaOecc06MHz++z3Ivv/xyXH755QOuR19IDAIoVwyqRPpB/atXDILSkewJAN0kiZ6rr756n+XeeOONKIesHYa11lqr6G0BoDR23nnnTOWef/75AdeRNW6IfwBDU7XHgUre/5kzZ0ZTU1Of5cRAgMIZOXJkbLPNNkXvhyXEIGAoOe644+Lcc8/NVHbEiBFxzTXXxI477hjlUMnn34Q+kPgDDO04VIn0g9qIQ0C5rLLKKnHiiSdmKnvhhRcOyXNwQl9IDAKGbgyqRPpBbcQgqDySPQGgB+uuu26fZebOnRsLFiyIUktGiCnUPgAwOGy22WaZyr3wwgsDriNr3HjttdeiHLLEv7q6ulh77bVL0h6Aoaba40Al778+IMDQ7YclxCBgqPjKV74Sv/rVrzKVTUbL//Of/xy77757lEsln38T+kB9E3+AwRyHKpV+kDgElNdnPvOZ9HN+X+68885oaWkZcufghL5Q38QgYLDGoEqlHyQGQSWS7AkAPVhnnXUylXv22Wej1J555plM5XyoBhg6sp7T33777QHXkVwMSWa3rsTYl7XeZPSw+vr6krQHYKip9jigDwhAOfphCTEIGAq+9rWvxU9/+tNMZZPP7Jdeemnss88+UU76QOIPMHQMxjhUqfSDxCGgvFZfffXYYYcd+iw3b968eOihhwZUh76QGARQrhhUqfSDxCCoRJI9AaAH6623XqZyTz/9dJSaD9UA1Wf8+PGZyuUz43RDQ0N6UaRQcaiQ3nrrrZgzZ06f5cQ+gIGr9jigDwhAOfphCTEIGOxOOumk+NGPfpSpbDI7wO9+97s44IADotz0gcQfYGgYrHGoUukHiUNA+e24446Zyj344IMD2r6+kBgEUK4YVKn0g8QgqESSPQGgB5tvvnmmck899VTR2zKQOseNG5eOxAbA0DBixIhM5RYvXlz0+JeM0Pbaa69FKWWNt1OnTi16WwCGsmqOA4O9D5gQBwGGbj8sIQYBlejb3/52nHHGGZnK1tTUxHnnnRcf//jHo1LoAxWuLYUk/gDVEocqkX6QOASU38Ybb5yp3IwZMwZch75Q4dpSSGIQUA0xqBLpB4lBUIkkewJAD7beeutM5R544IEopWT0sueee67PcltuuWV6wQqAoSHrl0VZv3wabPEva31bbbVV0dsCMJRVcxxYZZVVMo1kXY5RSrPsfzISd9aLQwBUVj9MDAIGq+985zvxve99L1PZ5HrFr3/96/j0pz8dlUQfSPwBBq+hEIcqkX6QOASUX3KOzGLmzJkDrkNfSAwCKFcMqkT6QWIQVCLJngDQg/XWWy/Gjx9fcV9q3X///ZHL5Qr2pRwAg0OS7J/F6NGj86qnUi/q3HfffZnKiX8A+an2OJDl/ckopS+99FKUSmNjYzz22GN9lttss81i2LBhJWkTQLUoVT8sIQYBg83pp58ep5xySubyP/vZz+KYY46JSqMPJP4Ag9NQiUOVSD9IHALKb9SoUZnKzZ49e8B16AuJQQDlikGVSD9IDIJKJNkTAHoZ3XPbbbfts9zzzz8f06dPj1K5++67M5XL0nYABo8k3mQxYcKEvOp597vfnancXXfdFaWUJf6NHDkyNt1005K0B2CoqvY4UIn7n9zY0NTU1Gc5fUCAwdsPS4hBwGBy5plnxsknn5y5/I9//OM47rjjohJV4vk3oQ8k/gDVEYcqkX6QOASU36JFizKVyyfZohLPwQl9ITEIGPoxqBLpB4lBUIkkewJAL/bYY49M5aZNmxalctNNNxW07QAMDo8//nimcuuss05e9ay11lqx0UYb9Vnu73//eyxZsiRK4cUXX4xnnnmmz3Lve9/7oqGhoSRtAhiqqj0O6AMCUI5+WEIMAgaL//u//4uvf/3rmcv/4Ac/iBNOOCEqlT6Q+AMMLkMtDlUi/SBxCCi/WbNmZSo3ZsyYAdehLyQGAZQrBlUi/SAxCCqRZE8A6MWee+6Zqdzf/va3KIX58+dnGsFs6tSpsdpqq5WkTQCUxj/+8Y9M5dZdd92SxL+FCxdmblO+ssbZ97///UVvC0A1qOY4sN1228W4ceMKdrGjELLUVVNTk7n/CkD20avvv//+kvXDxCBgMPjlL38Zxx9/fObyp556apx00klR6fSBxB9gcBiqcaiS6AeJQ0BlyJLwmFh55ZXzqkdfSAwCKFcMqiT6QWIQVCrJngDQi2222SYmTZrUZ7lrrrkmGhsbi96eq666KtNoaXvttVfR2wJA6TQ1NcX111+fqewWW2yRd31ZL4786U9/ilK49NJLM5UT/wAKo5rjQF1dXey+++59lnvppZfinnvuiWJ7+eWXMw34s9VWWw2pC2oAleAvf/lLpu/hVllllVh99dXzrk8MAirdb37zm/jiF7+YufzJJ58cp5xySgwG+kDiD1D5hnIcqiT6QeIQUBmynvM22GCDvOrRFxKDAMoVgyqJfpAYBJVKsicA9KK2tjYOPPDAPsvNmTMnbrjhhqK3549//GOmch/5yEeK3hYASufaa6+NWbNmZRpB673vfW/e9SUXdcaOHdtnucsvvzxNRC2m6dOnx2233dZnuY033jid2RqA/FV7HDj44IML2j/LxyWXXBK5XK7PcvqAAIV34YUXZipXiD5YBzEIqFQXXHBBfPazn810XkiceOKJcdppp8VgoQ8k/gCVbajHoUqiHyQOAeW3YMGCzDNpbrTRRnnVpS8kBgGUKwZVEv0gMQgqlWRPAFiBww47LFO5X/7yl0Vtx3PPPRc33nhjn+XWX3/9eM973lPUtgBQWqeffnqmcslFjYkTJ+Zd38iRI+Oggw7qs9yMGTOKPornr3/962hpaemz3KGHHlrUdgBUk2qPA8mAP6NHj+6z3EUXXZRe8CqW1tbWdP+zDPYgDgIU1qOPPhrXXXddprK77LJLweoVg4BK9Pvf/z6OPvrozAk2X/rSl+LMM8+MwUQfSPwBKlc1xKFKoR8kDgGVIelzLFy4sM9ykyZNine+85151aUvJAYBlCsGVQr9IDEIKplkTwDo4wP62muv3We5m266KR555JGiteOss85KP1j35ZOf/GTR2gBAeUasvvfeezOVLeQIWp/61KcylfvRj34UxbJ48eLMgymIfwCFVc1xILmoklxc6cvbb78d5557bhTLVVddFc8++2yf5XbaaadMfVYAsvvyl7+c6Wby5OJ21tGXsxCDgEpz2WWXxeGHH57p2kQimXXtpz/9aQxG+kDiD1B5qikOVQL9IHEIKL8k2THrQNDJ/Wy1tfnf/q0vJAYBlCsGVQL9IDEIKtnQONMCQJEkH9I///nPZyp70kknFW1Wz3POOafPcg0NDXHssccWpQ0AlN7zzz8fX/3qVzOX//jHP16wunfffffYZJNN+iz3wAMPpDdcFENywejNN9/ss9wHP/jBdGZrAAqn2uPAcccdl6ncaaedFvPmzYtCa25ujv/6r//KVPaLX/xiwesHqGbJgGu33XZbprI77LBDwS9ui0FApUhu9ElGa88ys0riyCOPjF/96lcxWOkDiT9AZam2OFRu+kHiEFAZfvjDH8ZTTz2VqWyhEk70hcQggHLFoHLTDxKDoNJJ9gSAPiTJnmPHju2z3A033BDXXnttwes/4YQTorGxMdPoZWuttVbB6weg9GbNmhX7779/OkJXFh/4wAdio402KuhgB1//+tczlf3GN74R8+fPj0J6+eWX4wc/+EHm+gEorGqPA8nFmp133rnPcm+88UaceuqpBa//Zz/7Wfz73//us1xyU8NQuZgGUAluvPHGzPGvY8TnQhODgEpw/fXXp4OKJTf8ZHHYYYfFb3/727QfMVjpA4k/QOWoxjhUTvpB4hBQGW699db47//+70xlx40bFwcddFBB6tUXEoMAyhWDykk/SAyCwUCyJwD