fonnesbeck/Non-Spatial Measles Model.ipynb

## Non-Spatial Measles Model.ipynb
{
  "cells": [
    {
      "metadata": {
        "toc": "true"
      },
      "cell_type": "markdown",
      "source": "# Table of Contents\n <p><div class=\"lev1\"><a href=\"#Disease-Outbreak-Response-Decision-making-Under-Uncertainty:-A-retrospective-analysis-of-measles-in-Sao-Paulo\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo</a></div><div class=\"lev3\"><a href=\"#Vaccination-Data\"><span class=\"toc-item-num\">1.0.1&nbsp;&nbsp;</span>Vaccination Data</a></div><div class=\"lev2\"><a href=\"#Compilation-of-cases-into-2-week-intervals-by-age-class\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Compilation of cases into 2-week intervals by age class</a></div><div class=\"lev2\"><a href=\"#Cleanup-of-Sao-Paulo-population-data\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Cleanup of Sao Paulo population data</a></div><div class=\"lev2\"><a href=\"#Stochastic-Disease-Transmission-Model\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Stochastic Disease Transmission Model</a></div><div class=\"lev2\"><a href=\"#Model-execution\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Model execution</a></div><div class=\"lev2\"><a href=\"#Summary-of-model-output\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Summary of model output</a></div><div class=\"lev3\"><a href=\"#Uncertainty-in-susceptibility\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Uncertainty in susceptibility</a></div><div class=\"lev3\"><a href=\"#Vaccination-coverage-by-strategy\"><span class=\"toc-item-num\">1.5.2&nbsp;&nbsp;</span>Vaccination coverage by strategy</a></div><div class=\"lev2\"><a href=\"#Removing-augmentation-of-migrant-population\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Removing augmentation of migrant population</a></div>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "!pip install --user engarde",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Collecting engarde\n  Using cached engarde-0.3.1-py2.py3-none-any.whl\nRequirement already satisfied (use --upgrade to upgrade): numpy in /usr/local/lib/python3.4/dist-packages (from engarde)\nRequirement already satisfied (use --upgrade to upgrade): pandas in /tmp/src/pandas (from engarde)\nRequirement already satisfied (use --upgrade to upgrade): python-dateutil>=2 in /usr/local/lib/python3.4/dist-packages (from pandas->engarde)\nRequirement already satisfied (use --upgrade to upgrade): pytz>=2011k in /usr/local/lib/python3.4/dist-packages (from pandas->engarde)\nRequirement already satisfied (use --upgrade to upgrade): six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2->pandas->engarde)\nInstalling collected packages: engarde\nSuccessfully installed engarde-0.3.1\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport pandas as pd\nimport numpy as np\nimport numpy.ma as ma\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_context(\"notebook\")\nimport engarde.checks as check\n\nnp.random.seed(20090425)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "data_dir = \"data/\"",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Import outbreak data"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0, encoding='latin-1')\nmeasles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\nmeasles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\nmeasles_data.ONSET = pd.to_datetime(measles_data.ONSET)",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "measles_data = (measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})\n                    .drop('AGE', axis=1))",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Sao Paulo population by district"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "_names = sp_pop.index.values\n_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\nsp_pop.set_index(_names, inplace = True)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "sp_pop.head(3)",
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "data": {
            "text/plain": "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\nAGUA RASA                5411        5750          6450          7122   \nALTO DE PINHEIROS        2070        2369          2953          3661   \nANHANGUERA               3068        3006          2755          2431   \n\n                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\nAGUA RASA                  7621          7340          6999          6984   \nALTO DE PINHEIROS          4612          4190          3539          3633   \nANHANGUERA                 2426          2636          2695          2308   \n\n                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\nAGUA RASA                  6346          5608          4987          4212   \nALTO DE PINHEIROS          3448          3289          3040          2533   \nANHANGUERA                 1653          1107           753           509   \n\n                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\nAGUA RASA                  4152          3595          2937         3637   \nALTO DE PINHEIROS          2298          1732          1305         1823   \nANHANGUERA                  352           217           162          171   \n\n                   Total  \nAGUA RASA          89151  \nALTO DE PINHEIROS  46495  \nANHANGUERA         26249  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0 a 4 anos</th>\n      <th>5 a 9 anos</th>\n      <th>10 a 14 anos</th>\n      <th>15 a 19 anos</th>\n      <th>20 a 24 anos</th>\n      <th>25 a 29 anos</th>\n      <th>30 a 34 anos</th>\n      <th>35 a 39 anos</th>\n      <th>40 a 44 anos</th>\n      <th>45 a 49 anos</th>\n      <th>50 a 54 anos</th>\n      <th>55 a 59 anos</th>\n      <th>60 a 64 anos</th>\n      <th>65 a 69 anos</th>\n      <th>70 a 74 anos</th>\n      <th>75 anos e +</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AGUA RASA</th>\n      <td>5411</td>\n      <td>5750</td>\n      <td>6450</td>\n      <td>7122</td>\n      <td>7621</td>\n      <td>7340</td>\n      <td>6999</td>\n      <td>6984</td>\n      <td>6346</td>\n      <td>5608</td>\n      <td>4987</td>\n      <td>4212</td>\n      <td>4152</td>\n      <td>3595</td>\n      <td>2937</td>\n      <td>3637</td>\n      <td>89151</td>\n    </tr>\n    <tr>\n      <th>ALTO DE PINHEIROS</th>\n      <td>2070</td>\n      <td>2369</td>\n      <td>2953</td>\n      <td>3661</td>\n      <td>4612</td>\n      <td>4190</td>\n      <td>3539</td>\n      <td>3633</td>\n      <td>3448</td>\n      <td>3289</td>\n      <td>3040</td>\n      <td>2533</td>\n      <td>2298</td>\n      <td>1732</td>\n      <td>1305</td>\n      <td>1823</td>\n      <td>46495</td>\n    </tr>\n    <tr>\n      <th>ANHANGUERA</th>\n      <td>3068</td>\n      <td>3006</td>\n      <td>2755</td>\n      <td>2431</td>\n      <td>2426</td>\n      <td>2636</td>\n      <td>2695</td>\n      <td>2308</td>\n      <td>1653</td>\n      <td>1107</td>\n      <td>753</td>\n      <td>509</td>\n      <td>352</td>\n      <td>217</td>\n      <td>162</td>\n      <td>171</td>\n      <td>26249</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Plot of cumulative cases by district"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\nmeasles_onset_dist.cumsum().plot(legend=False, grid=False)",
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9cff1b978>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9cff1b160>",
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ELFy7ilUZoJB6hUrurWV/v8cXwx85O+elTyahLYQQZ0gpRX7oOQBqlT5QDo1r7kT3Ble4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\nM6quS2r88BMzV6JiL248+kyUbYd0tY4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h/abkcqGGI0iKihoJI7tm/MuSqiC7ZwL8avwu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q64BkXHJl+xyVUsAvX2giqAaMgjtsbgwVdyTdf5ucdJhF0SkbVqjl9Ca9BLZOfbrHtP5rT7+/vX\nAbuA14C2wcHBGagJe39//5l2P13Aqyt2m1haVqdOnX8hvOo82bEfABBJbcUMNaxaX5jfA0CieTfH\nsscp+xU+1H0rc3//ZfxshvTHPkF43frLPv83Bh/mhYlXADBkKxua7uZz/RuxDckXBr7LwPwAhtHO\nnet+cVmwv/PqEG+USjRf1wZScIvhsuGfvgSmhapUkEBq1udo2w3MRTeBqL3WCg2zJNw0lGuvwOs+\nuJEfCZe5qksrkqkXRukOBJVonm33dfAb3b9A6fXXyH3jJUannkDEJGJ7kpGe9exPXslYuQmhWgl3\nR0gKgfR9rnzrZZpmZ5gX68ibbaQ7Y9y0o41w3GdiboK/efIU0+l21K47Vj2HNrdK28AEvYdfxNAe\nYb9MxH+bXOD82Y9ifYxSbxPzpTi9shlzMUtRGZzMJynnTaZck3kM/AOnsM2AlrRDNGLh924GBOua\nclzRNk+recaCDminwDYKa55e6dWJSedmV50r4oYJhrna9nd8A6UFrmNgGYqwtUL0BJiGQmuN4Wts\n8+y6RguIndfI7LLRlQCd8y4+AO7HaOxfDu9atJdc49+iNkdd7O/vP/dR1N3fder8BKCUx9zph0Ar\nhAzR1HvfqvWBV6S8eAQz3Ewovo6D498CoPetUUoDB4leuZ2mj953oUNfFIfmj/LCxCtI2UAsdD13\n917NLR2NmFIyUpjh4NzrSJHgF/t/hRvbmgH43p4RXverRDpjpCyDuw2X6P/3l7WXiu/hScHrW3ZQ\nDbYjtYVnV1lID9Md6iY5UrMXWjuThDvjfN8vUw4U6w2TE08OsQ6Bb7jcev96ti76jPxff4KxMYy8\nLkK4ZR1HF9I8dnQD+bdCmHGLpl3NyJBJaGGRzulRpA44vWknmWKe9g1NrO9MUEhKnprPskAMImmI\nQHO1RNwIkz52jPDYKPHFLE2L01jKXe5MFUiDmXQrImWSjLhYUmGbNZd33osiKx52voybDDPtpRjL\ndTAhmym6Np7VScGx2NKywLU90+yKlTGExjDUmtZmoASuLzENdVFiKEUteE4rgUBxbpUVrUArXYsG\nD3Rt7tzXqIkKarK6vFwCtckGgS/N88Rf5x3wLq3b2M8kv7X2qncl2v39/SY1wf6HwcHB7y0tnunv\n728bHByc6e/vb+fsrP0E0LNi9+6lZXXq1Pkxo7UiM/xdfKcWfJZsuwnDXJ33UszsA61INO9Go2tR\n3Z4g/vQb2F3ddPz27yIushPRuUyVZvjHow8BkmT0g/zmth2sT5w9/3898n1AcWPnh7i5vYVs1WNw\nschrfhUjbHJbCLa++TzVV2qNSxSSI50NnO64mniuBwyfya7DLLSMcVPubvInBeGoxYfu28bLyuHN\nbBFDKfqlwYEnT7MJiZYBN6ZPkH7+WeabBaHPdCw9K3hhZAvPDLYSSdusWw9OezNaSrr27afUnGZo\n61WEXIXMOszc3MGkIQAPimCYNuumR7myOUVPWyvVl/bhv/wjzOAcky1iQHMUc1OU0KY4fXbt2Xo6\nhovFad2O8lvpoYofzCFkFs81yM43YvsG26wsthHQGK3SkSwSsd5Z7PRS9TGJruVSOwrlLAmwAr3g\n1QLYqrWgNZWvRZTrBRcKlxnJHQ4t98IOtF6qQCpQF7DnKvEmionUqvStlUghli1+S0pMKbhUA1yH\nQhjtHUjTJGYaWO8wajGlIGaa75ml/255t5b2V4Ajg4ODf7Vi2feBzwH/D/CrwPdWLP9v/f39f0HN\nLb4JeONdnr9OnTrvgNaK7OgPqOSOIYwwOqgSbdx+3jbF+bcQ0iLWtJNTi8MUvRJXjlSI79hJ+2/+\nFkb0HepIrsFYYZK/3v9Fil6JcOhm7u7ZvEqw986OMFs6Qchs5dMbr+PFqQWeGJ+HIKBv+DhXHd1P\n8+TpZS/lSLiVY33r8Oxu4rkkoTTs732OzS193DD+acZOFki3xPjIAzvYVypzcKxIR8igf/QV/HmP\nftGLEAE3XneAhoYSULsWy2gj0XUTj+yzeTWfo/XmJDJsUgUoVillXCau2gGmpHGqTMUQVDqixJRP\n1/QE5uw0G8ZP0755G+WRcfzxEcpODonGtAXGzhSyN4qIGmBLSiKOpyVzbowFJ41hp9DYZPMJ3OkJ\nNjVMsDE9Ssg8a9XGQtCWOL95yNnfIwjXgKpA6BA6X0Xly6h5DzVZRi1cXDlOYduYjU01C1uDH9Ho\n7hacaAxPaQKlqS7lj5/BUxpXKRSCXCqNa4dYaGwh09yxyn8elpKYZRAxJE0hC3kBgTaloCVsYy4p\nZcI0aIuGSFiXLlkVt0o2U8T3Lxxt/k5DnYALe8idisfUeI7hExl8/731Dvyvb7Pu3aR83Qx8Fhjo\n7+/fR80N/j9TE+tv9vf3/zowwlIR1cHBwSP9/f3fBI5Qm8H/3XrkeJ06P160DsgMP0x58TBWuA2v\nOosd7cQKNa3azimOEng5Yk278BB8fd8/ArC9kqLjd/8N0rq8SNnZ8hx/te8LVPwKkdAttCeu4n1t\nZ+fRA6X55ola5uc9fXfww/EswwOHuOuN52iemcBUZ1+GChgLd6C6O5G6n0jFoGNLlNcan8SSkuur\nd/DWkTHSrTHu/6VdHCtXeGJsngiK3oE9DE+2EwQmluWxa9NRGgwHy2sltfk2QvFe5ouCr377VY43\nNZK6ognTdeg5epgTCzHMK3qI9SVBacyix0JHbQDTNjXKBx/7BrbnIno3MmV24Lz4OLYfYC/VDRFX\nprBvbGAm18LcfBOzkwkm25vY0TZHT9yjwy1iZ+eQ2XGits/WUIC1vuYev1A5T10NUDkffIWQNtKx\n8I8u4k8uQHVt8ZDxONFtV2J3dS43HxGpRrxwnLwXUFQap6mVLCYFQ5Pzy2SLDm7RXXOSU2gwqz7C\nVxhCYLpVXDdb29z1YGaS2MzkuXdAIVRhNpbjRGjtAch7gTJ8vFD1nTe8XMLAVT+OA39izTXvJnr8\nZc5J71vBhy60cHBw8E+BP73cc9apU+fi0VovC3Yo1oM0o3jVGeLpa87b9kyhlUjjlXz1tS8yS5Fd\npzxu/MwfXrZgFz2frx59jIpfIRW9BWlu5YENbZhLLvaKX+FLhx6l6JwgbrcgK42EH/4qHzl5GIAZ\nu5GSEabVLfBm200kESxGO2qtHXVA+mafPfIpCk6eu5MfZt+TY0TjNj13rOMrp6YYrzhIPyCxL8Nk\nvhPLqKLTE9x7/520t90J1KqZvTk4y3P7jnJqMk9sXZJET4J4dp6Nr7yGe9PtNG+MsmiLWjSWFPhx\ni/bpMTYd2Ue/k8e68z4mX3mLalYRToxiBDXhNHamMLYnIWmx7/AWTsy0E2+HO288ih3sqz2kpcDk\n5Oox1NLvDxwVw51KkCqV8LNZ3GOTUDpfmJVhUG1rIgjbVEKSuZiiQJVMg0Wmobas1iGrhNbHaylg\nSiOUZlVJtjnQQhNYKyKm38nBEn+nb8KFMTyLaDWOuMQiJxeFANAYgU3cTWKcySm7GJbi5zQaKTWG\nvEDKm9CIwEJUovDjuP63oV4RrU6dn0FU4FCY20N58TB2tAsr3Eoxs5dQrIdYenWDD60CyotHkWac\nxw69xAFnhI55j8/c9lvYbW2XfQ1fP3Gc4dxhpGzENLdyb28rvfEIXuDx5MizPDX6PJ7yMGSKB1s/\ngvuF/0wql2UmnObZxqt5vzvJSHQTp+xGpBAsAE68QC45zcYtrbzu7MUvaz4Yu4fSmzG0LrN4RSOP\nzC2C1oSyDs1DGVp0hkokQ/kWm1/b8UvkCwF/9dABhqcL5EvushHZGa+gNvagKy6VcAMH7r131f0I\nKbj5+D463niRhAowUinc+XmGckcZS13N1twemqbGIGKwcM0WMolW5mdSqPk2rrppHbcnx1iceBIR\nLAmybxBeMQ/taRtHtWDm83jjWYyRReTsMFZFLeuqDkumeyKMNEiKEQkCPEMw3Gnj2kvz6gAawn4U\nqQVCKsw1XMMYgBAIUUvnOtPtOuUkSRQboZSEcpS3VTwNVCPgnS3Qo4RCn1P8RMuAaqSIPiOCa4id\nb7m4oTLvVQzzj9HOvmg0Gv0e9Q0XazU8/0lhbq7wk32Bder8hFHODZIZ/g5aeQgjjJQ2gZfHtBtp\n2fggVji9avtS9iCZkYeZCq/na1MDpIoBf7Dxs7Rceb5FfrEcyGT48sB/JVAz3NX3CT687gZChkRr\nzZcP/SP75gaQIkq6so5PHJsjfPQIQmv2duzg2ehOrnence1OhBBIGaC2LTBoDuCHqtzSdSOvTe8l\nNd5N29jZPt6Vzgjz25pITuSJDZfZ1DxENpNlX4OipaWfhbFGylWfYuVsQJUAOsIe/dYkp3dcg2OH\na354U4LWSFeRtk16hUPnS0+RPrwfTBP82jHmot0oadJSHEWiEB1hXrv+g6S6d3FLZwa/OIxTnkD5\nJc7F92FhysXwXULTDsahPLirX3dB3CDXESLTHWbOhL1NBg2WpM0wSCzN9xoCmg2JtSSsEa1prmrs\nM3PFmlqKU+XCrnN/wiWfCVOWCTxhM2OvY9FqveC2AEoE+NbZWV4tNNpcwItkcMIuru2x0JRDy/qr\n+3L55i/+7ZqjpLqlXafOzxDVwjDzpx9CSIN483WUF48QeHmSbe8n1X4rQq7+k9dakZt+kQUFD00e\nxVCaB41rLluwtdZ8d/gEz458G6UWuDJ9FfdtuGG5Vvdz4y+zb24Aw2jj2tEubnj5aaTvM9/czoH2\nq5gpt3G1MPBCXSg0VvcYR7uO4WmPnngnqVCSFyZepTHbSdvYVsLCo1kscqyzh4X+JhJDBRqGcmxa\nf4o37HnchvV0VbYwcHCRlTZXS0OYBz+0mfGFSV4NQhw1NtTyloQECSLQtOyd49YNYVoOPkHlxPGz\n9+j7LJhxhhqu4Or5PUg0bipK9KoICzu3c2eDgZv7JoXJMz2tWe56FVQU+nQRPV0lOFUi4S0JmwDd\nEMLpi1JqtJDNBrQYxKImHULQAXi+wY2+JLvQSNUxMYWFWVToqRxlFUaXXESgKLseoxoCaVIMNVGw\nGwnk6imOcrRIKZ6jmMpR3lLCt13OdgA5gNAKoQNquqtYafW6tkS/Uyi1AqGWjOmfkKjr9wKtBQQX\n0cXr3P18C+28N670umjXqfMzgtaaxalnAEXLhl+utd70SyRab6Kh844L7lNeOEypOs+3ij4V4XPn\ngMcVv/vJy76GHwyf5Jmhf0DjsLPlen5z+yeXBfvZsVf41onvYwU29+7T9B59HCcU5pVb72VxsZlk\n3qdNgrAEc5Qpdh2l2DpNxAjzuW2/xNzsMA/PvEBbLkLbiSsQ2qMnGOCN7R+g2BUn5DmY2SHekjH2\neS569Hp8XwCLAGzra+T6ba30tSfojAm++dKbHGnuJlrJYfo++VQTZsmj+eQMW4YO0VsZwdw3v6qR\nVdEI80zzTdyUP8quzJsICcY9HST6wiizgTaxgLs4RsUzKDlh4iGPsBUQZFyC17Ko0TK6J4a1LoLV\nHUE02cyrDsaCHmy/SEqUqFRDBI5B5biNX9ToksLxQ5SsFBq5HImt0ShZGxhoU+G0F6hE8wSmh2+6\nOJEiMAVMItBLjm/w7Cpe+FKcxpeW5qfdEFoZZz87Ec5Xbo2wNMJYaY3L2nYrarLolfVZliqrcfa/\nS6veZmSgJKoURasVUqcMdCWO9kUtt/yiEbV70ZeX9vheURftOnV+RnBKo7ilccLJzUgzSn76RYQM\nkWy7+YLb16zsF3i27JINPK45UuKDt34OGb68voU/GjvME8PfRuPwiU0f50O971teN5gd5VsnvkdD\nweRjr1RoyIyTaeng+VvvhwGHlPKRIYO81JxqPIrRXWsbelPHddwTu5oT3/s2j3Zn6ZjaSMdkJ65h\nI9YLXmj/MH7MIuo7jB/I4gkX4h56bNvya1xKwWfu2MQHr+2mEiieOXSCHwyPMdm1jkQuiwgUeAFX\nPf0GaadE3/QeRHDWhT4ZauZgchOzoTQJK8ld4z8k5uUIGiLY70tibekikd7B/JMkac8AACAASURB\nVNTrKCrkKjapiEvECtBlH/9gAfdQEas3jP2xThYibUxMtlBYjFGdDlGthqiJztmmJitxI2XyDTP4\n4ZNUomUQAQhB1XYJrLdvmvG2KAtVjaMKCfQ7lAYFkKasteQEdGChix0Q1GqXC1MibQO0UbNEfYUw\nJYZ19rhaaZQT1HpZewrlqeXly+v+O3rUYxFjVfqZEGCakkiotjwSlvjaJdAKX/mU/QpO4K4qDyek\nRloBP84bqYt2nTo/A2ityU09C0CscTuzJ76KCio09d6HYV44/Le8cIjh0iwHXI/0gs9He+64rP7Y\nOafAV498h8GFWtT3bd0fXBbsil/hlcm9/GDoBTaMVfnw6xUs1+XYFdew95oP0PhGlpACQrDHL2Ju\nPoCRzNJRtPjshntJ5wWPPfXX7O/opvf4+4iUU7gGlFsjZDY0ooGmTJ6jBxaW3pNnA+ekFFzb38Id\n1/XgRwyensjwxtgMJdOGrnXECotctecFNgwdx1Rny2Zq4M3UVo7HenGliSMsWoMy7yuP01caxPKq\nGDuShG9pxohvJNryPkYGv0HSK6NCBqmIiztSgUM5ghJUwnEWd1/LVKGDypEQTnC2AYmhPFLeHJbv\nYPsV4m6WmLuI0Irh9gSHNkiyrbllY7dmeYrl6OjzSouesXB9G7XQhfZDyyt1UBMSHZiohVZQNZe5\nETGwEjZmzEIbGiu6SNguILRA6QBfV0AL/IqFEUhswyBQkiqKaiVABwLPPfPkLqEAiwiWrNbLdRlr\npO0grbXrjAoZYMYKCMPHFSuuTWhEuIgwa54KGSqjbIdzh0AeF2wZvowZgLGWy1uz1Ez8cvjNtc95\nmUesU6fOTxCVxaM4xVHCiY0sTj1bE+yee4mfEyl+Brc8TXbshxx2ay+t209A67+79BKlvvL5T3u/\nRLY6hSFbuWf9R7mnbxsApxaH+eLA1yh4Ra44VeFDrxcITIsXbr+f40Y7idczhLTAUWUOBhDd9ho6\nWuW2EZtdr00xG/0SD10bx0nsovdUrd55qSNKqTNMZKSE/eYUs07AjAM1VauJUmPc5p4b+7jxynam\nXY9/Pj1FeSl6WkiTnXtfZP3JwyRzWaTWlKwkmWgX6dIYs3YDe1NbOZzcSLc3xw3OKC2leZoL07Ub\ntgRc1cDEVdvxMoLW4jDe8BFSKQsRM9HVAPepedRYGbWpkVdjH6LqRGG8truhPJoro7QVhkk6c8Sj\nIO0ImfY4BxtCvCoiFEI2KrmIDJ1fC1wIasnRgHLCeJMb0W4cgUC7IYSWxPwqrdVczX0sDDxh4wgb\nV1oowySQAU64iudqdGASVBVB1YO5M49QAA3nnfvC6Iv4vIYVr2sDDLsRjHOzCiWYUYmwYhgRGyN0\n/jFk2EBeKJH9Yi71MlnusqYvZajx3k7q10W7Tp2fclTgsDDxJCBQyiFwF0m130a8+cLBZL6bZ+70\nP6ECh5Mll7Cv2HHjvcilNo2Xwn8Z+C7Z6hQxewu/s+NBNiRrVr3Sin84+i3K1SIfOBRl5+FZqqEI\nT9/1KU7OhIhkKzRjIAzFABbJ/hfxIhUeGG+l49WjHNsY46Vt7cQX2mmZWY8bleQ3p4iPlml4a55j\npofjnXnTK0BiSMEDt2/mjmu7CDS8ML3AM5NZ0JptA2/QNX6a9Nw0GkHOamc2vQFP2PQtHKK1OMxg\nrJfHW2+k3cmyuzrMjZkDxCu52jNrSRDZEUZsiHFgro0tkVkii0W0o5GdEXTZxztUwCGEm2pjymli\n1OlHSYu+hQFSlRliXo5wX5yRu+7iLdnPvG/gBB6uPo3PQRBnq5UtS5GWyKANU9sEgUekqOmbyhP2\nApKFKglnPwuJbg4a66lKv2bcBZpZI4mPSVmsSKJeNoQtcMNIs4RpVtFaorWBUnZtzvwdREYIjWUq\npKx9Xj68MFBy9f5aGgjbwIyaq6qiGbbETNhYCRtprSXqGlmsIDwfztR3WRkAV1yjy5YhQcpVJU+h\n5nnRUq5ZIrV2D6CEvMAjOCfPe+nzu21PeznURbtOnZ9yFiefJvDyhBLrcQpDRJJbSLbfesFttVbM\nnf5nAq/A9ClFMQ07vTRNt91+SedUWvO3A09yNLMHQzbye7seoCdeE+y86/Ptk68gxif4lZcrpIqz\n5BrSPHbtRxk9bRBzHbbaNr7rc8QPCK87TNwv8okXITF+mGPbmjiUupr1R2qtCqoSRiMG1YMZtFzK\niloSbNOEiB2iWPH4gwd2ccW6Jl6aXuDxsfmaq1Nrth5+kxtefRrPtBi89hZykymqoRS7Jp6gyZkj\nG0tyOLGLzXOn+P2hb666T2NnEvO6RsIhg/limKjw2JmYRA0UEVsTyLhJdVoxsH8Tc3Sjz0RKRcDC\nocc6Smx9gZM9/cyl25hxypTmj6NkFhkqg+EjjKDWiEOADMKEtUnbTIl0XlOaayWvGpi1GymaSYrU\nDOIz52A5/EDDGffvmeBmoRGhHMJSGLaBsDU4Bn4uAoFA+bFV7mBhCOykjZmwsOI2RtjgXPWSYQMj\nbKwtVhewZlduaUmBsbSvFDUhfVuaEssfK77Cv4QU5ct1TJtCLJdP/UmkLtp16vwUU8wcoDi/FzPU\njFeeRhphmvruX/OlWsoewKtMI7MRDk8PQzrGddfde0kWg9Karxx9iyPzz2CICP9m56/TE08CMF91\n+fzBNyhlfsiDL+aIVzSHr7qe13p2I47leH+wQDXUjO8GDKPxE/NEGsb51OMFoiWPU7dt4YBuo3my\nh0IUpqIh8pkKOuMgZABmFWGKWvoMtVzngu/xqQ9sZGtvI//52UEmYwIhBFa5zF2Pfp2W7AwAx/qv\nZTbTjQ4JIo0Z3tp8K9F8jg373+KGyf0EwiAT6yTcJUh1OnihME5XDBeNHQSkYxV03kdVFebuRnSg\nGT3ayMDodjAFuhG0VXuOpYYoudYOjvqKqjMHcgDKAwCIFBgaDC+M4RuIABw7oKEsuOmlHKqSYjbU\nwdFEF/NncupFgGyYBqkQhCEwQVsQWBBIhAFgoRxYMT2PLtu16mcrfn/SkoiwJBQxMRM1cZaWJJQO\nI9ZwN0u9FNutl7zza5U1facvT6CXG4VcqqjaQFKt3SBEa03Z8cFVyDWKyYhAY3j6/N6iK5De6liB\nYsVjJlvmkgLN3y1371pzVV2069T5KUQpj/z0C+RnXkUaYSKpfgqzL5Nqv23NwDMVeCwMP1krb/rY\nIAN3NRE3Y1yZ7r+kc3/71H72z9R6cv/WVb/ClsZa8Nei4/G9xx6hf+hZtoxUSJQVe6/7AAM9V9Ny\nMEtMWlRkGlcpJlVAq5rEuGqE235UYDYpeOvD/bhT7YRn13FEKEploFzBRBNqHcG3Ncytw3GWioiE\nDD5560Z2bWomFbf5T88cYzFhYAY+73vuUfqGBzGXosAroSizzibagmFaS6cwZ6s07cks39Nsshd2\nt9K+KYdtBSgVIiIBPFTBo3rKQ7k+9pVxzJRFYdRgz/A1lKsRit0x8huSqCU3r9Ya25+ktPAaXngM\nrSXBfAehqqCvPEPaLaICyVCHSbbFBwFqvo2p4av4ToO5ajpZhhVGyET5BlS6CN6mtjiADBmEmi2M\niLmkoAIzamKYslay1BCYCQt1ppXm8hStxlvhbnYXHMrjRVAarTXa//kqlBKxPGyj9qxNU7CuLYTx\nE2J910W7Tp2fMrRWZIa+TSV/HGkmiDVtpzD3GkJaxFvWjv6e3/sQ2nLwB4rsu/MKXHOa+9bfgW1c\n/Fz248N7eG70IQA+uv4jbG/eBEB2dp7Bz/85t0xPARCYBoev3M3pxqvoPLiAgaAQuAwbJkknyycq\nA8z96m6mn3mDsaY2plLr0QPdjCsDv1b4mZgliScmyRgOpdk+zthxtiX59Ac2cvvV3QgBB0ez/MXh\nEVSylsN84wuPs+HUYQSgEMy0X8FUNM1m9w26xk9hqICqtJiMtOG2tNGyzadznYtpZpfvU6AJXECB\nkbCIXlFzxytXc/rVFANyOy1tJfo6pwi3dGKEBOFQnOeODzJY3QehMoRBlWO0nW5lc6aIDk3x6vUm\np+UZn7aPKqbwJjZAtRlhGOcFG6uqRFUVwhAIKbAbQghDYIRNpC1RgcaIGJgRczmVKqj6tXSqJYu4\nPF7EXxLkkG1gm5Ku5hi9bQkS0TXqysfixDa109YUvWBLSo0mU1kgeMceWRdAQ84tUHLPrxJ3zt0T\noUzYK66K7l+JE3h4y+s0lqUwLqH2idYa7YHrBEh84kv9xUP/3au53b/mmrpo16nzU4TWmoXxJ6jk\njxOKr8Ow4hRmX0UaEdLrPrmmlV2dG6fCcfyK4ultmzhYOEVzuIlbOm+86HOPF4s8OvRDwODBbb/G\nzR1b0FrjVysM/vX/S3p6ipM9IY5tu5py/AYSx4s0nS6gtGJECm67tpm7H/salpMn8wsfoPDYY2TS\nXfz/7L15tBzXfZj53bq19L68fcUOFEhwAVeJWq1QpBxL8qLFsseLHI+S8cnYZ5LxSXLOJOPEY58z\nmUmcKHbGsT322CPLsmVLsixZtCVxRFESKW7iCpJorA8PeHhrL6/32u6dP6rfArwHEqQoAiT6OwcH\nr7uqum5XV93f/e25hbswFwUn0AgBxbRipCkRiTqnWkOEXjxNDRcS3HP7NO8+PIk04BvPn+cbZ8sY\neYmybdKNGjtmjrOv9CwAS+lpjg7dxZA8ww1nH8EOfBpWir8bvYuF9CS3JTze+bansK0Q3zfxu2DJ\nENHTmI1uiA417QVB088yZ05SyU3yjju/y175EE56mnrmBv7s5EOseEtoEcXxShYMzFsMr0TsWKzw\n3J40D0/k0MVlUJLgzH4MI4WIBlCdOIhtLR3LTFtYeRtnMIHWELUDok35yynH5MBwlvFiEgeBdZFb\nYyiXoJB1EECkFXPNecIoBVqTsVqYLNPtLl1gHraUj4w6+MrfYvVuVSCMBH5ocmqxSNvrLV50RHSJ\ndCaBQCiB0AZoI24IosW2jUGkobHt7QWyEBqv1xq01k2wUE9TaSe23ffVorUgusLFUrbjgx+59LZ+\n7fE+fd4gaK2oL3yb1YUHMZ1hpJXGa85gpyYY2v0RTHv7NJ3mc0+xMv8F9JDJF1YFp3STfYXd/OKh\nnyXvZLc95mL8SPHrj/0Ftc7THB55O78w8U7qD3+H1e98m3AlDo06Pu3wjX/wPibmpknOtABNPfI5\n5zj8D+8ZJ/knn6QTerx48xCzaY/zxUEmXriL80rGNcsE7C5qhiqSjox4IRIowLIN7rlnH1bW5sWl\nOi0/JJQCYW5Mtoef+BY3f+/b+HaCxYF9nHX2kTXLTFWPMVBZJDBM7h9+C0tmjutas0ynPCbvVti5\nuLSoiBQiHQdYhc+scu6IpJ4qEB6WLE1dz95ckR1WA6f+BMKwMAd/mC/OnuJI5xEQYISCzKqB2UyR\nP1egKvJU9lYQwwuIXoMM5SdQ83eiVrMXmLnNlElxJMVhd5j9Yzl2pBM4F6mLWmuMqIqpq2gVF+8I\n/VWioN7bHtHtLOIHbTphB18FKKWIdBQncEljvRZ507MIlYEGKu0Ebd+i7VuU20m0jrO/K+0ELd+i\nE5jUu6+toHy1mIYm58Sa8JpXPK6F9urN1hpImoKkKS87Zzy+/q++qI3WCq0vLA17MZ/8dz95yYH0\nhXafPm8AVOSxdPxT+J15pJXFyeykXT1CMneAod0f2VJTHOIa2fN/+vv4w+cwJpM83jD5RljjhsHr\n+MSNP4e1zTHb4UWKP3j+EY6ufJHRbopfKGXovvA8aE1gGpwfktQzNk+/9YMUnjJJeSaBViwFDVZz\nRT72Q2lqX/8UqtPFTkq+dUOaRmUXwdmD+L1J0klH7AhNCp6gjGIWULbBwVvG6Bas9TxrrTRmGJDs\nthlcnmdoeZ4dp1+kmR+gNn2QU6kxdi4f5eALT2DHFT+YS0/z3MBhbug8z87RZYw9aYzp5HrwnQ4U\nhJqwFrF0xoCCjdgX8F0/wY9k8ySjlfVrUYlMvrI4xEKwgEp2wQqJasP4p27EGRWYY8fRsoImTt9S\n3SRRdRzt20TlCQhtpITb3RHeceM4eyfzJOyN36ETdlnplAlUyEJzERXUSXuLZL157Oile08rDcF6\nkFgsyoQQCAReZLPSzvLgiUlOraQv63eXxMFnSRH/nxOQlQrDunBKDkKxRfxoqdGWRskA1YtqjzSs\ndmxCtSkljHis2yG0ouA3MVWIQJOOuuSDJhcf/WqJtCAIt9rSpVYMBKvYaqMYi61CBoNVpH4V7oBX\nwT/8/J/2hXafPm9UtFaUz3yRdvUIqcL1pAqHWJn5K6zECGPuJ7YV2MHKMgt/80cot41Im3TlBL9X\nncE0TP7dW/8lKevlmiTHHFtt8cXTZ5mt/jnD5SY//bAP9QbGrl3cP+pR2hEQOWNkzPdQeLxF1lME\nOmLeW2WqYHCL+Tzh7GnyrYgvvW+QJTONdfwWap0cAkgampQS7EIQAafR1AwYu3kYUXTQQmCpiD3n\nZikceZr988cwwzgfWQBKCJ586wfwyoKCnmfXzIukgyaeTFBNjiNViJ2JKOzxsG/IIJxexTCtiQI4\ndTbDEzOjNEOL6w+fQ+cqzAQRHa35uWyKpCGQ6eu470iTpfoZOsqkOtEBO0AHFubyNE5mnKC4TBCd\nAsK4GlkrS1QdIVqeBm2Qydocvm6YW3YW2DOeJRBd5monKddPs9JZYaVdxlMe+0zBQdvEIBaUa+lR\ngdacCELmQ4XhpUgFOfxmisbiIC3PoaUMuoHF5rYoUkeITfP72l95HTLmxwLIBCwEdtQlEzQItSIE\nckGDVNR9SX1SrItcjakiZM9cLtBYOlw/t60CEsrvbbt8xEUV3640oWH08tB/8Lzn85/td/nq0+eN\nSLv6AtW5rxIFDez0FAM7f5SF0h8CgoEdH9zatUspVh9+kMqRLyPvyCOESSN3K5+efwov8vmJfR+4\nbIF9qt7mT0pzFGa+zL2lBdzZuM7y0Ec/xifzi5TbL2CZBxhu3U7uuTq2Aj/qclqHfKT5KCPnFqll\nJc+/JU8pPUDt+C0oPw7CsgUUNOTRFJC00ZxAMzqW5aYDCY5ZCQrlRfa/+BTjczNkm3FTj1Y6i4ya\nKMPghUN3MJebZt/xp9m3eByIDaaN4RHShyx2DHpgGxgDCSCx7jdWGr59cgfHVooUxueY3XcOka1y\nVAr2YHNLcoDrC+NUSyanHzlGLfMQz95uoQob82gyug3L2YO36zht/yGIAG3hnz5MtDKKKQ3GBh3U\nqIeda2KMPsvxYBFzwaBWloxIg6JhUAT2QS/fOg4I7GLjaYkUgkg41OppZmcH6TQShL4k7JjU0JxC\nU9/mdxvxV3lH5Sn2N2evDqEngIsrmkmBsETcV/SSxwlE1oxvFgDTQORM9CsRnC+7q9jab0QIyJqQ\n2KSFG+A4F7ksEISvsJnKa0FfaPfpcxXSXPkezcoz+K1zcVT40G3kx95Na+Upwu4KmcHbcNKT6/vr\nKGLli1+gfuIhzHflMd9SQGiH3O4P8Ycvfo6G3+Qn9r2fd0y85bLO75VXePGLX+FDzz1KrhmbZa2J\nCQY++lF+33uWcv0FDDHI+Mx1ZM7XEVpTUV1qOc3HT36JVNPna2/NcnR3kmBlkuCFQ6ANsoDMWOzs\nRNgRoAxWLcGZhMkhN0MlITlmJchXlnnvlz5D2o/P3XGSnNnlsmPmGJEhuf/dH8WZXeCe576A1BF6\nwME6nEVOpUhlzd410URK0qg7JBMetq0IleC3v30bCdsnffBxVoyQQ6bkOmeEWQ6xvJpDnm9y8nvf\nQKfLHNk9wOndNlpLROMQdjEk0hW64hgd9Rz4PjpM4p84hGoMgDaQtkfxlqfossp+SzJtSrJSMpVM\nrkdid5SkFiURMocZOszPRkSRQafjsLA4RDLVIRCaLpBIdilmKwwUuwih40aZ3YCbKk1StQaJVgcZ\nhsiGjxFG0O1pvEUrFnoXIwWiaMdCcw3LQBStdSEq0ub2x74ajEtXDvNDA9ULULtgj41W4Begtnvz\nCnIlQtj65vE+fa4ymitPUjn7t4DATk8yuOODWIlhvNY5lo5/CgzJxHW/jLRi32RYqzH/+79LtzqD\n/eFJhGGQzh+mNXSI+2a/xXMrL3DvzvfwY3v/4cueu+YFHPvSl8h/9csIIJAwszvLbR/4BRL7ruM/\nPfVHlDtnMfUou5++DidIEWrNDBHDB0zck5/n6LRkrpBCLewjKk8QhDYS2GUY6AMFcqerNDNVus4Y\n0YCDHs6sN5xOtJuMnz/DLY9/k2SrycLkLowoYmx+FqkiAsPk9NBNJMMOU5UXEY6B9Y5BxL40atGn\n5ifxRYLlhRHsomBifIl8bsMX/OknDrEUKkYOPMe7spJVsY/OvM3Id54h21iglhOcHsly5ICDl+75\nL3UaaRaJonnopTgJkugIwuVRgnP7QJlghAyOzHPPgRl22ILURe5Sz7OorWZotlJEm3oyCwGJVBvD\n9nHskILTQiqFrvioFR/djaCj0KsB6Hgxohe6W4WXHRdNEXkLeX2WYDxPu5Uhikw8z6YbpIiMrel9\nbaVoKU07Univkzxo+RYNL0HTs2Lvu7i8wC7LgOGshyUvZ38DDOflA9WEhRCvfIFimYKRnOCVlD+/\nXD7x372/79Pu0+eNQKvyLOUzf4MhE4we+EWsXkWsbuMUK6c/j4q6DO/5GMn8AYJqlcp9f0vjkYdR\nfpfEx/dCUpHb8RN8eelFHpl/AoDpzAT/823/I/aWrgwbeJHi86cXqT7zDHf//WfpJJM8cqPFqV0p\nfurmX6IapLjv5GfwwjlS3X2MHzlAUkEbzf53DVLOPMfQzHO0uoMcaQ6xvDxFoCUCGABGkhaq6CAi\nRW13AtIbwVCDy/OMLJwl1ahjLVWJhgsUasuMz53B6PlJO2YKU4dYkb8x6JTEvL2AWvTw6jbtO6eY\nr42jIoO9u8+Rz23kAc+tpvn6sd2clQ12T89RbF/HyVYDZc6C00VJTZAKNtQ9DVJOABGRWgEiCNNE\nlR0EC2PorrP+2cKI2DG0woevP03OiYOXtL6g1Pa2aK3RlYDoeBO96KG7EboTQevlg52WEwVOZKdZ\nTg5QtXOEhqQjnfWTBpGBF70yQSQNwcEdWYqZ7e4TjY660AvE0qqLDldBK7TqMpptsmewhiEub7pO\nWiHmei60QKZ2Y1jF+KXhIJ1RDHsozp9b28uwEdssOmpNj5mFxksVOfu+CUJFrekRXWZZtDBSVBuX\nv//F/PtffmdfaPfpc7WiIp927UValafwmrMImWBk78+sm7/btRdZOf05EKLXueswjSceY+GP/wjt\necjpAZLv20/oLGMN3MZnVs5wanWGycw4H9h9L4cGDyKNS1ecaLfbfPePP0X29AkKtRW0EPz1vYPM\nDZm8b/fHeWixQ7v7ELJRZXh+P9nyGBJBYTLD9e/J8dDsn3Fn4PD4kUOcbmSpEJsxRy0D2y1iSIFQ\nEKRMoowVB4FV2tw88zR7Tr/IUtfhqUN3Ed24G6vb5m0P/h07zh3Hlw7VxBhpGmRalVhx2p3GGLLR\nKclyK02lM8jO6Sr2uGS5MsDYSBnT7LWg1LDYTPHE2TGemBsmkaviKYE5sICdXUYl4wWAjuJ0H+07\ngEbKnQi7hhLn4+1+huDcLqKVSTZL9azjcdvUIu/ae/aCAiRKw0ojTdO3EEGEKHt0lyMaXRsvNOKF\nTKvGRG0BJ9pUhUyYeIZN2c4RCYOqnWfJGaBlpfCkTcUuoAwJAnKZJNm0tUmL1GgdEgQhkdIYBMi1\nkDQNhhKgQCiFpSDqpXwBODIkn/AwhEYa0PQtwujy1EeNIFSCIJJESqC1INQGWkGoN0zfryUCSCKu\niGn69eK//NaP9oV2nz5XG1qF1OYfoLnyPXQvutbJ7KI4eS92agwVdWmWn2b1/DdACEb2/ix2YpzK\n/V+l+tR9mDcXMMZScV9iILAKfK6jmW3McdvIzXz8+p96SWEN0AkjHvmv/xeTR54ksmyS7n6+OFnh\n9HCK60c+ysz5Mwy92CXRyWD0ejWHWnPo+g6JsZMENYUZOHzj6B7OhCZGxiJRtEiOZJAF54Jz6UiR\nm19g3xOPcP3SMSwd8UxuL0c+9FGu/84j7Jt9irS/ut5k84IOTTuSyP0ZAkNS0hMkdySZTFRJ0cXz\nLaQRYVkbJtNKO8FfPrOfJR2C0Kj6IGlZwXSfxc94iNAgqI5jnJ/EGS2h0pL2wg6E1cUcOYeRahLV\nBwjP7Uc1C4DANCNuGlvijsnzDLeriEChawFRRxNFklrHoebZlJsJsp0Wo36FYrC1veYaDTPFQmKE\nmdQUrWSBfDGLjaIbQdXKUm2FBJHCZi1ELUYAaUPhCI00NNJQmGKjV6QOJTq8SMu+CguI9Lk0v/Zb\nH+wL7T59rhaioEV96SE69ROE3RWklSU9eJjMwGFMp4jWGq81S/nMF4n8VYQwGdz9EbxHT1O9/6uI\n20zkgQwgMBOD2MkxEtm9/MX5Z3hq+QhvHb+dnzn4EQzx0hN1EIZ884//lJ2PPkhndILrf+3f8nsv\nfobTTYeMOEy6NEtm2cLQkrb08SKLMpp7bjrKZLrDdx+7mRUlWcyYBEkTu2CTms5tnEBpvIUWoa+Y\nPHeSu0sPoLXAEJpU6HFq6i2cG9zH/tPfZbJ+Ij7GIFbTpUDeUcDIWYiMSWUWyuckU++WpIe2fi/P\nM3GckNlqlqOVDI/OTJANHKai88ihMyznfSrDAcJQhIs7CM5cRyqxCNeViVamidQi5thphFSgBbo5\nhX/yIKKrublxjEOp80zlWiglMBba6LK/ZQwX0zFsFp0BKs4AHXsADEkKMBF4MslqYuTlbeivgghN\nQCzDjfX34suqAdMycAyNiML1ymgGYBtxipXuVTB7OZQWeOryCpK8FgitSOAhtMZAsTU7/AdLnAL/\n+pzzV//zz/SFdp8+V4o4J7hOu/oCfmcer3mGKGgAgvTAzRSnfhhD2kRBOwpj/gAAIABJREFUi1b1\nOVrlpwm6SwDkRt9OdvitdI6dZOErfxBr1yM2dmKcwd0fWvd5L7aW+I1Hf4vp7AT/4vZfeVmBHdbr\nPPs7v0Pm9HG8fJGxf/arfKbWYr6tUf4sY0/7JFsZPLvNOQ2JsM0NB6oMDrRZDtssHr2TxUwGtSuL\nuU39aqPc5PyzNXY353hnu4QfKFKhT1p5CK0w0AgV4igvPsAxwIs1ZWNvGuudg/i2zWOz46TtgB0D\nqzgJHykgZWi0hheP76TZyOBkG+zbOY9thnzhwV3cOD+Lys9zdKfk9LiNkmtFVEychoOfDMEJQBgb\n7SwBHTpQ3oVYHmLnYpl9rbPsCRdIdTsX/p7ATHEH82KAVStLW8ZpbBJIosli0DUzeGYKU8R9nddQ\nOiIQEUJIhNCYEgyhtwggrXtm+1iMXiAXjZ5WrYKtrTNfE7RGEm5Ng7oIQyjSVhPTCElIj4TZ2fge\nBpCQiLVrv804LTMgnWxf0JPbsXySie62AlkI/YNY41yV3PHh/9zP0+7T5/Um6JZpVZ6mVXm2J6TX\nEOTH30N25K0YRizwmuWn44hxrUAYJAvXkR26ExW2WDn9V3jtWex7RgBI5g8yuPPHMXqNPrTWfOX0\n19Fo3rfzH7ykwNZRxNJf/Bmr3/42mTBgcfcBzJ/5OL+71KEdVPErjzF1cppkq0g1u0x6cJ6bJ8p0\njYDT3b0cCd+CEBmiO524VaPSeJ0ZpGUj5TiD7RYLD51hqrXITV4ZGYUkvCYTQfWCcURCYmyuLuUp\nxIhD9aZpFjODVOcTJK2AO3fO45gbZm8/NDhTzdGp5JkYLZPbN4thaCIFL5zxWN3zFF8+LEFszUUX\nVog/sElI+xZRYwgTg6CeJyxPkos8fnThO0y14naeGujsG8PbMYznW1RqWVaaQ/hmXP71YtM1QFeF\nWPhI4sA2YSsSaQ+pDTrtBIbvrDfzYKOvxxYiGRDJDZ+30GCHGiPSmJHGVh5Zr4ylV1E9LXnNxy1M\nA5mEFA0kIZ5j4NsSwxKYSYmfkHgJg6gX+mwZkDUFWUuTS3mY5qsv07lGqDSeAi3E5Sju63Rfobda\nCIEUr6BLyPeBNOT3VTb1taAvtPv0+QGwuvAdVue/AYCQCZL5gySyu0jmDmCYSQwZ+3uVCmhVnqV6\n9j4MmSA39k7SAzdBJFg59lm8cBY0qLqP7OQYu+cTWM7A+nnKnQr3zdzP95aeYSozwU3Dhy45Jq0U\ni5/5NPUHH6CRK/L8jXeydOtbKC/V6HQexSmvsOfUYczQppleYW7/97CT+5HWHdDM0Mg6cScprfFX\nOoTRHFHqKdLhLRw826Fw4jHM8iofaJ972WlX6gjSEmMqCYMOM4kxHm7uob6U4FZ7nrfuOk/CjIX6\nqXKe58+P0Knm0Z0Eh3bOccPB00QKVrpQ8j2e1AHdAqxPaRpkaJPwMzimTaSShPU0XmDSrGTQkQmB\nAwgioZi2aryn9R2GFuYxo4CV1CRHB26jaabwtQOz8ccK4uwuTyvyYZuhoI6BQtkWgZ0kMG2CwMbX\nmxYNPgR+/DrhhEyO1skUF6mE5ym3O3Q9Rb4VkTMEgzmLnGVhSoNkMcKweuVIHQMj9Wr80rL3bzOK\nS9XONhND2Mm92MkxpJ3vpWMZWIkhhHS2PWY7BGCYacTLWHz6vHL65vE+fV5DtFY0lh+jNvc1pJWn\nMHE3qcLBbUuNtirPUp37OipsIYTJ8L6fRS91qR75On5iHmPYRp3vEnxjidSuGxj9+V/AzMdNQRbb\ny/xl6YscrcaVwCYz4/zK4X9M1s5sO6728WOc+8yn4ewslcERvvpjP49nOYThIq3mN0l0Ye8Lb0Mo\nTXXsCOd3VpksfJBGtOGj1qGi8swKQb2NtesI9sAKb5nJcviR40gUcht9MRKSMJnAGhJYOYEYchA5\nE6RBOJjgycUJvnViiolcgx/ad47pQmyR6AaSJ8+OMbc8gOgkCBJ12gOL3DTU4oaCz8kg5Jsdj5bu\nuWWVRMiIjErwDmOMnJAs1wZYWLY423VYbl8ocBwCDpuz7Js/S6FVJhl1MXVERyZ4qnCIR3MHGZCS\nFIIkPZ8vL9+cwjAiMlmPZKJDRtTIBKsYS03Ecpt0p4YddSFhINLx/SDyFuateURqazETrTSs9bEO\nNLqhsAaGMPN5hHnp9D0AIW0sZwhxUSCiMOxYAIuL37ewEiPr1pvLRWtNY7WL772Smtyaeq1Lu/Xy\ncQEX43shtXIb9SpTqb4fokhTq7QJvPDld/4++ef/9t6+T7tPnx8EKvLx2+cJvQr1xYeIggZah708\n63+ElRje9rjGyveonv0KwrDIDt+JUc1S+eqXEXcZiF75RKOeImPeTnLXfpzJjepndb/Bf3ziv1Lu\nVtmb381dE3dw28hN2/bFDsplZj/750RPxjnbp/Yd4rG73ks3laFTexRfPkuilWXvi7ciohSN6TLN\nndcROhYIgVz10PNtWqpDfUWDVWVvvsTNZzvsnjnDxV7VlpVjMbMLRzcxHZ/iQU3uhp5VIdI0Vm2O\nzeb42uI+Akz2DNR474EZJvJxTvXcSp5KtUCtnqQ+dB471UImPJARo9LgWBByIoi26ImGkky2h7i5\nO4BI+Ty/OsjR+QGitfm15xoumh3eXX6WAyvHMHpzX1smaBkOTw7eSi09jSUEWcDofTMtNCEC2/YY\nztcxpUYITSbdxjRDtJboyEB2lwnnZigO2uSzFmLYRqTN2Py9HtslMC8u3anBEElMq0gy45IY3hN3\nEnOK6+6T7dBa02p4rCw2qZbbRNH22rPWUK926LT9Le/Xqm3ane62x10OWmmi6KWn6NDy8J0OV1Up\ns5dAGxov0UTJrcJZSgPTfJl4EenTsZvfV9Da//3f/3pfaPfp81qiwg6rC9+iWX5qPV1LCBMrMYyV\nGic//m5Ma2vby1b1CPWFbxN0lzHMFKP7/xGNBx9j5cufw/7IBEbeIiH2U3DvwU4ObTl+tn6OPzzy\nacrdCu/ffQ8/svueLfsESnG81qLx1fvIPXg/MgxYHh7nsbfdy/JggcbyHEH3HFbhLKOlWxloDSCE\noDqeoHn9IDpSBHWfzkKbzvkmk51lsmGbLDXeVn6BZHjhZNa08swUbyIwLIbDGaZv7WBP2YhkvPjw\n65r7T+3kicoUqUBQRLBrpMyt+8+QcTyiSLK4UmRubpRmO0luxwzhxCzPeCHNbeanvLLYJx0KSGxD\nM5EMWaoN8/jcFGerWcKebBKmQEhQnmZX+zx3VZ9nR2cegLn0To5l91B1BgjNNFkEzqYoJw/Nkggx\nUx3uGJ5hx2iLYiEgjAS1OlBvkV3oIB2JyFux5uwYGPkNIau0JDLTYMSdtkxhIpEYZgZp5Ykd3hIn\newOms7G48/2Q6kqsTWqtmTtTY2XxwtQxraHZ8Oi2t+9FvR1aKLxEk06qTjddp5Oq46XrRNsIpz5X\nlr/82H/rC+0+fV4rus0zlE9/nihsIq0cqeIhDJkkPXDDek/rKGzTqR3t9c0F0HQbp+islhDCxE5P\nUhi/m/rXvkv91IOYbxtCJA1yo2+nMHH3lnMqrfj88S/z4LmH0Wh+eNfdfGD3ves1nc82u8y1uyx1\nfJ5bqnLTg3+He/RpWok037vz3Rzdm6XbfJEwrKNbeUQzz77lnaR1HJRUO1jAG0zgL3fIP3mCG8sv\nkKLCgN8k512ooYXCJJAJPJngbOEQvuGwu/E0w/s8zJtyiKyF14KFdobVIMnXSrtp+jZTaPZn2pim\nouubtKwOq3abzMQcY+kuaVPTEYqjfshCpDC1YEjbJAwoGBamEFyXDLFVjqVujprK8MLKKPPnBJHf\n6zhlGXHksRdyffM0k91lxr0yORVQSU6wnBiinNmFuEiDjdBxUZhEk/fsO8aOsRYXtbRGa33JGtoq\nAqUkflCk0Zmk5U8QRinW7BDddsC5meqrrtolpUAYF547mbIZGs0wPJphYCSDNAUznRlmu7PUw7iV\nSKQjyn6ZAJ9m2CLUGwJaIBhJDVF0Cpf8XmvfW+uAVzv4lJlgOFF82YyGy0HrEBW2+UHKLSEEQ3aG\ntHn5PvzN2IbJsJPF/D6C49wb3tsX2n36vFrWUrZCr4rXOsvq/DcByI//ELmRu9b91Vor0IpO/STV\nc/ddFDEeY6cmGJj+MfwT