0Yfz48fG5z30uU9nPfOYz8frrrxes7rPPPjuuueaaPsvV1tb2q/MBQO/uu+++uOKKKyKXy5Wl/uRLmj333DMeffTRzO/51re+VfB2JDdIrLnmmn2We/HFF+MLX/hCwepNRupOBjBYsGBBn2W333772HXXXQtWNwDLVHscOOmkkzKP+Dlt2rSC1fvwww9njutf+9rXoq6urmB1A5TTeeedF08//XTZ6k8GcDvggAMy31C+wQYbxEc/+tGitEUMAsrppptuSm/eWbJkSabySTLOBRdckF6jGOz0gcQfoPyqLQ7pBy0jDgHlGmAgmRk663mwWJJZxZLzcVNTU6byn/3sZ2PkyJEFq19fSAwCSq+aY5B+0DJiELAig/PbLgAosZNPPjlWXnnlPsu9+eabaUdg3rx5edd58803x/HHH5+p7NFHHx2bbbZZ3nUCEPHKK6+kNxNsvfXW8Yc//CHzF1qFcOedd8a2224bDz74YOb37LfffvG+972v4G0ZNmxYnH766ZnKXnzxxXHGGWfkXWeSYJvMqP2Pf/wjU/kf/ehHedcJQM+qPQ7ss88+6eALfWltbY1PfOIT8fjjjxfkM0jSn1y8eHGfZTfddNN0sCGAoSIZ7Oyd73xnHH744f3qDxXiprLvfve76fm3sbEx8/uSuFesi9tiEFAut99+e+y///6Zz4fJKP6/+93vhszNPvpA4g9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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "(SEIIRD.S/N).plot(color=colors[0], linestyle='-', label='Susceptible', ax=ax)\n", "(SEIIRD.E/N).plot(color=colors[4], linestyle='-', label='Exposed', ax=ax)\n", "(SEIIRD.Ia/N).plot(color=colors[1], linestyle=':', label='Asymptomatic', ax=ax)\n", "(SEIIRD.Is/N).plot(color=colors[1], linestyle='--', label='Symptomatic', ax=ax)\n", "(SEIIRD.R/N).plot(color=colors[2], linestyle='-', label='Recovered', ax=ax)\n", "(SEIIRD.D/N).plot(color=colors[7], linestyle='-', label='Dead', ax=ax)\n", "\n", "ax.legend()\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('Population')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Time')" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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77ne/2+l1pcqQeaoXppDYX/7ylyxcV03PPPPMh/586NCh8be//S0LNKa/d9TGG2+cBegOP/zw3I854ogjYsqUKVEpf/3rX7NKgnkMGzYs7r333qxKZzlSqO7BBx/8QJXDhUltU9W8clxxxRUlq/qlkFsKP6YKhl0h9b/55ptnY0mhwm9961vR3Nxckb5TJcTx48eXbNe3b9+49NJLs3DpUkstVfZ6V1lllSzkmiqoljJ37twsBFwp6Zi4++67c7Vdbrnl4v77748f/ehH2T7oqBQaffzxx7OQakdeIwCgOxFsBAAAAACg04rtxXj7vP/4pXwxYuqd7fHc1xfEO5e1RtvcnhfaG7xJQwzfufzL64M3b4hVzugVA1Z3qR4AAAAA4H0jRoyICy+8cKFVzd6Xqrddf/31HV5HCjT+5Cc/ydX2t7/9bay66qpRD1LgM1Vd3GGHHcrqJ1UEPOOMM7JwZx4p1PjDH/4wKmHBggXx7W9/O1fbFMC7/fbb4xOf+ERF1r3GGmtkz/2QIUNytT/22GOzqoSdlZ6rUr7xjW/EZz7zmaiGFLT71a9+Fdtss03ZfaWwaaqiWUoKBKeKmV/4whfKXud/95vCkjvvvHPJtukYyhtGLFUpNlVfzGPFFVfM9lHeypIfZdCgQVkQ+Itf/GJZ/QBAvarcbaUBAAAAAFjkTL27PeaM/9/wYnF+xLuXt8fkW9tj6f0aY+hWDVFoKD0JpbsYcUBjzH6pGLOe73hwc7FRhRg5plGgEQAAAOpU28ybon3m32s9DKhLDQN2jMYBn671MFgEfPrTn47vfOc78Ytf/KJk24MOOiirajZy5MhcfU+cODGr3tfe3l6y7f7775/1Xw969+4d1113Xay11loV6/O4446Lt99+O84666ySbX//+99n1f5GjRpV1jpTaPW1117L1TZV+Ft77bWjklK1v1SBc8cddyx5DKRA55lnntnpyqCPPfbYQv89hXePPPLI6I7SPikWF/47koaGhuw53HTTTbuswusFF1wQ6667bsljKlVNvOOOO8paXwqFvvfeeyXbpeDs3//+91hmmWWiEtJ+POecc+Kdd96JW265pSJ9AkC9EGwEAAAAAKBTUjXGdy7+j2qNH6J1asQbv22LSTe2x4iDGqN5jZ4R5is0FWK5bzXF8/+3INrn5ntM44CIpfdtjGHbNUShseeEPAEAAKCnKbZNj2LrG7UeBtTt+UH9SBXI8lQ1rIZf//rXFQ9onXTSSVnFs3/84x8LbTd58uTYZ599skp8Kei0MCmIdeCBB2ZhvlJWWmmlLMxXL1LIc+ONN654v7/5zW/i4YcfjrFjxy60XWtra1YhMy2dlfb/z372s1xtv/KVr8Quu+wSXWG77baLww8/PNv2Uk499dT45je/mQVLO2L+/Pnx5ptvLrTNmmuuGaNHj47u5sYbb4wHH3ywZLtUFfXzn/98l44lhQhTUHWTTTZZaFA1Vc986aWXsvO6M+bMmZOFXPM4//zzY4UVVohKSq9tl112WVZ1NM/rFwB0Fz1jBgkAAAAAAFU38dr2WDAlX9tU1fGV41tj/CkLYt7bHa9yWI96L16IJfdY+CSZTEPE4js2xKq/6xWL79go1AgAAAAAkENTU1MW5EnBpVLuu+++OP7443OFA1MltVL69OmThaUGDBgQ9SAFGlMQryv06tUrqwZXKhSapHYzZszo9LpSUDWFy0oZOnRormqd5QZnl1pqqZLtUoW+66+/vsP9T58+vWRFyGWXXTa6o1/+8pcl26QA4VFHHVWV8Xzyk5+M3XffPdfx21l//vOfY9KkSSXb7b333l0WyE2vhb/73e+6pG8AqBXBRgAAAAAAOmz+5GK8d83CqzV+mOkPF+O5IxbEW+e2RmtL9w84Dt+5IXovZN5D/9UK8fFfNMXIrzZFU7NAIwAAAABARyy33HK5w0innHJK3HbbbR/576ny4/e///3cwa1111036kUK+TU0dN2073XWWSf233//ku1mzpwZV199dafXc+GFF+Zqd/TRR8egQYOiK/Xv3z+OO+64io77P82bNy/XGLqbl19+OauOWsrPf/7zLDRbLd/97ndLtinn2E3BxlLS9qbXoa602267xeabb96l6wCAahJsBAAAAACgw965pC3aS/9O/sO1RUy8oT2e+/qCmHhDW7Qv6L4Bx4ZehfjYwU3/8/NewyKW+1ZjrPiTpug72qV4AAAAAIDO+tznPperWmGqjpfCee++++7//Nu0adNin332idbW1oqtr1q23nrr2HTTTbt8PSnkVyiUvkHfpZde2qn+0/OTJ1iWwn6HHHJIVMNBBx2UVYcs5aabbopZs2Z1qO88wcy33norupvzzjuvZJu11147C+BV0yc+8YnYZJNNFtrmxRdfjPHjx3e47zfeeCPuv//+ku0OOOCAGDVqVHS1E044ocvXAQDVYjYFAAAAAAAdMvuV9ph6Z3vZ/bTNjHj36rYolp5HUtcGrl+I5vX+Ndmj0CtiyT0aYpUzesWQzRpzTQIBAAAAAKAyFRRTqDGFG1OI7j+NGTMmXn311YpWiKyWQw89tCrrWWGFFWL77bcv2e7222/PKjd21NixY2PKlCkl2+29995dXq3xfX379o0DDzywZLsFCxbE3Xff3aG+m5ubo3fv3gtt89BDD2Wh2+7k2muvzfUc1sK2225bss0dd9zR4X6vv/76KBaLdXOubrPNNrHyyitXZV0A0NUEGwEAAAAAyC394vat89sq1t/S+zZGY9/uHf5L4cVUtXHQRoVY5fResfS+TdG4WPfeJgAAAACAetKnT5+4/PLLs7BYKbfddlucfPLJ//7/3/72t3HVVVeVfFxTU1NWjXDIkCFRLwYOHBi77LJL1daXJ+SXql7ee++9He47PS95fP7zn49q2nPPPXO1S4HOjlp66aVLBiZPOeWU6C5S5cJnnnmmZLu99toramGLLbYo2eaxxx7rcL95wpCrrLJKVjWyWlJ1SADoCQQbAQAAAADIbcY/izHrmdJ3pc2j7+hCDP1Uz7hMvdiIQow+ulf0WUqgEQAAAACgK6y44opx9tln52p7wgknxH333RePP/54fOc738n1mBNPPDE23njjqCc77rhjyap/lbTTTjtlAc9S7rzzzg73/fDDD+eqoJiq0VVTes6HDx9esl2qrthRm222Wa5qpCm02x3kCaeuvvrqWfXPWkjPZboZZSWDjemGn3mqdVYzgJzstttuVV0fAHSVnjFjBAAAAACALpd+eTvhispVaxzxpcYoNAgCAgAAAACQzz777BNjxowp2a6trS323XffrHLcvHnzSrbfYYcd4uijj456U+2Q3+DBg2P99dcv2e7RRx/tcN9PPvlkyTYbbLBBVp2zmlIQbtNNNy3Z7qmnnupw31tvvXWuYzUd18ccc0y0tLREPXvkkUdKtllzzTWjVvr16xeDBg1aaJuXX365Q32+9tprMWnSpLo7V9dYY41YYoklqrpOAOgKgo0AAAAAAOQy69lizHmlMtUaB25YiOY1XaIGAAAAAOisLbfcMrshXT0sRx55ZNW2+4wzzsiqwpXyxhtvxEsvvVSy3dJLLx0XXnhhyUpvtVCLCpJ51vn00093qM/Zs2fHuHHjSrb75Cc/GbWw0UYblWwzY8aMLOTWETvvvHNWhbKU9vb2+NnPfhbLLbdcfPe7343nnnsu6lGeaoerrrpq1NKwYcMW+u8TJ06M+fPn5+7vmWeeKdkmvXbU4tittwqzANAZZo0AAAAAAJDLe9dWqFpjY8SIA5sq0xcAAAAAAIuUFBS7/PLLs+ps5WpoaIiLLrqoLiuf9erVK1ZbbbWqr3ettdYq2ea9997LVcXufSkQmAKwpayyyipRC3nDeK+++mqH+h0+fHgccsghudtPnTo1fv7zn2fP+9prrx3HHXdcPPDAA9Ha2hr1IE+gtRbHbEeCjek4fOedd3L39+yzz5ZsM2rUqJKVIrtCOkYAoLsTbAQAAAAAoKS5bxZjxiOVqda4+I4NsdiI+rvzNQAAAAAA3UMKT6XKjeX6/ve/H1tvvXXUo+WXXz4aGxurvt6Pf/zjudq9+eabuft8++23c7VbaaWVohZWXnnlim7Hfzr66KNjyJAhHX7ck08+GSeeeGJsuumm2eN32GGH+PGPfxy33XZbtLS0RLVNnz4913r33HPPrIJhrZaHH3645Bg7sv9S5ddKHT+1OlcBoJ4JNgIAAAAAUNLE6ytTrbFxQMRSe1V/IgYAAAAAAD3LwQcfHPvtt1+nH7/55pvHCSecEPUqVYGr5/V2pOpd3rZLL7101ELe9XZkm9+31FJLxWWXXVZWSHXmzJlxyy23ZMfrdtttF4MHD86q9X3961+PSy+9NCZMmBBdrSNB1no3Z86c3G3zPOf1fq4CQD0TbAQAAAAAYKEWTCvGlLvaK9JXCjU2NavWCAAAAABA+c4666xOVfkbNmxYXHLJJTWpiJjX8OHD63q9HQnTpWp/eSyxxBJRC4MGDYo+ffpUbDv+2/bbbx+nn356VlGwEtrb27OKjmeeeWbsu+++MWLEiFh33XXjuOOOiyeeeCK6QjXCk/UYbMyz3fV+rgJAPRNsBAAAAABgoSbd1BbFBeX302fpiGE7uCwNAAAAAEBlDBgwIC6//PJcobT3pXDZ+eefHyNHjox6lsKXtdCrV68YOHBgriqCec2dOzf381kredbdkUDcf0vVFVPlxubm5qi0YrEYjz/+eJx44omxzjrrxJprrhmnnXZazJgxo2LrmDVrVvQUKRiaV57jvFbn6uKLL16T9QJAJZlBAgAAAADAR2qfV4xJN1WoWuM+jdHQS7VGAAAAAAAqZ9VVV43Ro0fnbv/Vr341PvvZz0a9W2yxxep63XnDinnbpuqZDQ21m9qeJxzbkW3+MHvttVdWabGrj7+nn346vvnNb8ayyy6bVXFsaWkpu89yt727mjdvXt2eq7V8jQCASmmqWE8AAAAAAPQ4U+5oj7b8N13+SL2XjBi8sXvtAQAAAPWv0DgoCk3L1HoYULfnB9Sbo446Kp5//vnc7e+8886sClstqwPm0bt37x4T8stTIa+Wocb3g5WVrPT3UUaNGhXXX3993HPPPXHyySfHzTffnFVc7ArTp0/Pqjj+8Y9/jN/97nfx+c9/vksDfj1Rnu2u1bnakUq1AFCvBBsBAAAAAPhQxbZivHdDW0X6Gv7Zxig0qtYIAAAA1L/GAZ/OFgDq37XXXhtnnHFGhx7z4osvxmGHHRYXXnhh1LNKhOg6q62trSJBwI4EsBYsWJAF/AqFwiJRmW+LLbbIlpdffjk7Fi+//PJ44YUXoiu8++67sccee8QhhxySnS9NTR2PEPTq1SsWRXkCt7U6V/OcpwBQ79weGwAAAACADzX9n8WY/075/TT2jxi6tcvRAAAAAABUzuuvvx4HHXRQpx775z//OS644IKoZ/Pnz6/Zuisd8svbtpZVAfNUoKxksPF9K664Yvz4xz/Oqo6m0G0KHu65554xYsSIiq/rrLPOii996Uudemzfvn1ztbv11luzgGo9L1tttVXu7c7znNfqXF1Uq2gC0LOYSQIAAAAAwIeaeG1l7vQ6bIeGaOyrWiMAAAAAAJXR2toa++yzT0ydOrXTfXz961/vsgp5lTBr1qy6XndHQn79+/fP1W7SpElRq4DYjBkzKrYdnbXSSivF4YcfnlVvfOutt7Jqjuedd14cfPDBsfLKK1dkHRdffHH88Ic/7PDj+vXrV7GAaHeS5ziv1blay9cIAKgUwUYAAAAAAP7HrOfbY9YLxbL7KTRFDN+psSJjAgAAAACA5Pjjj48HHnig7FDQ3nvvXbdVz957772arLelpSVXOG3o0KG5+1xqqaXqepvzrjfvdlTKCiuskFVYPOecc7IQ7sSJE+OKK66IQw89NEaPHt3pfn/0ox/Fww8/3KHHLLnkkrna5QmIdidDhgzp9sctANQzwUYAAAAAAP7He9dVplrjkC0aotdQ1RoBAAAAAKiMW2+9NU455ZSK9PXEE0/Et771rahH7777bl2vd+mll87dZ962r776atTC+PHjK77NXWHxxRePPfbYI84888wYN25cPPPMM/HjH/84VlxxxQ739YMf/KBD7Zdddtlc7VKlyZ4kz3Ne7+cqANQzwUYAAAAAAD5g3jvFmP5Q+dUak+E7uwxdaW1zi1Fsq8zzAwAAAADQnaQgzwEHHBDFYulrpNtuu20UCqVvvJdCYldffXXUm