56k8/xXUSBtjwAZhkh99O7mxd24J3tFa87njX+Kb5x5iNDXMTx74cdzi\nPk7U28y1PGaaHY6ttkFrpk8f485H/z+y9SrLxUH+9t6D1OUsoprDmj1IykuTQpAGHATN8RTVgwVy\n52vse/YZBtpHGK+uslbaOZACqcDQmlCYHBu6k3J6itHmaXLdFbJ+mXRYx7p7BHkgg1JwdiXHiYVR\nUgIsbRB0bRqNDG3pk586R6pYQSWbDEoDKQSnw4jnvIDNS4NiVMRu3UKz7uD3ssKU0nS7BmFnGzOw\n1uSDBrmwzYhf5abmGTqJERrOIIF0qCbH4hQv1gS0po0mKbqMpMvcffg0ThJEL9Buvdd2qIhCTSBA\nBxa6nUApgTKTNMIhzs+ZrNYz+P7L+4KHRjMUBlN0RYeGUQNAEGGZLUSv9rYUPpbVxXHMdSHtOJJE\nUhOFDbSKiPxVlA45EwSc8gNqSqF13EhkO53ZBhKGQVIIRk2TUVMyJiXDpol9GTlUWodxVkOf143b\n7v0PfaHdp88rQeuIzuoJWpWn8ZqzqGgjV1eaGQZ3fxgnPU3QWaLbOEW79gJ+Z3G9NjMIcqPvwEqO\nbHxoC9qPv0jj6YcRt9jInSlQkMxfR3HHvZh2fss4zjXO87njX+J47RTj6VF+9bZ/StJM8rezyzy8\nGE/8ltfl+soCkw/cz0hjMe4xvWMXpfTNJKIUqcjEviieOzIFnZEkRqrCLY8+znhtJo7oBnwzni/s\nXhBUKExODRymPlggHyyyd+ZI7A82wLh3DHtvHBntBwYnTu5gZnaaCI3K1JjacZ7JgTrSiGjLgBf9\nkNNhRP2iQCIZ2ejVCbzVAlE9h/YuTNkSWsW5vpuEjKkjJr0au4MGhcija2XxZYJAOnQvck100Syg\nCdDYqTZ3Tp/l1qlljN533SyoI99AdSZYPJmgvmqymp9GK0m1vNF4RAlFK7eCGOuQHpAkUjZOwkSI\n+N6Ja3J7oAO6KmDFr6PRKBWw4DVQr5F/VyIYMC2kiEPkxq0EB5MZBqWFELE2nTHkS2rSL4d4FdHj\nPyiEkFiJ4S2Wkjcbuw68tS+0+/S5HFTk06o8w+rCt1BhHBwl7QJ2ahzLGUQYNk56im79BM3Ks6iw\nGR8oDOzEKIaZxHQGyQzegp0aQytF4/HHWH3wATrHShgHMljvGUaYAtucYMj9GKa91fcN8LWZB/jS\nqb9Ho7lh8CA/ffDD2DLDV0vzfPfpGW4+/QzXLR4j01kFoGEXOVa4nvOZXSQNcz3S2UfjpQyMTESU\nT9EcySO1x+3ffoB9p5/FQONLg8AySHbjDsGBYdNwBghsm0pxmKnlE0SWydmd+1kaGqdqZLhj9BRu\nfp5u16bScnj8+DQNu0V+6hzFTJshU7KqFIthxPlIsdoT1BIT6aXxPYnSgrA2TLS0A4SBIQWFgYhu\nK6LTkuhe+U1DazJaMaRDikqBlbogL3kdrdBa0QRWDIM6EKKYytYpZH3etecsQ+kLi6VA7H9eUPs4\nVbNpeIryUouu06RRWF7vQCVNA9uRGCKkE/l01eX7gg02Eq8GpcFuSyINE2lmkFZmo8uUYSJlct0q\nsIYQEsNMxVaY3raR5BA3Dx/aNgCxzxub4eFsX2j36bMdfmeJ+sK38VpnUVF3PagMYZEevJlEegda\nh/idBYL2PH5ncSPwTCZI5Q/ipKdIFq5Dmsn1z1WeR/N7T1C9/2t4584gijbOXTthZ4QwHIpT7yM9\ncPO2GpAfBXzr3MP89cmvkLXz3LPzg4yld3PfE7PMlJa4fu4oN7bnMIFaYpCV1Bhtq4iWifXPCKSP\nlWxCwWRpegfF2gpm4DO0dJ7J2ZMMrSxsqTrVtnLUnSK+TJHya9SLg0SGSVdmmB/bxUBhld0DZxBC\nk3YC8imfqq95zg9piwhbCCqRYilSNC6aV7QyEa1hRH2a9vkiaIFVsMlPWEyk62T9JounNfOrGTpI\nlBBYQBHIIBiMAiRqo7JW5LFkmDRkgq6Iy3T6gEIzYrUZdpqMjbQYL7aZKtQvKNCiA4WqhYQqzYnU\nME+s1llKLKPk1rSlhGGSMMxebELclANiAbzXMtltSdK931CaaRAS08kjDAcpU0g7i2WYDNhppDAw\n7QLSymJIB2m/tC+5z7VLX2j3uWYJgwZ+6xxaBQTdZfQm7SjwKnTrJwCNkGkMQ66bNjebwzcQWIkh\nrOQYydx+UoWDICShV4mFemeRoLqCf/4c/sIC2lAYQw7GkLNeBFqYaTI7fwqZGCWIAjphh6pX41jt\nLLP1ORbbCzT9ZTQR6ARJ6/0YXpJdjzzBwfJZfCvHufxBgk0LBIBIQpQxsO0WIhEijYhCdZmBlQXG\n52YwowsFUiQEnmXTMgeo5CcpO9N0zDxaKLrJRi+yeJXR4ippM2JHEvalwNOak17IslJ0teYFP+Ti\nbFtbSUQnS1AvIJsFRDsHXhIQBEAoABP84OK2IDFJrZgKu0yETcywQxh1Oe4U6ZgJPGnRNpJbjhnL\n1vlh9zQThRZ2zyHfUZoIHVcdU5KWFpTxWIxU71+E15tdCoZgypSMSoNUz5ecEIKdpkRuEqzSypEb\nfRvSzGAmhtaL5Bgysf53nz7fL32h3eeaQGtN0FnE78zjt+fxOwv4rTleKj9UWjlMp4jfPo9WcfqM\nkA52YiTOlbULWKkx7OQYVnJ0PW9Wq5Dq+ftplZ/e0M63xSCwhqgbg5S8AV4IJwi0QRCeolV7gmhl\nFLpJkl1F0lMYGuxAEoo8oZ1hMmiTUBbhJi0arSibcUDVqJ5nRKySjNrsOHMcK9g6Fk86VFKjeJZF\nRJJKeopGYhCFidCCyAhYLc5TnjyHTNRJG5AQ4AhBVWnCTdevreJmFOvfLjTJLO7CbOWwMMm1UnSC\nJMto1pY9Crg4MckAkmyYjJORz6S3yI3WLLl0nc+Gd7GkemlR8Zdms4AXKNzhCjdOLCPtLvOJKi29\nIawXI0XrZea2orSZtlLcmhtgRyJHuuBi2sWtOwow7QEMK40Q5ivSjv1IUfUDAqVZ7vj4r7goiKbm\nhawG/bSsZhBR8YLXrWnHleT/vPumvtDuc/WgtH5NGtavmSzR4LfPsrrwLYLWmY3tCEJ7lLK5i9XQ\noCkKhMIiFy1TVPMMROexiUOTOyLLeXM/i3IPHZG7IOCp6gc0/AgbjwKrJOlyWDzHgKjRDSzEbANj\noUVYDlkcnGZut8vC8DgeESv+HH60QhSUMVQHu5bBXCgQdIrUzEGswQpmooVIeKQNzdj5MZzVAcSm\nYs1aarycRZiSjJdPM7J6hrTXYKC8iNykQQeGzZmB/YRpRT7dJco4ePk0kRMvNJQMaaebNGVIgEII\nQUJE2EbA0SCkvI1AsQFDy971FkSBjVOdIN0qkO3aWJ00SpmsAlU0baEJemO3RNwxS2hNUofYOiCl\nA6a9KlPtlfUKaoumwxO5XdTNFAix3oNZJNoI08eREbbjMTS0hEg20CKia0S9HG5BiN4S2GUHNqlu\nBgIbQ0qy+QSDxRx7BqeZzk0ylRknYSb4flFaU+4GNIKQbqRY7vp0I8WL1RarQYgfvVYhZ30Aspa8\noAHLm5X/+N6rSGi7rvvDwCeJF9t/VCqV/o+X2r8vtF9bIqVpRy9fclBpWOn6dMLLK0+ogEo3oNXb\n3+tNYIHSBErhK02kNJHWdC5RuWkrmgQekoiiqGMTMCaWGRVlLEJSQWtLC8E5NcqsHqeiC9TIESHJ\niwb7OEmRGknhkdEt8BQhglOdCY5WBlERmL6Psc337YSSbmRiaIXuRVRHAkLTIDIitFSQ6CKEwMhY\nyGSChBLYYUC+WccgQhpJ0u0u2WYDQ0Nk2Qggt1rB7nbwVZpFZw+11Dhm1GW0OYMddcl3lym2z2Nc\nNPUrIWgUCyzunoZhxUC6TaGgyCRiO3xXaXw0p4KIcu96t5TmeBBumxYEcNC0uN1MIhA0ujbtrs3T\np/bgt9KkNGzWdBWwKiJahsaLNvJR01GLaW+eA90ZBsMama5P2t96xnLW4vyI5NkDSVaKrzwS2Igk\nlpdCaIHQgpHmDg4VrmPnviGGBrJk7DQQ34erhqZ9mfecBipeQDPY/r5vBCEVL7YbrD0j22nPphAM\nJywSpmQ4YWEaBkMJi+TFyd+XQcaSDDjWFW5TceVJSIOE+fo0BrnSXDXmcdd1DeAYcDdwHngc+KlS\nqXT0Usf0hfarJ/AjyktNlpebnGt2OdPsUvUCXt5CFwtLY1OxSBGB0VLrlmaLEAcPW7x6s10oJWHv\nIdSRIOiYmyzZGsnWloVtJWgq0NogiCQ+cRCSEBrHDrBND8OPC0EINEYQUlcWK6lk3CZp7ftYHWTa\nQzqp2Nxp+ggZrkcKbyAwZApDJuOKZ7KAgYW2bMwwYOfpo0zPHKNYXUGoCKEh1W6sa8CRkKykpgg2\n+Tsjw6JpD9B0ioSGhS+TqF6udy5YYE/7aZI0UCLuPW1uenw7Q3mWdozQNQVVy2PViMettaBjBLSM\nkFDHOcjbkVQWA1ESI7TjAieBRde3UbVhOs3N5ujLI0mLwbDCrsYyN66eJh+2CDbNq4EpODnt0EoY\n1DOSVtKgnZAsZ20SYQoTgQGYQiN7v48QcTGRlErjqFiTV2GSbFRgMj3BxMgghVw67u4kDYpDKZIp\nm0YQ0goiFjs+L1SbzLW9dQH7WiKFYK3OyYBjMZFyKDoWliEYSdhY0mAy5ZC8RgRMn9eelxLar3eX\nrzuB46VS6QyA67p/AfwYcEmhfa3iR4pGEFL2Ao5UmjR6Pq21Wr/thoe6qOavavlEZxsQKoxul6AD\nF07CsSDbrrFD/JZe/3OVWJvSxD7N7aoTCxRS67hMYzaBHMtC0tq8A0JrBCoehSUh3TNJCgGvwaRm\nsB7jRQRsFz4GsLUg6EugNXY9QKylKDVCpN8T5irACDuMnpsn2eoitKLGFNX0DvSa2W5AgDSIDJMm\nedQlHjNDRBgyRFoe6ewKifE5zqXKnMegEuXobCpoEbdvFEQiJF7vXmLong1aortpVGii21mi+lCv\nDaSk001TuUgw24QYKIqihSVCDBW3fzSAgt8m69dI++0LjhFoJjvLjPkVAGpJhxPTQ5wcvIGOlUVg\nIEMDPAcbgQ0obaJ8g1TX5KZ2gUIxTb6YJD+QJJm0MW2DdMYh0prFjs9826MdRrTCiHI3oA7M9X4D\narWNwayU0Ro8deGCKykN9uaSTKQc8vbla/M5y6TobP+bJU1J0X5lfu0+fV5LXm+hPQmc3fT6HLEg\nvySNiwIwtNYQRegwhDBAR4pOJ6Djh4ShhrBNud6k3Koh/AY+Pl18NAqlNKFWWIZCqRBfKUId4SmN\nFyq00igtiPTaJBmhRYQWm5rQ640wCA0IW1wQKrNFHvZea0OBEVyg7W2HXp+ee4UkBHFepiHWP0sq\nTa4VIENAiV4vhBCFRJoi/lUTYBfi8xtohNaEQHBRkY0gOYg2bLSTAjOJKQRaWTiAYUAkPCLhcUGp\nCxFri56ZJFzLEY0CdFRZv0Z210P5ERer9VZl4/c0VIQdgblJNdNAFL10ASahJGZovUR82aUmVE0i\nCDF6k3uc5xtbD5wwJOH7mFFI2xqkYw1eegBAh0E6W1s2X3Q2TeR0aRbm8BJNQiNCGVEvQruJn2ht\nHWrPKqtDC+2ntnxH7SfQfhIdSXQni2pnSWiNLQJUINGBBBlg6oiRsMxoZxkzWiJEInWAsgy0adAR\nCSJhYqqQtnA2Fhybh2JAJylopxwiI0EoTCJh9PztgiN6EiUMQsPaSMVqX/wpAa21PxsbQXLL1GB5\nm4u2VolMrGVhbzxdm1OX15aCqme232I9EoKmabAsBM9sc5q1T/nBoi9Ot35FCKW2WJq+X5QWBGG/\nXebVzl/95s9ccttV30/7X93/m6/+4Ivnoc2K3VV/32o2JpWeBFv7PgYs5C/+ci/1UwouLcjKW9+6\nWAHebiFyqZqJm4dz1d9dl+LSmuxriv/SUl9skzcszBak1sTgIhD/HN7aDjJAmAER8er47JZP6HOt\n07cRvBG4eoT2HLBj0+up3nuX5K9+/rf691ifPn369OnD6y+0Hwf2ua67E5gHfgr46dd5DH369OnT\np88bktfVSFwqlSLgl4GvAc8Df1EqlV58PcfQp0+fPn36vFG56our9OnTp0+fPn1irvpwrD59+vTp\n06dPTF9o9+nTp0+fPm8Q+kK7T58+ffr0eYPQF9p9XjGu68re//375yVwXXfgSo/hasd13cH+ffTy\nuK47cqXHcLXjuu5w7/83dZpw/2G5BK7r/qjrupfOcL9GcV333wBfBiiVSpfb+eOawnXdH3dd9wjw\n3t7rN/Uk8mpwXXfIdd1PAZ8ulUqqL7i3x3Xd613X/VvgL6/0WK5WXNf9Edd154A/AyiVSm/q6Op+\n9PhFuK57CPh14O3Ag8DHS6WS99JHvflxXffHgV8lroOmgV8qlUrHruyori5c170e+PfExUhHgFKp\nVPrFKzuqqw/Xdf818AHgGeBjwIFSqbRdUdNrFtd1U8BvAu8kbrJUBf7XUqlUvaIDu4pwXXcP8F8A\nhziN+C3A/1QqlV6nkoZXhv7qlg1NyHXdfwl8CvgK8M+AcqlU8q51LcB13V8APgH8L8QFcebZVDmz\nD7iuWyC+Nt8olUo/AfwTwO6byDdwXVe4rvseYDfws6VS6ZeINch3X9mRXZX8EyAqlUp3AL8C3MtL\nFw6+pujN2e8Fvlgqle4FvtDbdKmeQW8a3rDVoV9j4p4b8DDw30qlUsN13VHgt13X/Y1SqbRwZYf3\n+uO6rgPkS6XSEvGD8Sebth0i1gDOuK4r3uzmqJfCdd13Ag+VSqVC0b6WAAAVdUlEQVQa8GubNu0l\n1gCa/WvkHiBe5M2WSqUHgAd672eBHL02Ka7rGteyy8V13RsBv1QqlYDf6RWjolQqVVzXPQV8CPh/\nr+QYrzSu6/4EcKJUKj0H/MGmTTPEmvZh4IE38zN3TQtt13V/lJ6ZznXd3yuVSt/pve8Qd6e8D3gH\n8LkrN8rXH9d1/wXwYeBYz+/4eO/9ZKlU6hBPHDfBm99/dClc130HsfnSB15wXffJUqn0Kdd1nZ47\n5avA7wGHSqXSU2/mSeRSuK5rE7sL7gW+DmSAf9zb5vQWx2eAnwP++loV2K7rDgJ/AgwANdd1/5BY\ngVh0XXet9c4TXMO9PlzXPQh8htjKN+O67gLwyd49lCiVSl3Xdf8UuAt44M38rF2zZl/XdT8G/Bvg\nb4DbgX/tuu6dAL1J1wASELeSvlZM5K7rvhV4F/B+4M+Bu4F/CtAT2EDcHrm3/zVxXbbhQ8Bf9Uxz\nnwJ+13XdiZ47xezdQ58F7oBrdnFzI7Ep/K2lUumfA3td1/1PvW1rpt6/BrxrPDr6R4itEG8H/nfi\n5+9nAUqlUlgqlbpAllggXavP3DuBPy2VSu8H/gOwB/hXvW1rrrqI3n21luHyZuRa/PHXeAfwqVKp\n9BXgN4BbgZ90XTcJUCqV2sAssRbwpo6Udl3X2vTydkCUSqVyqVT6u957P7a2oOnxHeAj8Oa+Lptx\nXVf2XCa4rlsE9gPfBCiVSk8S94b/33qvw57PzaL3jL2ZJ5HNuK47venlHcCZUqnU7L3+c+ATruvu\nXTP9Aini69TkGmLtXuoxABQBeta+YeC9Pf//Gv8PcJPruvlr4ZlzXdd0XXdvz+oJcCdwA0CpVJoh\nFtQ/6bruDZsWxEeAn+/ts7Wv7ZuEa0Zou677Cdd1P+667s29t54HDvXMlqeIJ40RYg1zjS8BYi3/\n781G78H4beCPXNddW7X+PWC5rvv/t3fm8XqN1x7/OkiIGEvFfH2CnyEuiqsxi6Q1lHANH9VbQ0Rx\nzWMQJSJpzEGvRnBr7jWLStRV3BgSNbXm3p+YpxRFk9QJEXL/WM/O2Tk5GW7J+56z3+f7+fic8+7h\n9Zydvfd6nrV+a60fps+dgGeZtcHr84SLqiEERJKOAMYBwyXtlhS8fwYGSNpY0n5EpsEuksphg7cJ\nAV+lXyIAktaQNAa4StIZklYnwku9Je2V4rVrEffS4NKpjxKCog1qPug6IGkLSY8Bl0r6VQohPAE0\nSTpM0nrEM/ccsGlpVd2VaGNceY9E8oJOIBZTI9M1Ogf4gaR9JR1AaJDGkJ6vxNPAu5LWrvWYa0ml\njXZSqy4r6TbCndkEXJOEVA8RM9pbJP2emLlNIGb+BcsTxr1yq4D0IPwnES87EdgyGe5PiBXRSZKe\nJFZBo4GppRfIkkQM7g+1HnetScbmR4Rn4UpartNAIhVnANAPOCnt36l0+kiSp6bKpLjrMcQztScw\nhXjJTiWu0/eJa/MG0BfoVrjDbU8Hdrb9lCqez568eIOAK23vRwgVLyEmMtcQaaYjiNjtK8BKpVX1\ni8Bw2xNqPe5aIqkbcCCwq+39iXtpKPAhcDihpdkbuIqYKH9YOn0K0K/q16jSQjTbMyR1JcRCB9ie\nmlYANxDimEOIF8ritkdJOolw6V2fzr+biHlXkSZC2HKt7Y8kDSLEU+/YvlbSncDytl+XtDHQo3iB\npDzIG+s18BqzHqGif5eYxfckUrsetz1E0ndsfwwgaWli5QiA7UnApAYQoc0A+gD90zN2PxGTHWj7\nOOBuSV1t/13SJsCLKSsBANuP12fYtSNNSFYlhFRj0+ZhwMvAg7bvkPQoMDW9t7YDNi/unXQvjavH\n2Bc0khYueaIWIfQyhW26iwgNvE5MdsaUzusJzAw7NUqufyVX2inmOPNj+rlS+jmKWEHvlh6E+22P\nSvvWAB6ozShri6RVJe1Wiq0uAzQD66bPbxEu3+9JWtH25GSwVyfys8fWfNB1QNKJyf1WMI4wvPum\nz10JD8NP04v4E0ldJA0hhEJ/bf2dVTPYkraXdLaklWCm6/9m4OR0yLLAn4CVJfVOx/xdUi/gl0Sh\nkNmo4HXqL+kEmPm3fUK4t3sr0t3WAx6nRR/SDCwsqR+x4q60ChpA0vlEmKmoZ7AI8AKhfVgS2AR4\nEliN9A5XVNM7Bzie0Nc0FJUy2pJ2lTSN0irQ9gOEqvB0RSWms4lE/KPT/hmS9lGUnfwuFTROimpm\nE4BfAesDpNzzh4E+aVV9H1EGcAtg5XReL+A2YILtC+sw9JqRwigPEkr5XsktDrEyuh44XNI4oBsx\n8ZtCPD9FGtPawF62n6v54GuIpAMJ79PmwI9Luy4HlpJ0C2Fw7gVMeLmQtBlwJPBr24NqOeZaI6mT\npFMJV/hZktYCSB6Zq4GNiHvoCEI4tV5aNUJ4AA8ADrNd2dKlkhaXNJioiLcRkV9diMxuTofdSmgd\nispwhUp8R2AJYPuU999QVKaMaVpdHwW8A5wAnJAMNmm1uB7hwhtte2wSzZxp+xlJPYClbI+v0/AX\nGGk1uAvwPvGS/Qr4RcpvbCJi+NsDz9t+OwnTnk45x4sQ1+WT9F2VLn6hUOt+CuwMdC4bF0XFs+Vt\nvyppFWJi2CcpxbsWCukGuEbdiRXPEsQK8Qrbz6R9iwEr2n4rfb4TuM723WrJXy++p+rXqchNHwJ0\nTzHs8v4NbL+Ufr8UuMb2s7UfaX1IHr8NgFcJD00TMaF7q3TMirY/SL/fAgy2/VIrd3rDURmjDVGL\nNrl0jyDKJG41h+