5dffrkm633ppZcqHvJbZpllcrVLVQlrIe96R44cGfVktdVWi+OOOy5efPHFuOmmm2KjjTbqUED4oYceyt1+8ODBMXDgwJLtXnvttehJ8hzn9X6uAkA9M6MEAAAAAIAPmHhDW0QFcnPN6xai73IuQ1dK66xiTLiyLZ49ZEFMva+91sMBAAAAAKiq9vb22H///XNVKVt//fXjxhtvjG9/+9u5+h4zZkzdBbLeeeedmDFjRtXXm0JypTQ2NsZyyy2Xu8/hw4dn1QZLeeyxx6IWxo4dW7JNr169YuWVV456lAK8O+64YzzwwAPxi1/8Ivfjbrjhhg6tZ80118xVBbUnWX755bt9IBcA6pkZJQAAAAAA/FtrSzEm316Z0NwSuzZWpJ9F3YJpxXj7otZ49msLYsIlbdE2I+K9q9qj2K5qIwAAAACw6DjllFPitttuK9muubk5/vKXv0Tv3r3jpJNOig033LDkY6ZOnRr77rtvtLa2Rj155JFHqr7Ohx9+uGSbFVdcMfr06dOhftdaa62Sbe6///6ohRQILOXjH/94dkzVsxRw/M53vhM///nPc1dt7Ig859Ljjz8eCxYsiJ5ijTXWKNlmypQpMW7cuKjHcxUA6p1gIwAAAAAA/zbp7+1RnF9+P31HF2LAmoVKDGmRNX9iMd78U2tWoTEFGdtn////NveNYrQ8KdgIAAAAACwaUuDthBNOyNX27LPPjhVWWOHfVfYuu+yyGDRoUK5w23HHHRf1pBZBvwcffLAilfv+27rrrluyzdtvvx3PPvtsVFNa51NPPVWR8deLVKk0z3hTcLYjYd5PfvKTJdvMnDkz7r333ugp8gQba3Guzps3L1elUQCod02xCHjvvfeyD7lpSeXnW1pasiV9WUl3ZRk4cGCMHj06VltttexuGn379q31kHuEOXPmxPPPP5/t91dffTVmzJiR7fd0F46039Oy1FJLxaqrrprt+yWWWKLWQwYAAACARVr7gmJM+ltbRfoavktDdmdgOm7u28V476q2mHJ3e8RCno7JN7fFwHXcvxAAAAAA6NlSNbR99tknVwDr4IMPztr+pzRH+I9//GPstddeJR//s5/9LLbeeuvYbrvtoh7cdNNNVQ1bPvfcczF+/PiS7TbZZJMO9/2pT30qfvWrX5Vsd+2112bziqslra9YLH0jwXRcdBcNDQ3x5S9/Ob7+9a8vtF1bW1tMnjw5llxyyVz9pvOiqamp5Ll41VVXdav9tTApZ7D66qvHM888U/JcPeCAA6o2rttvvz0LNwJAd9cjg43pw+Udd9wR1113Xfztb3+Ll19+OfdjU9hxs802i5122in22GOPGDVqVJeOtadJX2auvPLKbL+nO090pJT4SiutlO33XXbZJfvyYtITAAAAAFTX9Ifbo3V6+f30GhYxZFOBu46aPb493vtre0x7sD0iRzHG6f8sxvzJxeg9zLVUAAAAAKDnSmHFN954o2S7VGjjjDPO+NB/23PPPeNrX/taVs2x1BzkFE564okncoe9utJDDz0U77zzTiy99NJVWd/VV1+dq11nQmtbbrllNk+71NziCy+8MI499tiolnPPPTdXu2233Ta6k2222SZXu4kTJ+Y+1ocOHRqbb7553HnnnQttd/HFF8fPf/7z6NevX/QE6XjPE2xMQcM+ffpUZUwpPAoAPUGPmlmSPgyceeaZ2ReT9OHxN7/5TYdCjUn6sJw+bB111FFZGfrdd9897rvvvi4bc0+RSoZ/7nOfixVXXDGOPvrouOuuuzoUakxeeumlOP3007MP0ulOK7///e9rcieJrbbaKgtVdsclVcYEAAAAgM6ackd7RfoZ/pnGKDQJ2+U187n2GHfignjx260x7YF8ocZMe8SU2yvznAEAAAAA1KM0FzhV1CtlscUWi7/85S8LDVKddtppscYaa5Ts6913383CjXmq+HW19vb2OO+886qyrrS9eUJ+w4cPj7XWWqvD/Q8YMCArfFLK888/H7fddltUKzj6yCOPlGy39tprx8iRI6M7GTFiRK5206d37I6Xe++9d8k206ZNKxki7k7yhFrTNv/1r3+tynhaWlqy1zsA6Al6TLAx3eUglXlOJbNfeOGFin0ZSHceSXeW+MIXvhBvv/12RfrtSd56663Ya6+9Yosttohrrrkm22eVkL6UHHbYYdkXyJtvvrkifQIAAAAAH23+pGK0PF7+JI2GvhHDtusxl567dILIjMfb46UfLIiXv98aM8Z2bt9PvrUtim21n1wDAAAAAFBpY8eOzQqV5PHrX/861lxzzbLDj++79dZb45RTTol6cNZZZ8X8+fO7fD033HBDvPLKKyXb7bHHHlkxjs7Yf//9c7U74YQTohqOO+64XO0OPPDA6Kk6WlUxPYeDBg0q2e6nP/1pTJkyJXqC7bffPgYOHFiyXSrwUw0pgDxz5syqrAsAulq3n12SKvqlEvM77bRTrg/TnZW+yKyyyirKNv+HdFeJVB3ziiuu6LJ1pIqbO+64Y4wZM6Ym1RsBAAAAYFEx9a4OVApciBRqbOyvWmMeE/7SFrOeLW+nL5gcMeNRwUYAAAAAoGdJFclSZbg8gb4UtDvkkENy9bvaaqtlVSDzOP744+OBBx6IWnvjjTfiD3/4Q5ffjC9vmHCfffbp9Hp23333rHJjKWm/X3755dHVQc4UYC2lqakp9t133+hu0nGTx7BhwzrUb//+/bO5+6VMnjw5Dj300OgJUih61113Ldnu4Ycfzo6rrjR79uw4+eSTu3QdAFBN3TrYmCoopkqB1Sqxnr4kpS8/6e4c9VBevlbStqd9sOeee2b7pFp3lthyyy3jnXfeqcr6AAAAAGBRu+Y3+c628jtqjBj+2cZKDKnHS3ezXvLzldlXk26pwHMHAAAAAFBHUlAxFccoZdSoUfGnP/2pQ32nYht5wnmtra1ZoG3q1KlRayl0+O6773ZpVcjHHnusZLtUEGWzzTbr9HpSKO5rX/tarraHH354vPfee9EV0nOaNwy73377xVJLLRXdzS233JKrWuMyyyzT4b6PPvroXAHVFE499dRToyf46le/mqvdt771rZgzZ06XjSMFrrvytQAAqq3bBhsnTJgQW221VXZng2pP8DnxxBPjm9/8ZiyqjjjiiGwfVDvc+dBDD2XPuQ9jH33HlBEjRtR6GAAAAAB0Q7OeL8b8CtxTbMimDdF7cdUa8xq4XiF6L1F+Py2PFWPeu4vuzfgAAAAAgJ4lFcO45JJLSrbr1atXXHbZZTFo0KAOr+Pss8+OFVdcsWS71157LQtC1tqUKVOyYFVXzN196aWX4rvf/W7u0Fa6cV85vvOd72QV8EqZOHFiVpBmwYIFUUltbW1ZNdC33nqrZNuGhob43ve+16n1pFDf9ddfH7WQ9lmeKp8bbrhhp57PFPTMe8yk5/uPf/xj1FIq5HPSSSfF66+/3uk+UqA37a8851MKfnaFu+++O0477bQu6RsAaqVbBhvTh/Ntt902e+OvldNPPz2+//3vx6ImfTg/44wzarb+F198MXvu0zHABx1wwAHRu3fvWg8DAAAAgG5oyh3tFeln8U93y0vONVNoKMSw7StQtbEYMflWVRsBAAAAgO7vueeei2984xu52qYiHZ/85Cc7tZ7m5uYsFJln3uXVV18dv/vd76LWrrvuuqxyYyVNmzYtdt111yz4VcrIkSOzuarlSqG4I488Mlfbe++9N77whS9ULNyYQo0HHnhg3HrrrbnaH3zwwbHyyit3al3PPvts7LLLLrHeeutlQd1KBzQX5phjjsnOpVJ22mmnTq8jBRZTBc9SUhg3hXJ/8IMfZPu/miZNmpS9TowePTqb9z979uyy+jv22GNztfvtb38b5513XlTS+PHjY88996z6PgSArtbtZpmkDzf7779/PPPMM7kfk+6WsfXWW2cfElLVv1Txb/78+dmH8FdffTW7G0b6ALfssst2aCzpzg3pbhqLivQF7uSTT+7QY9IHwRSGvPHGG7O71sycOTPmzZuXPQf/+Mc/4je/+U1WhbEjd/t4+umnsy8V1a4YWe/q4Y5AAAAAAHQ/bXOKMe3+8oONfUZG9FtZtcaOGrp1QxSayu9nyu3t0b7ANVMAAAAAoPuaM2dO7LXXXrnCRzvssEMcddRRZa1v/fXXj5/97Ge52n7729+OJ554ImrtJz/5Sfz0pz+taKGZPAG45JRTTok+ffpUZN3HHXdcjBo1Klfbq666Kj796U/He++9V/b27rzzzrmqgSbDhw/PfXwszGOPPRb77bdfLLfcclkw9ZVXXomukkJvqZLiqaeemqviaTlB1VR1M+3LvEVZ0nGbqh4+9dRT0dUeeOCB+PKXvxzLLLNMdqxNnjy5Iv3utttuscUWW+Rq+5WvfCX+/Oc/V2S96ZhJ8+1TFVMA6Gm6XbAxfSi+6aabcrfffffd4/nnn4/bb789vv71r2cloJdYYonsw9iAAQOyD4mf/exns8BeCjleeumlWRgvr/Shp5aVI6tZKTHdLSOv5ZdfPgt9pg9S6YNouqNHCo72798/+wCbnoN0l5x0V50777wzuytJ+rCXVwpK/vznP+/k1vQ86bheY401aj0MAAAAALqhaQ+2R/vc8vsZtnVjh25gxr/0GlyIQZ8s/1J96/SI6Q9XpvImAAAAAEAtpCp+qfhFKUsvvXRceOGFFbkmndaZwm6lpKIee++9d8yaNStqLVW+S4G0VGyks/75z39mwc5HH300V/tNNtkk9t1336iUfv36xR//+MeseE0eaR742muvHRdccEGnCqP85S9/yR7fkTnoqaDO0KFDo1Leeeed+PGPfxwrrrhiFvD79a9/HS+//HLF+r/rrruy0F3e+dXpGErVM8uxzjrrxOmnn567fSqKkx7zxS9+McaOHRuV0t7eHg8++GBWlTHt30033TTOOeecmDu3Ar8A+y+nnXZaNDY25q4OmoKm5VTrvOGGG7J54q+//nqn+wCAetatgo3py8rxxx+f+wNv+tLy17/+NVZaaaVcj0lfcFK58ieffDIr1ZxHqvqYPlz15OqBadu+9KUv5Sozn+yzzz7/3od5vzSussoqcfXVV2dlt/v27Zv7i1lHKnd25IN92uZaL+lD7JJLLplrzKo1AgAAANBZU+6oQBiuIWLIlt3qcnNdGbZDZfbd5FsEGwEAAABgUXL33Xdn8zTraclbhe+/pWIaf/jDH0q2S0G4VAUtFdiolDR3deTIkSXbvfDCC3HYYYdFtaT5uKkq3oe56KKLYuWVV87CgR0Jb6UiMIccckhstNFG2d/zSIVkUqCw0jc3TNUif/jDH+ZuP2HChGw+c9ruX/7yl9nzsTDjx4+P3/zmN7H66qtn88PffPPN3Os64ogjsuqhXeX++++Pb33rW9kc9xTEO+igg+JPf/pTFjjNG1hNYdsUDkwFaDbYYIP41Kc+lVUqzGPgwIEVq/yZjqdjjz22QyHENMc/BWtTyDFVsbznnntyz1NP3nrrrbjtttuyEOeuu+6avR6k8O1JJ53UpRUxk3XXXTerAplXGmMqXnPllVdmYce80lz8z3/+81nwOlUb/TDmjwPQEzRFN5Kq+7W2tuYKNaY7auQt9fxhH8DTF6Svfe1rub4kpTs8pA9YKeDYE6UvI2kb8zj88MPjjDPO6PS60heOVNnxM5/5TMkvWulYSMfEHXfcET1RusPGu+++m+t4T1+4AAAAAKCj5k0oxqxny79p28D1CtFriGqNnTVg9UL0+VjEvLfK62fmU8WY+1YxFvuY5wIAAAAA6D7GjRsXX/nKV3K1TQGqbbbZpqLrHzZsWFx88cWx9dZblwwepfnCKUC22267RVdLAb5U4e/oo4/+yAqAX/3qV+N73/te7LLLLllQMAWolllmmWwudJpnO23atHjppZeyyox/+9vfssqHKVjWEWlecArfdYVU5CQVvknztvNKVQ6POuqobFl88cVj1VVXjeHDh2fzadPc48mTJ8dzzz2XBSE7Y/vtt49f/OIXUS0piJeW888/P/v/FCBNhVFGjBiRVVRM25UCrun5TBVDU/Dxtddey4KpHX0u35eqGZZbrfE/pUBhCiamKpcd8cQTT2RLOs7TdqeAcTp+U1AxFcrp1atXFuBMz+vUqVOzY/7tt9+ueeXUdNzeeuutWUA1jxdffDErGJS2LQUx02tICtym6rP9+/fPtjFt3/PPPx8PPfRQNoe81Nz9dHz86le/yp5LAOjOuk2w8Yorrsgq6eVx6aWXdjrU+J/OOuusLFh27bXXlmybykSnuyKkLwI9Sfrwe8wxx+Rqm7Y/3dmkXOmLYfqCmPor5c4778zuYLHHHntET5P3g2b6oJvunAIAAAAAHTXljvx3hl2YoVs3VqSfRVX6Zf2w7Rvj7fPKfz4m39IWHzuo21z6BwAAAAAWcQsWLMiKO8yYMaNk20033TR+9KMfdck40rzj448/PqseV0oqypEChF0V9vtPqapfqkx3yy23fGSbSZMmxbnnnpstlZbCg6loSVdeH0/VJ1Nw7brrruvw49O233vvvRUbTwqcXXPNNVmgrlaKxWIWyuxsMLOUn/zkJ10y7zoFYFMgMZ1Hnd3uN954I1vqXWNjY1x11VVZ5dNUGTSvtG0p/NnRAOh/a2pqyirXDho0qKx+AKAeNEQ3kT5E5XHkkUdmdx2p1IflVF4+3R2hlBSATOXce5qzzz47V9XAUaNGZUG8SpWZ33333bPqj3mceOKJ0dOku4mkqqN5KCMOAAAAQGcU24ox5a7O3cn3PzUOjBi4vgqB5Rq6VUMUepffz5Q726N9XvlVOAEAAAAAqiEV3/jnP/9Zst3QoUOzwicpUNSVVdhSsC1P0ZCDDz44q7LW1dL2puIwqbpbte27777xs5/9rMvXk0KEaRu7MkCZRyrIkirlpUqBPVGa4/3Tn/40O867ynHHHZdV3xw8eHD0dCnEmaqgpj+r7fe//31WSAgAeoJuEWxMb/pPPfVUyXap/HT6wFVJQ4YMiV//+te52qZ26c4xPUXalrzbfvrpp1f8rg8nn3xyVmK7lFSC/O9//3v0JBdccEG0tZW+O/vKK68cm2++eVXGBAAAAEDPMvOpYiyYVH4/Q7doiIZego3lamouxOBNy79k3zYzYtqD5QdWAQAAAAC62o033ph7nmqqRpinUEk5GhoasuqBw4cPL9k2zWtOIa5qGDhwYDZPdrXVVotq+epXv5pVhKtUwZNSevfunRWjScdD+ns1pfBoqgSawpX9+vWLnigFg//617/G9773vS5f15577pnN7a634F2fPn2ypZJWWWWVuO+++2LZZZeNakjn489//vP48pe/XJX1AUA1dItg46mnnpqr3UknndQlHyjTHTjyhMdSeeh0l4me4i9/+Uu89dZbJdulD56VqpL5nwYMGJC7GuOvfvWr6EnSF/A80h1/AAAAAKAzJt9RmfDb0K27xWXmbmHx7SuzLyffLNgIAAAAANS3ND81VegrFosl237jG9+IXXfdtSrjGjFiRFacIk+g749//GMWzqyGVPwlBai22GKLLq+e+Itf/CLOPvvsLOhZbUceeWQ89thjsckmm1Rlfeuuu248/PDDcfzxx1c8xLn++uvHGmusEbWUtmnvvfeOZ555Jj73uc9Vbb0p6Hf77bdnYcoVV1wxammjjTaKM888M955550YPXp0xftfaaWV4h//+EdsueWW0ZVS4DeFjY866qguXQ8AVFvdzzhJYcE77rgj1wegffbZp8vG8d3vfjdXuwsvvDB6ivPPP7+i+6YzDjjggOxLYinpGMkTwuwO7r