N6EMKhflV+cRQo0kQmSVqDKIxydlsz1DTbH0Rcl/dK25uAGQ3gqlsoeV52IQRT\nt9l+oByTTkKZc4nJ4Vnl+6fqhgha/kZJqxICvM62B7Zx3H7Eyvog26+1Pr92I64Pain40ZnItjjK\n9u8VOfzTS8edTqjn9ym0EY1CMZFTFE4ZSMSv77H9Zek+6w6cBqwC9LU9rZ5jbg9UymiXkfQ4kYc9\nIq02i1XlccBBwEjb51dNJDSvl6Kivvr6wGm2J6ZtiwFnESvxQS6VLK0i8/o3V6T4HQCsTlyn5rR9\nMyIm+zhwSvnlW0Xm417qQ6y2x9j+bdq2CDCc0EoMtT22FmOtF5KWsj15DvsKw3M4kUWwbbFCTJ7B\nC4h77DDbb9Rs0DVmflbGko4jCqZcYfvltK2JaATyAvG8fbnAB9sB6JAxbc1aDKT1vkJ1eAZwLMwU\ngXQi4mufEikm55f2VYbiJStpo9K1mNkUhTA63Yg4EpI2dFRc+i9g7QYw2E3z+jdPKtQniBhaL0lb\nSlrO9tPEbP+E5BbvkM/PvCjEisngdJnLoc8RL9TNFYUwtkkTmYts90lhqMqmcSnKjw5Iv3dX1Fif\nSSnb4goiLLWXQhC6iyPX/3TbP6i4wV6oNFHpoVZFhkr3x02ECG0DSQdL2jVdv762j88Gu4UOs9JO\nD8j+tn+ZPq8MfJYU4LOsnoqZnaKG75JEWsCDtkfUafg1Q1GG9FDgIyJmP620r7gufQjjvShRoODY\n1teuDkNfoJRXjSk8sg9wu6PxQJurb4VC9RhiotfHKf+zgcIFOxA5/Fc7UiJnW3krUriuIUqWnml7\naGlfVe+lhUuG6DdEFcFmwks1qtWxxWp7V6IP/TvAANs3t/7eqqIodnIJ0c/h9CQ2a+u4gUTY8kOi\n9W/Diczmh45ktL8HXES43jYh3HKvEQVC7k3GaJYXr6TbiXjREFdQ/dz6pagoGvMC8WCcO4dzlibU\n0OsSxvq+mgy2TrQy1osBOxDCl/eIPPVxKYQyi0GSdCjRq/dk25fXYeg1o43nZnNCaDeOuE/eAA62\nPa0U928iJsQvECvuYx2VBRsGhSp8X2Jis6ftR+Zw3CZEcZk/EQLZyhVrKmjjnbQMkVnw2NwWTYpK\nlb8lmoAMX/Aj7bi0a/eepKaS++R/CWNzIlHsYkMip3onIm2AVi+e3Yg0p9WqZrALt2yaqHSRtLui\nyMdLwB3Alum4zm2cviRRpECFwa6qmxdmq41+IWGIT7H9U6Ly1D6SVkqrofL9NopQQl8Os4RdKkfJ\ny1LcL70JF3c/QgQ0lUhPKp/zdfJy9bW9m0MAunBV3eHlZ0TSjpLGA3sQosRhxApxTvfJB8Detn9W\nZYMNLeV6NWve9T8RHr25hTYnAMoGe96025V2qxXSco6esqsSRumPto9IN8auhLDqHNvNpZVAJV1z\nZSTtBZxKuJ2agcuAonnFhrZfm9t1aK1krRrJgKxAGJwxhGvyMeDQFG9dGvg5gO2TWp+b7qNFgK+q\n5govuW2Ln3sTE9zhkm4E3rV9qqLAxR5E7fmDbU+cg5u8cs+bWlJFHwempPthM0JAdq7t/y4d+zzh\nHr9TLdkajaKUL4cmdyTKjj5HPGtPEKm4t9geVzqnoa7Rt0m7WmFJ6pZcmIUIZnVJo4HLJZ1JGKfz\niRZ/3Rz5w9OIGr3N5ZunSi8QSb0krVn6vJikQ4hQQT/bvQnX0v5EPeOhRN1rSC0026JqBlvSxZLO\nSL+vkO6FvwErEjHpDwjBSyFQnJQ+91GUap1J6T6aXjWDDbN4IJZMPzsTIqCeRB/wHpJWsT2FaE/b\nhci6aLOzW8WetyZJ5xHFhw4j0iSHpd3LAR8UBrvknRhK1OsfAdyrUJVX1hhJWlkhdl28tG0rojjR\nj4n30UWE6LUZ2FNRSW9ZSVcRmpKG6RL4bdIujHZyq51NzMyUti1PuJ5+TeSDnkwYpaeIhPzL0unf\nB6apYqlbBYrUkBuAKyX9LG3+gogldiI6J0HUDf+QSCsZSqied6jiNZkLdwHHSxIx0eudhHi3Amsp\nCl6cDawpqW86589EzexKF7ZoY+LXWdLRRLgAIlTwV6KX86fE/XWdpB8RwsZngJWSd6Lq9Ae6A2vZ\n/lfC9f0TSbsTRmiipKUAHHnGXWzfQhjuN4E9PIc0sI5OelcPJUKTg4lnrsjTX5Tw9O1FpJBenOL8\n/0FLEZWxwOu2r67x0CtD3Y22ogXkRCL2sZ1bykAuSvxDf5eYtd0G3Gj7bUIk01vSQ4ThGlBh4/Q1\n8SBcBxwo6SCgyfaTRIy2mLG+A3yH1JcX2LiR1JfJzfYokdc5jAijFL11xxJtMvdIh1+SjsH257af\nqvmAa8gcJn7TiAnwUpJ2TM/PPcCmRGOL09PnfQij9QgwPXknKksKh/wQuCppRpZwFIc5g+g+9RQx\nUT5I0jJJQHWppI1tj7F9nivauCK9q99PH7chGucMBk6RtD1R0nd/YuG1s+1zUwhzIUfaW39ga9vD\nZvvyzHxTd6NNuC+Xtz3Q9nuStpW0JWG0tyVa/Z1lu5/tzyStTxixw4m0gCMdOY+VI3kPJhGNBlYg\n0o96EgX2FyFWR2tIGpGEdz2JeDbESqmcB1l1iknbkUAvYrIzSS3NPx4AdiNW1dfSYsAbgbYmfhCN\nGB6hZXIzjpgk9yNKb15KXM+tiVXkM7Uddu1JIaNpRC15CBEetq8D1gHWJHQQ3Yka2TcQjT0q7alJ\n/A1YIb2rPybSHscTVRSHEZ7SN4DxwJQ0obmXWHlje2IKt2S+AXU32rafAO6SdJuk4UQcpGtaUb9C\nvFT+kuK4txIrgK9s32r7lfqNvKaMAjo5ajy/SBR0OJdwY15GqMV3J/LYR8MsMdmqeiBmIYmEmpLO\nYThxje4gVgEbEUb6fwhhDLZfaYQJzVwmfqcRz/8twPKSBirKtzYTzWPeTl+xNeES3t72TbUef61J\n98TDwNpJF/F1EuNBGKAetp+1fSxwou1/tv2bug24hqR39R2SrkmbpqftFxNlRjchJjQbE01lrgEu\ns31JHYZbWdqFelyRy/cecIPtw0vbizzIrYgmBaNtn1mfUdYPSf9GrBJnEEUsLiCM0MdETGkLog/v\nLxQVh75uFGM9JyS9RQjOliEEVOPcRo3sqlNSwe8JrGP7vBTLHkJ0nDqZyMUeTIjSTrP9x9L5Dafu\nTZqIo4DXygZH0s3ACNsP121wdSa5u98AtnQ071gieUCvJ3rMj0jHrdNAi6qa0i6MNoCkQUS8o7ci\nl2+malfSakCzG6ygfkGa1LwO3GT76LRtHWIi8yjRvewY4BBHy82GRS0pTPsRFbrWl9QpCdIa0gjB\nXCd+kwn35ntOXbgKD0QjT/wk7UxclzHAs4QqegaRrTGxjkOrO4qWmr1tb1nadg/w8wYJE9SVdmO0\nASS9CZxk+3ZJizrXmwVmvkQvAn7n6BTUuupQV0LskeNFzGK4HyAaENze6B6IOUz81iZysx8qHVe5\nfOt/lKSt2ZbwZN1ne+Q8TmkYkifrQKJn+rVE2eQjgcmN+ozVivZW5elUIm/29mywZ6M7sJhKBfgL\nXPEqS/9fSnHIzwhDVak84n+QScTL9Xcw0zhPICpRzSRfpxaSyGp8VdNJvyGnAA8RYsarcwpX7WhX\nK20ASccQtWobdlXUFkpV4eo9jo6CotnFDkTv8IY3RMlbM4qIY4/Oz1bmmyKpP5GG+3m9x9JItDuj\nnZk7edY/f+TrNDt54pfJdHyy0c5kGow8oclkOi7ZaGcymUwm00Goe3GVTCaTyWQy80c22plMJpPJ\ndBCy0c5kMplMpoOQjXYmk8lkMh2E9lZcJZPJfAtI6kR0XupLdK2aCgy2fbek7YjmKQNsX5CO3w64\n0Pbm6fORwGHAV0BnIrf7lLTva+B5onvYQkR5z52Inu4ziBrmKxPVsgDG2P75Av+jM5kGIBvtTKaa\njAC6AOvZ/lLSBsB9kor6/ROB4yWNtD05bStq/W9ONFvZ1PaUVJhlg9J3zwB62p7a6v+5STp/O+AC\n2/+yQP6yTKaBye7xTKZiSFod2IfoN/8lgO2XiJ7Yg9Jh7wO3EaWDW7MK0Tu5OZ07w/aLpf0Lpf8y\nmUyNySvtTKZ6bAi8mvpol/kD0YJzRvpvCPCipEtbHXc/0Y/8TUmPAGOJcpXllfX45CZfCHjL9h7f\n/p+RyWRak412JlM95rUKLlpvfiRpJHAmcGux03Yz0FPSpsA2QH/gSEmb2Z6eDmvLPZ7JZBYw2T2e\nyVSPF4C1UjvOMj0JAVmZC4m+2t1bf4ntZ2xfAmwNrAH0KO3O7vFMpg5ko53JVAzbbxHx6hGSOgNI\n6gGcRktMuzh2MtGr/Yxim4Ky8GxdYFHgnQU78kwmMy+yezyTqSb/TqR8vSzpC+Bz4BjbjyV1d5nL\nCbV40YigC3CJpBWAL4DpwE9sF8rzGcwa054B7GL7Lwv0L8pkMrlhSCaTyWQyHYXsHs9kMplMpoOQ\njXYmk8lkMh2EbLQzmUwmk+kgZKOdyWQymUwHIRvtTCaTyWQ6CNloZzKZTCbTQchGO5PJZDKZDkI2\n2plMJpPJdBD+D7YQFBW2pgv7AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "measles_data[measles_data.ONSET<'1997-06-15'].shape",
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "data": {
            "text/plain": "(3698, 14)"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "measles_data[measles_data.ONSET<'1997-07-15'].shape",
      "execution_count": 20,
      "outputs": [
        {
          "execution_count": 20,
          "data": {
            "text/plain": "(11982, 14)"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Age distribution of cases, by confirmation status"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "by_conclusion = measles_data.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\ncounts_by_cause = by_conclusion.size().unstack().fillna(0)\nax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9d0cfdf60>",
            "image/png": 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5tTYDyDbGdDDGOIABXmNERERERESkHAJdzfIe4DVjTGUgDRgMOIFFxphUiqZQ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\nQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Vaccination Data"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\nvaccination_data.head()",
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "data": {
            "text/plain": "       BIRTHS   VAX        POP  SIA  beta_alpha  beta_beta\nYEAR                                                      \n1980  3896442  0.57  121740438  0.0     55.3128    41.7272\n1981  3933136  0.73  124610790  0.0     56.8232    21.0168\n1982  3952137  0.66  127525420  0.0     58.5816    30.1784\n1983  3952735  0.68  130455659  0.0     58.5072    27.5328\n1984  3935224  0.73  133364277  0.0     56.8232    21.0168",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BIRTHS</th>\n      <th>VAX</th>\n      <th>POP</th>\n      <th>SIA</th>\n      <th>beta_alpha</th>\n      <th>beta_beta</th>\n    </tr>\n    <tr>\n      <th>YEAR</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980</th>\n      <td>3896442</td>\n      <td>0.57</td>\n      <td>121740438</td>\n      <td>0.0</td>\n      <td>55.3128</td>\n      <td>41.7272</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>3933136</td>\n      <td>0.73</td>\n      <td>124610790</td>\n      <td>0.0</td>\n      <td>56.8232</td>\n      <td>21.0168</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>3952137</td>\n      <td>0.66</td>\n      <td>127525420</td>\n      <td>0.0</td>\n      <td>58.5816</td>\n      <td>30.1784</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>3952735</td>\n      <td>0.68</td>\n      <td>130455659</td>\n      <td>0.0</td>\n      <td>58.5072</td>\n      <td>27.5328</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>3935224</td>\n      <td>0.73</td>\n      <td>133364277</td>\n      <td>0.0</td>\n      <td>56.8232</td>\n      <td>21.0168</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "alphas, betas = vaccination_data[['beta_alpha', 'beta_beta']].values.T",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Calculate residual susceptibility from routine vaccination"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]\nn = len(vax_97)\nFOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "vacc_susc = (1 - vax_97*0.9)[::-1]\nvacc_susc[0] = 0.5",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Susceptiblity accounting for SIAs"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "sia_susc = np.ones(len(vax_97))\nbirth_year = np.arange(1922, 1998)[::-1]\nby_mask = (birth_year > 1983) & (birth_year < 1992)\nsia_susc[by_mask] *= 0.2",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Compilation of cases into 2-week intervals by age class\n\nAge classes are defined in 5-year intervals. We will combine 40+ ages into a single class."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "age_classes = [0,5,10,15,20,25,30,35,40,100]\nmeasles_data.dropna(subset=['YEAR_AGE'], inplace=True)\nmeasles_data['YEAR_AGE'] = measles_data.YEAR_AGE.astype(int)\nmeasles_data['AGE_GROUP'] = pd.cut(measles_data.YEAR_AGE, age_classes, right=False)",
      "execution_count": 27,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Lab-checked observations are extracted for use in estimating lab confirmation probability."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "CONFIRMED = measles_data.CONCLUSION == 'CONFIRMED'\nCLINICAL = measles_data.CONCLUSION == 'CLINICAL'\nDISCARDED = measles_data.CONCLUSION == 'DISCARDED'",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Extract lab-confirmed and clinical-confirmed subsets, with no missing county information."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lab_subset = measles_data[(CONFIRMED | DISCARDED) & measles_data.COUNTY.notnull()].copy()",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "age = lab_subset.YEAR_AGE.values\nages = lab_subset.YEAR_AGE.unique()\ncounties = lab_subset.COUNTY.unique()\nconfirmed = (lab_subset.CONCLUSION=='CONFIRMED').values",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "clinic_subset = measles_data[CLINICAL & measles_data.COUNTY.notnull()].copy()",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Histogram of lab subset, by outcome."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "_lab_subset = lab_subset.replace({\"CONCLUSION\": {\"CLINICAL\": \"UNCONFIRMED\"}})\nby_conclusion = _lab_subset.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\ncounts_by_cause = by_conclusion.size().unstack().fillna(0)\nax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5), grid=False)",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