777nj55ZdLtmtqaoovfvGLVRkTAAAAAD1L68xiTH+o/PBb3+UL0XdU3V9m7jb6rVyIxUaVP3Fi1gvFmPOqcCMAAAAAUJ/a2tpi3333jUmTJuUKnqWgXTV9+tOfjm9/+9u52h5++OHZXOdqGDJkSDZf9uSTT47FFlus4v2vueaaWXjyO9/5TtRSqkyZxpFCceutt16XrGP11VePiy++OP75z3922Tp23nnnrLLnq6++Gr/73e9ip512ir59+0Y1pDnGe+yxRzzyyCNx2WWXxVJLLRW1sPvuu8ezzz4bl156adXCqinMmV43fvCDH8QLL7wQDz74YBx66KHZ+dNVll566SzI+ZOf/KRLnuMNNtggHn300dhvv/0q3jcA1Frdzzi55JJLct2N5etf/3r2IayrpA+TK6+8csl26UPJhAkTort7++23484778z15WH77bfv0ju/pOe2lPb29uxYWZSqNaZjslZfNAAAAADo3qbd1x7FBeX3M3Sbur/E3K2kX7YvvkOFqjbeItgIAAAAANSnH//4x3HPPfeUbDdgwIAslNWnT5+otpNOOik23HDDku2mTZsWX/7yl6O1tbUq42psbIxjjjkmC8wddNBBWRW3co0aNSrOOuusrFJinm2u1vXyFIpLYa4UckzBtOHDh5fVZwq2HXzwwdlc77T/Urg27c+uttxyy8Vhhx2WVfecMmVKtj0prJu2LwXiKjnnOlUNPPXUU+P111+PK664ostCmx0d1xe+8IW4//77s8qRKfyXqllWskJmqpr4ta99LdvmiRMnxtixY7P15Jn7XynpWHo/TJkK11Ti3Pz4xz+eVYZN4cxaV/8EgK7SdUnACkl3aCgllTrv6jsQpA9P+++/f1ZqvNRdZC6//PL4v//7v+jO0jaksGApaZ90tbSO9EGvVMA1HSvdvbz2jBkz4sorr8zVdsy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eLqr8WBeVijNew7BQZH10LiGjPx/BxkUqGGOMKlqkihajhnVS0OPpqQdmcJjZumv/rllpe2aBO10Xvzt/4V333VTZCqV0m2tZhCDdVzqVkl93KOgYBUKjX/uBx27qgpL1RhULJC9OEBIAAAAAAACzONjoQlt33XXXhOu56hJvfetbC9aPj3/84xMGzJxvfOMbMyZg9rWvfS3rY1Mob3/72/WXf/mX2rlz5xHXc+eIC2G6wbn5wHkHAEB5BKWe/2g6qrTo7ro7W/S3LinavhLzd+YcbFx4/X+q/pSH5Vf2qdBidZ2KNe5Xum1OTtvp23GVqlfMjLuRF1oYhurt7Y0Cit3d3eM293w6nS51dzFGJfhFixYNVWV0j92gJgAAAAAAMLsZE5OpOEpyrebioeuxUWXA1NZMhUc3TbupuxEu131mHiuFnVGzytzseMLYVlQRsmEg6Fg3LPjoSsC5xy4omWlD86Zyxl2Pcr8rfS/nGHIzUmJB8Y5LcncZh/IKxFUozOe5526yOVOEfZmW2jt4Xox/fpzwb3FVLi1a1wAAAAAAADALlX2w8Tvf+U7mS5QJfOADH1AsVri3c+WVV+r444/XCy+8cMT17rzzTrW2tmrBggWazlyQ8O67755wvRNPPFGvetWrCtaPeDwe/Wz//M//fMIB1+5c+djHPpaX/XLeAQBQeu4L56BrdoUanaCrQenOuijEV2gV83ZJz+S2jVhNd1FCjcOrNnZNMthoZaMv3mtP8FVzglHdmtldoW4wrNjT0zM0dW2swKJbns1/F6P03GenhQsXRkHGwamr0AgAAAAAAJCNKAAUmysTmytVrRtabm1KNr1zZOAxtTUKQWI2VoTskw12S6ksgpAR/7Cw4+hpNG8Gl7mQpJuvkTHleXO69EEp6M5tG/EWyasoXrCxf3fu26iYF/34FXRoWog35vf4JvfPzuvkrrJjMQQ9Nqo66ddKfo1k/JkViAYAAAAAAMA0DjZ+97vfnXAdz/MKXq3OfZFzww036K/+6q+OuF4QBLrxxhv1h3/4h5rO3HtwA54n4o5Jobl9/MVf/MWEA6rduZKvYCPnHQAAGWHKKuiU0h1W6Y7MtKLFqOaE4gTDYo1GqVn4ZbGr2hire67g+6mY25rzNvp3LVHd6sdULJVLXlLX04cGVo3Fr7fqrN+rZEub+ue2qb+lXZe/9lIlEmX/8SevQcWx5t3UNUx/LS0t0Y1NXHMhRvfYfUYBAAAAAADIJ2PiMvHlkmvD2LA7Cjqmejdr24v3q7aqTbWV7UrEi3cTNEwHgRS2R23i2nCjmOphAUiXdKqW8dyyasnUDM0bU5NZdth8RUHekQtdHfOXsahqY992q/7tmQqOkwk7Vi4qbmgrHxUbF7zZV/NFvlJtmffdt22gbbfq3VZ+N6l0FRvzaTZ+V+XEihRs7HzC6qV/OVQd2KvKhBxjtSb6nYsCj26+WvKrB5a5X/UqozAuVeyvVxhPK6xIR1MAAAAAAABMH7Fyrxq4fv36Cdc799xztXjx4oL3561vfeuEATPntttum/YBM/cesvGWt7yl4H1ZtmyZXvnKV+r+++8/4nqPP/54XqoWct4BAGYim7ZKd7lqgFK6cyCs2GWjx0HnwHODy4Y9F46Rf2q+xCtYsNHatBS6HXdFA2Ni9a4yX71mY7Cx5rjCBxsTrmJjjvp3LVXhVchUHCNTcZxqVp2sfbcfesaNTak62qjmOKPq442qj/NkG5L6+c8f1XTjbuTR39+vvr6+KHzopoPzox8Pn7rXYGZraGgYCjG6Nn/+fFVUFGZgFgAAAAAAQDaMq6iXWCXpGD27PRhaHvf7dcmFqxQze2RTL8umd0RTBXvcLbpK2mdMM7bHlXGTjc6dgUWT2kBsIOToqj8OBiJHhyMzQcjM81WSqYqmJppWRo+NGTmsxksY1a91bVhXbaba3GDYMWovW/W/bJVuP7xniWkYbKyYZ4YqIbpWt2bk+08dUBR07N9h1bcjM+3fmVleCrGG/B3j6P3NwiK1xayc6L6XHM59P+laau/w5eOdx0YLdc6IJRu+Z+VXJzPhx+rBQGQmFOl+/YcCkm5aM7CsamC+MjMf/QmgciQAAAAAAMDsDjb+8pe/zGq9q666SsWwYsUKHXfccdq4ceMR17v33nuVTCan7UBTNzD6vvvum3C9VatW6eijjy5Kn6688soJg42D50yuVSQ57wAA5cgGVkGvojv+Bt2ZO/8GPcPmx1w2MD9OQHGq0u3jfwFubSjZfinslmyvrLtFr+3OTIcCi27qlmUey2ZCjJn5kXfzTh/4xOwMNu5aUpT95KViY2ue+2oqZOJHycSPzYQZ464tHRo8UnuS1dzXBKo6xqj6GE+JRYd/sVyqnF80uCGViv6bbDBweKQ2fJ3BkOJEVcox8zU3N2vu3LmaN29e1FyIsbq6utTdAgAAAAAAyEoqSEjx4+Un1oy6dpaUTe+SUjtk0y/LDk7TOySbLFl/MZO5Gyl2RC2baNT44kOhRzMQdpSpHBaAzDzn+ZWqPrpK1cdUygyGJE2Vgq5q9e2sUX9rQv274krukmpWFjcs1Z+HYGO8Zfw+G2NUMUeqmGOkYYFPJ+jNBByjoOdOF3iU+tx0py3or34+Kza679vCWXh/Qfe+178lmQn5ueBfpYlOd/dr4KqOLn53/oacue8y88mmTBQqzgSLp/4XwN1cczDk6KpDDs77VZkAZFRZcuC5EfOVY7/OixOUBAAAAAAAmJHBxksvvbTgfRm+r4kCZj09PVEI76KLLtJ05PruBlaX23H/8z//8wnXu+OOO4oWbOS8AwBMxAWU3BedLtMX9LlwYSacGD3utQr7MkHEoeXRYzt0F1L3miiY6NbpUdlIHdit9P4bZd3dkqPAYs+heev+GyKPwSxvdoa88h4WHEes4YBMvF82lZjyNvp3L5YNjcxUflbu7tjx5YcCjG4aWyJj/HFfEm82WvyuWEF+X10g0bXBcOJgGyuIOF4LQ+46j+zEYjG1tLQMBRhdc4+5UQkAAAAAAJiJTHRDs+WSa6NvlhfsG6juuDMKP9p0ayYESZVHlIWUFKYkdcgeKk4ayfaqeKIp07RqYJiOqVRyVyKaGpOZyk29hMzg/MA0E6Ycta439mO3bRcyzHvFRuNCi1N7qQuAVR/r2sjlNsxUQYxCjgPVHfsGqzzuK6+Kjal9s/O7KseFT9MugDoqIDhWJdJcBKMqNpb7+5/qd6HuHp5RwNH9uldKx/9TPPodAQAAAAAAmM3KOtiYTYW+hoYGrV076pZvBXThhRfqC1/4woTrTeeAWTbHffBYFMu6detUW1urrq6uvPQ9121w3gEABu3+YaCejeFAcHEgsDgYZOybmWMu0m0ufPnrouzLr83zN6PTRLGCjS6M6Ko29u8cOZhoMmyqQsl985WYd6Tqj55MbLFMfFkmyBhVZFwm+XPHHGSRDRcgHB5AHD7vbvjQ3t4erTPY7rzzzmg6PLA4/HVuChRKZWXliACja64yo+d5pe4aAAAAAABASRnjSbF5MrF5kk4b8Zy1aSm9ZyjoaINhocf07pl5AR6zQFqyXUMl6kbHo3KLd3kDIciKQ80mlNzziZyGB8Wb0rK2VUqP2vY4QcpsGM+oYp5UMc9Ip458bt9tab38pdx+v+N5rNiY2p+/bc0ULpSX7+qQs4H7Z8396g9WqPSKdJ/D7ufD6DttL2EyocqB5n6NXRXJaDrQjCtSWzFsWXxgvYrD13O/xwAAAAAAADM22OgGIm/ZsmXC9Vy4bKoXSqcasMvG448/runqsccey2q9008/XcXiBvy6n/V99913xPVefPFFdXR0qL6+fkr74bwDgPJhAxvdANe6G+EmM3fEDJN25ONo3g7NZ9Zxd2GV5lwyfsW3fH8R1PFIed5FtFCC7qn9OzsVsbrZGWwMuhqU7qxTrK6z4PvKNdg4GMQcCjZ6jVLsKAXeUqW1WIEWKa05SqeNUj2pKECYTqeVSu1VKrVzYH748uzmg2DUrbEn0NbWltN7BLKtwugCi3PmzIna3LlzoxCju0lLMT8/AAAAAAAAzATGlbaKL5KJLzrsuSj0GOwdUeEx03ZL6b2uRl1J+gyUVijZ3kwbkDo4Rzad29CgWMNmpXf/9ThBymFBR1eZdSj06FJPmWYGplEba/mwZa6lDiyVtCi3Pjfm73pschZXbByPX5Xf7aXLtGJjQXnSnp+GA0HCwUBhJkDogqN1J+fvxojJvXbg++z8Hmf3z/Rg2HF4/zOByYHlg8tGPD4UqIyWx4b9CYiZTJgymh8IVkbNZNYbtm70nM93LwAAAAAATHexcg7XWTvxBZXTTht518ZCO+aYY9TU1KSDBw/O6mCjG6S7fHluA+AnywUpJwo2unPmiSee0Pnnnz+lfXDeAZiNbGhlg8wdIl1o0E3D9OBjm5kOPR72XNqOsf6w59zyocfDXz/wOjffP7BOykbzQ6HFgddNVdUKq+YLe9zXtZm0o3V3kh3csKvKlplae+j5aJCDHb780HqZlswMlHDrR3fTzayfbnuPpBWaTcL+KoWpmLx4Dj+kLMUaDmjWMYEqF29V0DP1YKP7z5kwNEqHnoLAUzpwU5OZhoOP3dToQPMmtc+tU+gFCr10NA3c1AQjlrnpoeWDyzLzer5e2nKh0oFVOh1ElRGl/QPtybwfIqAcAoyD4cXhzVV2pwojAAAAAABAkUKPsYUysYWHPRd93xu2ybpqj8GeqLqjm0aP03uiQKQ0uRunAdNVmIqrZuWTSu6fp9SBFimc/DChePO+8bYu2b5MGzDWaIvJRqmSre/JOdio1D8qtXtfJgUVBSfdDVEHU1ExmWHzQ8vlZ54bWpZ5Xf+u4yQVd3xMufMq8xsmG6xgOKuE0q5vjf1vkV8vrfla/so5uu/hC2FwDEDYM2LpWGuqYLxhAcjhoceY0fKP+Ko6qvDf2bgbMffvtFEoM+qDP9AG/vRELVpOCBMAAAAAgGkVbHThtGysXr1axXbiiSfq/vvvP+I6ruqfq/7nBrZOJy44t23btgnXO+mkk4rSn6nsM5dgI+cdUBoHfx0ocNkdf9SFXc+MsezQ40PPmeiC9UQmukwc5ZptJuiXmWa+UIjm7bD5UdPoO7uxpoPzo7bnQoSJRUa1JxYn+LDt82l1bwij/Q8FGAe/ZEjNzO/uw+7tSu3646LsK+jO35dK00nQXSev8ciB+3yoaHLBuNKzg/8z4aHmhbIKFQ5Mo8fGRsE/G4X/wsy8C/+ZkfOZ5weWe279Q897DftUs+ZRbdpSrXDzcVEQMQhNFFR0YcTM/LBlA/NumnmcWW/iv3qD9kvH/TK3A+T+jhy6ATQwY8Tj8XEDjFRgBAAAAAAAKE/RdRu/ScZvkrTysOet+7IkODAs7Lh7oPpjJgDpnst8KQRMf4l5rVr+gU9F89bdELG9SamDLZl24PBp2F992DbiTeMFGwsj3dGY8zb8qhdkU+3jPj+ZmFWy9X0EG0fxEv2yURrRfVnvvpPyDk0H2mSuoc/KYOMRuMqE+VSoYGNZcGMgklJwWKFmq/13hKqYZzOn5+DYEnd6+lLjWZ78mvx8z9O/y+r5j2ZxQ2AzGHA8lKseOW+O/LxnRo6TGXhfg7+Gg+tm3q8Z8X5Hv/9Dj83Y2xtz+wP9G9iGXyP5eQ45AwAAAABmp7INNm7atCmr9VasKH6FpOOOO27CgJmzefPmolf2m+nHPZ/vIZfXct4B+bX7h6H6thbwLn1lqPnCvao6aksmDRQlHt3UJYMG0ofDHkdVAqMv0AeXjfGaaD4dhatGr9P70u+of9dSzba7zxaLiR/2LcmMMBjkUxTCGwz1ZYJ9MladBxoVr+iOvgSPsrvWDLXhj8eaD4fWHf4484X60PzAOr1hu/YvWH+oH1EAcFi4cKCFQ8+7kOChAOLodcd/7eg2GFAc2JZX5EEsL84v7v6AWcxVWHRBxcbGxqhK+vBWX19PgBEAAAAAAGCGMW6UfmyuTGyulDhpnODjQdlgX9Q0MLXpQ/MKO0rSdyAXxrOKNx2ImvTCYc9HxU57q4eFHecqdXCOao57ZnoFG00ovzZ/v6MVc3arcvkmpduaM32zxbl5bVkL7lBq1zcmWGl44NEff954Snf8haQ5Rep8+TOxIPNvTnSMzEBzx2pwfvDYDs5npuN9nzGjg41HsO+28b/frTnBBRvzs59oOEdWK0o2lWnjrjDRBspI04WeGs70Dss2u9PQ3ei7Ym7+vl/b8k8phX2j9jNw6kdTNzv4p2UwsDl63YHpUAY7eq0Zuc7wrLYZ/7VH2v5hrzUjf1VHzrv/G/aaYfNeQorV8x0lAAAAgNmhbIONrvJcPsNu+ZTtPt17mG4Bs5ly3Gf7+59u5x0wW256G4XEBuZSvU8rue9/MhevR6xkDq01MGPd1cuh+bHWOXQx046xXvv+CoUVnVFAa2RPXGhtjP4NrHfo8ajXRJse3msX/Do0P2JrA68dWjLGtjJvb+T2DlsWBevG2tbIIzvYd6+qU3bbnBHhuczxGXg87PgMXzY47zZ+KFx36Gcwcv3MtL3paaVM61D4L3M87Kj3MRAKHHovdpzp4PEcfM3g+xtr/ZHbPvRzGf764fseFkwcNj+4XCMeu/CijuyhhZJcK4Jj7inOfgDMWO6LwSOFF124EQAAAAAAADgUfGyRibWMu461/VJ6/zjhR7d8v2R7itpvIFdRsdPqHvnV21S5eFvJ+pFrsNGv6YxCnPky99U/ippjA1/p9kal2uZk2sE56nxqnXpfPEGziZdwCaOJuO8k3U15hy0a58cSdFflq2szg2