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yKcCh2eMxwE0RsToiZgMvAqNSSlsDNRExPaubWrSNJEmSJKkN2nTNXEppCLAb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\nAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lab_subset.shape",
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "data": {
            "text/plain": "(41547, 15)"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Empirical confirmation over time"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "ax = (_lab_subset.groupby(['CONCLUSION', 'ONSET'])\n             .size()\n             .unstack().T\n             .fillna(0)\n             .resample('2W').sum()\n             .apply(lambda x: x.CONFIRMED/x.sum(), axis=1)).plot()\nax.set_xlabel('Time')\nax.set_ylabel('Confirmation rate')",
      "execution_count": 34,
      "outputs": [
        {
          "execution_count": 34,
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fb9ce8e41d0>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9d0a56710>",
            "image/png": 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C8CJwxhizJtDBRPxl5KAUjY9LkFqUn8zSgmROljWP/P8q4itfxsh/APyuZVlF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\nl13z1Y8/BVY3Sr2Sfptjmw58FRgHrAY+KOnXueZ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          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "By age group"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "(_lab_subset.groupby(['CONCLUSION', 'AGE_GROUP', 'ONSET'])\n             .size()\n             .unstack().T\n             .fillna(0)\n             .resample('M').sum()\n             .apply(lambda x: x.CONFIRMED/(x.CONFIRMED + x.DISCARDED), axis=1).fillna(0)\n             .plot(colormap='Reds').legend(loc='center left', bbox_to_anchor=(1, 0.5)))",
      "execution_count": 35,
      "outputs": [
        {
          "execution_count": 35,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x7fb9cecd30b8>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9cebfb908>",
            "image/png": 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aGzTBGDgU2bUP2B3Bq0WpMtSITvBVyqADEDdiGMLnJf+RjwNYkhJQPh+Wh29B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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lab_subset['notification_gap'] = (lab_subset.NOTIFICATION - lab_subset.ONSET).dt.days",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Drop negative values for gap"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lab_subset.loc[lab_subset.notification_gap<0, 'notification_gap'] = np.nan",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lab_subset.notification_gap.hist()",
      "execution_count": 38,
      "outputs": [
        {
          "execution_count": 38,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9cecf1a90>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9ce8ecf60>",
            "image/png": 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KfQOwOSJ2A28By9vTniRJGjVu6GfmPwGzxnj4q2OMWQesa1F/EbiyRf19yosGSZI0Nbwj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\nXwAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Define age groups"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "age_group = pd.cut(age, age_classes, right=False)\nage_index = np.array([age_group.categories.tolist().index(i) for i in age_group])\nage_groups = age_group.categories\nage_groups",
      "execution_count": 39,
      "outputs": [
        {
          "execution_count": 39,
          "data": {
            "text/plain": "Index(['[0, 5)', '[5, 10)', '[10, 15)', '[15, 20)', '[20, 25)', '[25, 30)',\n       '[30, 35)', '[35, 40)', '[40, 100)'],\n      dtype='object')"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "age_slice_endpoints = [g[1:-1].split(',') for g in age_groups]\nage_slices = [slice(int(i[0]), int(i[1])) for i in age_slice_endpoints]",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Get index from full cross-tabulation to use as index for each district"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "dates_index = measles_data.groupby(['ONSET', 'AGE_GROUP']).size().unstack().index",
      "execution_count": 41,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "(lab_subset.YEAR_AGE<2).mean()",
      "execution_count": 42,
      "outputs": [
        {
          "execution_count": 42,
          "data": {
            "text/plain": "0.22454088141141357"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Cleanup of Sao Paulo population data\n\nMatch age groupings, exclude invalid districts."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "unique_districts = measles_data.DISTRICT.dropna().unique()",
      "execution_count": 43,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "excludes = ['BOM RETIRO']\n\nN = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "N_age = N.iloc[:8]\nN_age.index = age_groups[:-1]\nN_age[age_groups[-1]] = N.iloc[8:].sum()",
      "execution_count": 45,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "N_age.plot(kind='bar')",
      "execution_count": 46,
      "outputs": [
        {
          "execution_count": 46,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9cec4a2e8>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9d01d77f0>",
            "image/png": 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zLF+++KDVPaMAkJl7O378BPC5srwbOKpj3arSNll75z4/iIiFwHMyczgidgOnjNvn7sx8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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# All confirmed cases, by district\nconfirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\nconfirmed_counts = (confirmed_data.groupby(['ONSET', 'AGE_GROUP'])\n                    .size()\n                    .unstack()\n                    .reindex(dates_index)\n                    .fillna(0)\n                    .sum())\n\nall_confirmed_cases = (confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n                       .fillna(0).values.astype(int))",
      "execution_count": 47,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "confirmed_data.shape",
      "execution_count": 48,
      "outputs": [
        {
          "execution_count": 48,
          "data": {
            "text/plain": "(22097, 16)"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "confirmed_counts_2w = (confirmed_data\n                        .groupby(['ONSET', 'AGE_GROUP'])\n                        .size()\n                        .unstack()\n                        .reindex(dates_index)\n                        .fillna(0)\n                        .resample('2W')\n                        .sum()\n                        .pipe(check.is_shape, \n                              (28, len(age_groups)))) ",
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# All clinical cases, by district\nclinical_counts = (clinic_subset.groupby(['ONSET', 'AGE_GROUP'])\n                   .size()\n                   .unstack()\n                   .reindex(dates_index)\n                   .fillna(0)\n                   .sum())\n\nall_clinical_cases = (clinical_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n                      .fillna(0).values.astype(int))",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "clinical_counts_2w = (clinic_subset\n                        .groupby(['ONSET', 'AGE_GROUP'])\n                        .size()\n                        .unstack()\n                        .reindex(dates_index)\n                        .fillna(0)\n                        .resample('2W')\n                        .sum()\n                        .pipe(check.is_shape, \n                              (28, len(age_groups)))) \n",
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "confirmed_counts_2w.head()",
      "execution_count": 52,
      "outputs": [
        {
          "execution_count": 52,
          "data": {
            "text/plain": "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\nONSET                                                                           \n1997-01-05     0.0      0.0       0.0       0.0       1.0       0.0       0.0   \n1997-01-19     0.0      1.0       0.0       0.0       3.0       4.0       0.0   \n1997-02-02     4.0      1.0       0.0       0.0       2.0       1.0       0.0   \n1997-02-16     4.0      0.0       0.0       0.0       2.0       1.0       1.0   \n1997-03-02     9.0      0.0       0.0       2.0       4.0       5.0       1.0   \n\nAGE_GROUP   [35, 40)  [40, 100)  \nONSET                            \n1997-01-05       0.0        0.0  \n1997-01-19       0.0        0.0  \n1997-02-02       0.0        0.0  \n1997-02-16       0.0        0.0  \n1997-03-02       0.0        1.0  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>AGE_GROUP</th>\n      <th>[0, 5)</th>\n      <th>[5, 10)</th>\n      <th>[10, 15)</th>\n      <th>[15, 20)</th>\n      <th>[20, 25)</th>\n      <th>[25, 30)</th>\n      <th>[30, 35)</th>\n      <th>[35, 40)</th>\n      <th>[40, 100)</th>\n    </tr>\n    <tr>\n      <th>ONSET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1997-01-05</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-01-19</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-02</th>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-16</th>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-03-02</th>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "clinical_counts_2w.head()",
      "execution_count": 53,
      "outputs": [
        {