lVqvX/m8oLhwUfB+aNUWrvtZKuKUBHp6+w60dKt/eOOm46bJoJiw5fNvxL9MzjIPqbda5mm4O/CqM2loU37NfcqwYD5oNpviMf65HrDV8udT01T0H3ZL7LK68Q6FTEGq1qToxGqhw6bAOHqHKZNO+a4e9xjOM2tGjsYzr88Y6vhGr7zRgBzIEW9mbuqz582eDLRywb43G0mi9VtAzr10Cec8R6YywbCmsPBj6HP6+xl7kwabrDKugf+dxh+3M3pL/Ek+ePPl6Z+bqTXdXTPFV1bbXqeDQzBmesYxj2uH8H7ZGP4xi/RsMfxxqNvPjgGx3x4z3812rU2xrxZ274sRi+uWHr2LRV0DvqdcN+PQfDxi7gfFg4d+A1XoUy/c2Tvu3Dx7eNeh/u78Hgn6rhzw/vsxnn+WHHMAo0DzfquI35Z274NoefamPt77B+j9ruaF5+j+GR2MHBiMNwc2gAAJBv0zrYWFFRofnzi1/NZsmSJVmt9+KLL2q6yTbYt3Rp8St/LViwQLFYTOl0umDHnfMOKL6g52H9uvF5hU0uUDVBcO2wYJ7GCeKN3NLIkNxgAOzQ/NA2Ry0bM4g3eKFm1PaOGMQb77P8jWerKFZ/T7PR+t+sLM6OarZlGgCgpNxnBRdSdAFG11xocTDI6B77vrtVKwAAAAAAAJA7YxJSfJFMfNG461hXWih0lR8PRC0TeDwoDTzOLHOV88Yt3QTMOm7cdK7Bxlhd4SqqGj9QvHl/1AYFXfWzL9hYkU2wMTs2NAr7qvO2vZnAxKb678LAzXeH31na3bA3yfcjo9m+2xV2Hvo9zkW644RZGWw8krDnFqX33pa37dn0VyTNrr8T6Taj9t+MPdioZuWTajrzU3nbV2rv+5Q+eIEKqf/lqYRNCxtQ7Xg4GPe5E/7lffISyXGeHf5zsbrklOF/s42074dKDlulc/1a7fjyB3Lv8Ayy6IZvqvHsBwYeTTUgd+h1G/7o85p1TBj9d+mhx4dma1c/p6W/O3ZlbTOF473tv39HnevXFPZ31zv03y5mRLGFsU6R4eMzJ9j3YUFRqzAdk4IJwvJGije2D3s8crsr/uZf5MWzLdl8ZB1PnKSd3379uM/bVFxhMp79Bsf4Efs1PTLewPkyInhrj/zS0T+LLJ4LU3EFXRP/mx2r65SXGPu/eee//meqP+W58Xo1KTbwtOlvP3rE51PtdTntw6/ql1/dO9meTX5HA8c8tb9pcPCzluvSEats+Jb7OffIq+xXrL5zzONXs/JFLXzzzcqXXd+7Wt3PHzvu86mDDbLpqX8eMX6o+JyDU3jhZF+QOb7p9jqF/YkJ103M3zfus0d//IvyYuP/d8ZkdD59vPbcdNkY+6hVYtH4N2QDpqOyDTZu3bp1wnXmzZtXkjs/uIBdvt5Ducm2z9keg3xyVVTmzp2rXbt2Fey4c94BxWeUUkfNXoWDH54AAADG4IKJLqA4GF4cHmJ089XV1dwZEAAAAAAAAGXDeJWSt1AmtvDI1S9s11DIcfjUhu1S0CYbtEUBSbkqkcAMZ9MxNZx5r9IdTVFlRBdyTHc2Sjb7Sl1+3bBByEUQdNfmvI35r/9qVHon3e7ed1P0vlPtzUp3NijsyX37panYmJ2gtyZv25opjJ+fgfKDwuREA3NnIX/sSoNTEhAcHc0MC6jkg53EvwGzwpGCJlMRcnwP4yqvZ/Hf3u6b6YrRI7Dd64b/iNzNTjCS7Ypu+pKXTU3/Aq1TYz3Z9Ni/uzaVlNIvj/3clPY12cDWFMZshIf+LS3kjzTrbVsXxhr/Zis2tU02TzcoCvsWKeiqLeibDDrL7/OE+6w3nrC7Qzb1Ul72YwNfyb1zVEhBV0xBV3l8phn88QfdMQXdY/cpPme7bCp/BZRSB2Lq31W4YlE2LfXvLH5mZSJ9L49/ozOlNsva/IyHDzoXqG/74fsKU+5GacDMUpbBxu7ubvX29pZluG4y+927d6+mm2z7XMpjP1GwcfD8qaqqmtS2Oe+AEvGqCn2TLQAAMA3E43HV1dVFbXhgcXBaU1NDcBEAAAAAAAAzSnS9y9TJeHVSfPkR17VhrxS2D1R9bJMNXejx4GHL3FTKbygGKBZX+WTRW790WEU/VxUxCvsNBB6jx50NURs9H6stdrAxtwofTsO63yhW6ypqHM5Vt8i8PxfybFC6o0HBwPsdet+D0+56FYNXOdkB7oUNhs40JkawsdCMyV/wzgWyMUoej2+E4F1Bg412oOoTCnSMOb5j4PgCAABgpLL8ZL1/f3Z342hpKU0JVVc1MJ/vo5zMpGO/ZMmSWfvegWnFuNLzXGQAAGAmSyQSUWCxtrZ2KLw4+rFbBwAAAAAAAMDYjLtZqFclEzvyDXEHq0Aq6JANOwaCj246+Lhj4HG7bNgZrScli/Y+gMkynlWsvj1qldp6xHVdCNKm4yqmdB6CjX5V97jPmVigeNOBqGVTkSQdhUCHhx3rlO6qi0Kfbr5v11Kl9i0om4qNIRUbD0OwsQj8/FRQGfy9Q4ErNhJsHMHku2IjwbAxELwDUCz8jQAAoGyDjQcOZFce1Q1+LYVs95vt+ygnM+nYTzbYOJPeOzCtuC9hAQDAtOR5XlRJcXgbK7hYUVFR6q4CAAAAAAAAs6oKpLw6GS2ecP1MELLvUPDxsEBkp2zYPiwQ2SHZnqK8F2AqIUhTkZxWFRu9RI+Mn58QkPEDxRsORm08+++6Srt/8vac9uNV9Ctfgh6CjaN5sVRet2eTfEczmvHyGGwMCTYWvGKjJdg4Ur6DjRzfQqIi5hjyHc4Fio5zGACAfJvWwcb6+nqVghuc6wbwhmE44wJm2fTZ931VV7sKa8WX7c98Ksee8w4oDWOqZbjzDAAAZcVVTxwdWBxs7r9L3ecBN62srMwMlAIAAAAAAAAwjYOQA9UgNT+r11ibGgg8dkphV9RsNHXLugeWuXk37R56jkAkZqL5135T6bamKOCYabVRFcdovqdGYW+1gt5a2dTY4TKvuri/F0Fv7uN98lmxMeipzdu2ZgqT52BjmKzM6/ZmgnxWFLTpshx+OXOOr8uOELwbKc9fzRK8G0NeDwnHt6A4fwEAAGaEsvxk3dOT3UU7N5C2VNy+Ozo68vI+ykk2fS7lcc+2auFUjj3n3dS4oGV/f/7uxoeZI5lMZrVM3L0OAICCczfIqKqqigKJbjpec8+78GIslt1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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "(SEIIRD.E/N).plot(color=colors[4], linestyle='-', label='Exposed', ax=ax)\n", "(SEIIRD.Ia/N).plot(color=colors[1], linestyle=':', label='Asymptomatic', ax=ax)\n", "(SEIIRD.Is/N).plot(color=colors[1], linestyle='--', label='Symptomatic', ax=ax)\n", "(SEIIRD.D/N).plot(color=colors[7], linestyle='-', label='Dead', ax=ax)\n", "\n", "ax.legend()\n", "ax.set_xlabel('Time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"Data \n", "
" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.2" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "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": 4 }