          "execution_count": 53,
          "data": {
            "text/plain": "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\nONSET                                                                           \n1997-01-05     3.0      1.0       1.0       0.0       0.0       0.0       0.0   \n1997-01-19    30.0      3.0       1.0       1.0       1.0       3.0       2.0   \n1997-02-02    22.0      4.0       0.0       2.0       1.0       1.0       1.0   \n1997-02-16    21.0      2.0       2.0       2.0       2.0       1.0       1.0   \n1997-03-02    24.0      5.0       2.0       5.0       2.0       2.0       2.0   \n\nAGE_GROUP   [35, 40)  [40, 100)  \nONSET                            \n1997-01-05       0.0        0.0  \n1997-01-19       1.0        0.0  \n1997-02-02       0.0        1.0  \n1997-02-16       0.0        2.0  \n1997-03-02       1.0        0.0  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>AGE_GROUP</th>\n      <th>[0, 5)</th>\n      <th>[5, 10)</th>\n      <th>[10, 15)</th>\n      <th>[15, 20)</th>\n      <th>[20, 25)</th>\n      <th>[25, 30)</th>\n      <th>[30, 35)</th>\n      <th>[35, 40)</th>\n      <th>[40, 100)</th>\n    </tr>\n    <tr>\n      <th>ONSET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1997-01-05</th>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-01-19</th>\n      <td>30.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-02</th>\n      <td>22.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-16</th>\n      <td>21.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1997-03-02</th>\n      <td>24.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Stochastic Disease Transmission Model\n\nWe will extend a simple SIR disease model, to account for confirmation status, which will be fit using MCMC.\n\nThis model fits the series of 2-week infection totals for each age group $a$ as a set of Poisson random variables:\n\n\\\\[Pr(I_{a}(t) | \\lambda_a(t)) = \\text{Poisson}(\\lambda_a(t)) \\\\]\n\nWhere the age-specific outbreak intensity at time $t$ is modeled as:\n\n\\\\[\\lambda_a(t) = S_a(t-1) \\frac{I(t-1)\\mathbf{B}}{N_a} \\\\]\n\nwhere $S_a(t-1)$ is the number of susceptibles in age group $a$ in the previous time period, $I(t-1)$ an age-specific vector of the number of infected individuals in the previous time period, $\\mathbf{B}$ a matrix of transmission coefficients (both within- and between-ages), and $N_a$ an estimate of the population of age-$a$ people in Sao Paulo.\n\nThe matrix $B$ was constructed from a scalar transmission parameter $\\beta$, which was given a vague half-Cauchy prior (scale=25). This was used to represent within-age-group transmission, and hence placed on the diagonal of a square transmission matrix of size $A$. Off-diagonal elements, representing transmission between age groups were scaled by a decay parameter $\\delta$ which was used to scale the transmission to adjacent groups according to:\n\n\\\\[\\beta \\delta^{|a-b|}\\\\]\n\nwhere a and b are indices of two age group. The resulting transmission matrix is parameterized as follows:\n\n$$\\begin{aligned}\n\\mathbf{B} = \\left[{\n\\begin{array}{c}\n  {\\beta} & {\\beta \\delta} & {\\beta \\delta^2}& \\ldots & {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-1}}  \\\\\n  {\\beta \\delta} & {\\beta} &  \\beta \\delta & \\ldots & {\\beta \\delta^{A-3}}  & {\\beta \\delta^{A-2}} \\\\\n  {\\beta \\delta^2} & \\beta \\delta & {\\beta} & \\ldots & {\\beta \\delta^{A-4}} & {\\beta \\delta^{A-3}} \\\\\n  \\vdots & \\vdots & \\vdots &  & \\vdots & \\vdots\\\\\n  {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-3}} & {\\beta \\delta^{A-4}} & \\ldots &  {\\beta} & \\beta \\delta  \\\\\n{\\beta \\delta^{A-1}} & {\\beta \\delta^{A-2}} & \\beta \\delta^{A-3} & \\ldots & \\beta \\delta &  {\\beta} \n\\end{array}\n}\\right]\n\\end{aligned}$$\n\nThe basic reproductive number $R_0$ was calculated as the largest real-valued eigenvalue of the matrix $\\mathbf{B}$. To impose a mild constraint on $R_0$, we applied a Gaussian prior distribution whose 1st and 99th quantiles are 8 and 24, respectively, a reasonable range for a measles outbreak:"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "from pymc import MCMC, Matplot, AdaptiveMetropolis, MAP, Slicer\nfrom pymc import (Uniform, DiscreteUniform, Beta, Binomial, Normal, \n                  CompletedDirichlet, Pareto,\n                  Poisson, NegativeBinomial, negative_binomial_like, poisson_like,\n                  Lognormal, Exponential, binomial_like,\n                  TruncatedNormal, Binomial, Gamma, HalfCauchy, normal_like,\n                  MvNormalCov, Bernoulli, Uninformative, \n                  Multinomial, rmultinomial, rbinomial, runiform,\n                  Dirichlet, multinomial_like, uniform_like)\nfrom pymc import (Lambda, observed, invlogit, deterministic, potential, stochastic, logit)\n\ndef measles_model(obs_date, confirmation=True, migrant=False, constrain_R=True):\n    \n    \n    '''\n    Truncate data at observation period\n    '''\n    obs_index = clinical_counts_2w.index <= obs_date\n    confirmed_obs_t = confirmed_counts_2w[obs_index].values.astype(int)\n    clinical_obs_t = clinical_counts_2w[obs_index].values.astype(int)\n                        \n    n_periods, n_age_groups = confirmed_obs_t.shape\n\n    # Index for observation date, used to index out values of interest \n    # from the model.\n    t_obs = obs_index.sum() - 1\n    \n    lab_index = (lab_subset.NOTIFICATION > obs_date).values\n    confirmed_t = confirmed[lab_index]\n    age_index_t = age_index[lab_index]\n    \n    '''\n    Confirmation sub-model\n    '''\n    \n    if confirmation:\n\n        # Specify priors on age-specific means\n        age_classes = np.unique(age_index)\n\n        μ = Normal(\"μ\", mu=0, tau=0.0001, value=[0]*len(age_classes))\n        σ = HalfCauchy('σ', 0, 25, value=1)\n        var = σ**2\n        ρ = Uniform('ρ', -1, 1, value=0)\n\n        # Build variance-covariance matrix with first-order correlation \n        # among age classes\n        @deterministic\n        def Σ(var=var, cor=ρ):\n            I = np.eye(len(age_classes))*var\n            E = np.diag(np.ones(len(age_classes)-1), k=-1)*var*cor\n            return I + E + E.T\n\n        # Age-specific probabilities of confirmation as multivariate normal \n        # random variables\n        β_age = MvNormalCov(\"β_age\", mu=μ, C=Σ, value=[1]*len(age_classes))\n        p_age = Lambda('p_age', lambda b=β_age: invlogit(b))\n\n        @deterministic(trace=False)\n        def p_confirm(b=β_age):\n            return invlogit(b[age_index_t])\n\n\n        # Confirmation likelihood\n        lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, value=confirmed_t, \n                                observed=True)\n\n    if confirmation:\n        \n        @stochastic(dtype=int)\n        def clinical_cases(value=(clinical_obs_t*0.5).astype(int), \n                            n=clinical_obs_t, p=p_age):\n            # Binomial confirmation process\n            return np.sum([binomial_like(xi, ni, p) for xi,ni in zip(value,n)])\n        I = Lambda('I', lambda clinical=clinical_cases: \n                           clinical + confirmed_obs_t.astype(int))\n\n        assert I.value.shape == (t_obs +1, n_age_groups)\n        \n        age_dist_init = np.sum(I.value, 0)/ float(I.value.sum())\n        \n    else:\n        \n        I = confirmed_obs_t + clinical_obs_t\n\n        assert I.shape == (t_obs +1, n_age_groups)\n        \n        age_dist_init = np.sum(I, 0) / float(I.sum())\n        \n    \n    # Calcuate age distribution from observed distribution of infecteds to date\n    _age_dist = Dirichlet('_age_dist', np.ones(n_age_groups), \n                         value=age_dist_init[:-1]/age_dist_init.sum())\n    age_dist = CompletedDirichlet('age_dist', _age_dist)\n    \n    @potential\n    def age_dist_like(p=age_dist, I=I):\n        return multinomial_like(I.sum(0), I.sum(), p)\n\n    \n    '''\n    Disease transmission model\n    '''\n\n    # Transmission parameter\n    β = HalfCauchy('β', 0, 25, value=5) #[1]*n_age_groups) \n    decay = Beta('decay', 1, 5, value=0.8)\n\n    @deterministic\n    def B(b=β, d=decay):\n        b = np.ones(n_age_groups)*b\n        B = b*np.eye(n_age_groups)\n        for i in range(1, n_age_groups):\n            B += np.diag(np.ones(n_age_groups-i)*b[i:]*d**i, k=-i) \n            B += np.diag(np.ones(n_age_groups-i)*b[:-i]*d**i, k=i)\n        return B\n\n    # Downsample annual series to observed age groups\n    downsample = lambda x: np.array([x[s].mean() for s in age_slices])\n\n    @deterministic\n    def R0(B=B):\n        evs = np.linalg.eigvals(B)\n        return max(evs[np.isreal(evs)])\n    \n    if constrain_R:\n        @potential\n        def constrain_R0(R0=R0):\n            # Weakly-informative prior to constrain R0 to be within the \n            # typical measles range\n            return normal_like(R0, 16, 3.4**-2)\n\n    vax = Beta('vax', alphas[:17], betas[:17], value=vaccination_data.VAX[:17])\n    \n    vax_97 = Lambda('vax_97', lambda vax=vax: np.r_[[0]*(1979-1921+1), vax])\n    \n    n = len(vax_97.value)\n    \n    FOI_mat = Lambda('FOI_mat', lambda vax_97=vax_97: np.resize((1 - vax_97*0.9), (n,n)).T)\n    \n    @deterministic\n    def vacc_susc(vax_97=vax_97):\n        v = (1 - vax_97*0.9)[::-1]\n        v[0] = 0.5\n        return v\n    \n    coverage_residual = Uniform('coverage_residual', 0.2, 0.325)\n    \n    @deterministic\n    def sia_susc(r=coverage_residual):\n        s = np.ones(n)\n        birth_year = np.arange(1922, 1998)[::-1]\n        by_mask = (birth_year > 1983) & (birth_year < 1992)\n        s[by_mask] *= r\n        return s\n\n    A = Lambda('A', lambda R0=R0: 75./(R0 - 1))\n    lt_sum = Lambda('lt_sum', lambda FOI_mat=FOI_mat: downsample(np.tril(FOI_mat).sum(0)[::-1]))\n    natural_susc = Lambda('natural_susc', lambda A=A, lt_sum=lt_sum: np.exp((-1/A) * lt_sum))\n\n    @deterministic\n    def p_μ(v=vacc_susc, n=natural_susc, s=sia_susc): \n        return downsample(s) * downsample(v) * n\n    \n    # Following Stan manual chapter 16.2\n    λ_p = Pareto('λ_p', 1.5, 0.1, value=0.5)\n\n    a = Lambda('a', lambda mu=p_μ, lam=λ_p: mu*lam, trace=False)\n    b = Lambda('b', lambda mu=p_μ, lam=λ_p: (1-mu)*lam, trace=False)\n\n    p_susceptible = Beta('p_susceptible', a, b, value=p_μ.value)\n\n    # Estimated total initial susceptibles\n    if not migrant:\n        S_0_init = (N_age.values*p_μ.value).astype(int) + 30\n        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible, value=S_0_init)\n    else:\n        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible)\n    \n    '''\n    Model of migrant influx of susceptibles\n    '''\n    if migrant:\n    \n        # Data augmentation for migrant susceptibles\n        imaginary_migrants = 1000000\n        N_migrant = DiscreteUniform('N_migrant', 0, imaginary_migrants, value=100000)\n        μ_age = Uniform('μ_age', 15, 35, value=25)\n        σ_age = Uniform('σ_age', 1, 10, value=5)\n        M_age = Normal('M_age', μ_age, σ_age**-2, \n                       size=imaginary_migrants, trace=False)\n\n        @deterministic\n        def M_0(M=M_age, N=N_migrant):\n            # Take first N augmented susceptibles\n            M_real = M[:N]\n            # Drop into age groups\n            M_group = pd.cut(M_real, \n                             [0, 5, 10, 15, 20, 25, 30, 35, 40, 100], \n                             right=False)\n            return M_group.value_counts().values\n\n        p_migrant = Lambda('p_migrant', lambda M_0=M_0, S_0=S_0: M_0/(M_0 + S_0))\n\n        I_migrant = [Binomial('I_migrant_%i' % i, I[i], p_migrant) \n                             for i in range(t_obs + 1)]\n\n        I_local = Lambda('I_local', \n                lambda I=I, I_m=I_migrant: \n                         np.array([Ii - Imi for Ii,Imi in zip(I,I_m)]))\n        S = Lambda('S', lambda I=I, S_0=S_0, M_0=M_0: S_0 + M_0 - I.cumsum(0))\n        S_local = Lambda('S_local', lambda I=I_local, S_0=S_0: S_0 - I.cumsum(0))\n\n\n    else:\n\n        # Remaining susceptibles at each 2-week period\n        S = Lambda('S', lambda I=I, S_0=S_0: S_0 - I.cumsum(axis=0))\n    \n    # Check shape\n    assert S.value.shape == (t_obs+1., n_age_groups)\n    \n    # Susceptibles at time t, by age\n    S_age = Lambda('S_age', lambda S=S: S[-1].astype(int))\n\n    # Force of infection\n    @deterministic\n    def λ(B=B, I=I, S=S): \n        return S * (I.dot(B) / N_age.values)\n\n    # Check shape\n    assert λ.value.shape == (t_obs+1, n_age_groups)\n    \n    \n    # FOI in observation period\n    λ_t = Lambda('λ_t', lambda lam=λ: lam[-1])\n    \n    # Effective reproductive number\n    R_t = Lambda('R_t', lambda S=S, R0=R0: S.sum(1) * R0 / N_age.sum())\n    \n    if migrant:\n        R_t_local = Lambda('R_t_local', lambda S=S_local, R0=R0: S.sum(1) * R0 / N_age.sum())\n    \n    # Poisson likelihood for observed cases\n    @potential\n    def new_cases(I=I, lam=λ):\n        return poisson_like(I[1:], lam[:-1])\n    \n    '''\n    Vaccination targets\n    '''\n    \n    # Probability of vaccine efficacy\n    p_efficacy = 0.95\n    # Reach of campaign\n    p_reach = runiform(0.75, 0.95)\n    \n    @deterministic\n    def vacc_5(S=S_age):\n        # Vaccination of 5 and under\n        p = [p_reach] + [0]*(n_age_groups - 1)\n        return rbinomial(rbinomial(S, p), p_efficacy)\n    \n    # Proportion of susceptibles vaccinated\n    pct_5 = Lambda('pct_5', \n                lambda V=vacc_5, S=S_age: V.sum()/S.sum())\n\n\n    @deterministic\n    def vacc_15(S=S_age):\n        # Vaccination of 15 and under\n        p = [p_reach]*3 + [0]*(n_age_groups - 3)\n        return rbinomial(rbinomial(S, p), p_efficacy)\n    \n    # Proportion of susceptibles vaccinated\n    pct_15 = Lambda('pct_15', \n            lambda V=vacc_15, S=S_age: V.sum()/S.sum())\n    \n    @deterministic\n    def vacc_30(S=S_age):\n        # Vaccination of 30 and under\n        p = [p_reach]*6 + [0]*(n_age_groups - 6)\n        return rbinomial(rbinomial(S, p), p_efficacy)\n    \n    # Proportion of 30 and under susceptibles vaccinated\n    pct_30 = Lambda('pct_30', \n            lambda V=vacc_30, S=S_age: V.sum()/S.sum())\n    \n    @deterministic\n    def vacc_adult(S=S_age):\n        # Vaccination of adults under 30 (and young kids)\n        p = [p_reach, 0, 0, 0, p_reach, p_reach] + [0]*(n_age_groups - 6)\n        return rbinomial(rbinomial(S, p), p_efficacy)\n    \n    # Proportion of adults under 30 (and young kids)\n    pct_adult = Lambda('pct_adult', \n            lambda V=vacc_adult, S=S_age: V.sum()/S.sum())\n\n    return locals()",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model execution\n\nRun models for June 15 and July 15 observation points, both with and without clinical confirmation."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "n_iterations = 50000\nn_burn = 40000\nmigrant = True",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use `backgroundjobs` to run the models each in their own thread:"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from IPython.lib import backgroundjobs as bg\n\njobs = bg.BackgroundJobManager()",
      "execution_count": 56,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Instantiate models"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june = MCMC(measles_model('1997-06-15', confirmation=True, migrant=migrant))\nmodel_july = MCMC(measles_model('1997-07-15', confirmation=True, migrant=migrant))\nmodel_june_noconf = MCMC(measles_model('1997-06-15', confirmation=False, migrant=migrant))\nmodel_july_noconf = MCMC(measles_model('1997-07-15', confirmation=False, migrant=migrant))",
      "execution_count": 57,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Run models"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for model in model_june, model_july, model_june_noconf, model_july_noconf:\n    jobs.new(model.sample, n_iterations, n_burn, kw=dict(progress_bar=False))",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Starting job # 0 in a separate thread.\nStarting job # 2 in a separate thread.\nStarting job # 3 in a separate thread.\nStarting job # 4 in a separate thread.\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "jobs.status()",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Completed jobs:\n0 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb9ceb3ce48>>\n2 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb9d00ab438>>\n3 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb9d0039400>>\n4 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb9d0045550>>\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Summary of model output\n\nEstimate of R0 for june (with confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "if model_june.R0.value.shape:\n    Matplot.summary_plot(model_june.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_june.R0)",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Plotting R0\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9110fb940>",
            "image/png": 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NtmZ6dmEfOHKMu+Y3nkTUm78sWEVhaSVzZ57id79QxyXuk842tRxGYqxlG/bx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\nSrJEREREIvD/MD6hhEfFWT8AAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for june (no confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "if model_june.R0.value.shape:\n    Matplot.summary_plot(model_june_noconf.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_june_noconf.R0)",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Plotting R0\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9ce8a2128>",
            "image/png": 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9lQfD/nIqq6xjYhjvU4e8LI925OR4vm6zn1vMIzMn+t1+3IjufB6k0Kivum1F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\nqQtMsiRJkrrAJEuSJKkL/h/M4lqnkERHUAAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for july (with confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "if model_july.β.shape:\n    Matplot.summary_plot(model_july.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_july.R0)",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Plotting R0\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910fa6f28>",
            "image/png": 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vSfiPRNu27XjggYeZO/dzrrzyKu699++UlR1F/gQQouFJQGYzkTzQwj40wIEfLd7C/y2v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\nmSRJUsVMyCRJkir2n1lZaxSxuEXXAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for july (no confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "if model_july_noconf.β.shape:\n    Matplot.summary_plot(model_july_noconf.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_july_noconf.R0)",
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Plotting R0\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910e42b70>",
            "image/png": 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TTVwEJtCdPrt+AuGde8psjasLVr/lUEIvET6njT2a9+1KfjxQt2TZ5xvtr0ma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\nSUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Lab confirmation rates, June model"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n\n    p_age = pd.DataFrame(model_june.p_age.trace(), columns=age_groups)\n\n    f, axes = plt.subplots(figsize=(14,6))\n    sns.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n              color=sns.color_palette(\"coolwarm\", 5)[0],\n              order=age_group.categories)\n    axes.set_ylabel('Proportion lab positive')\n    axes.set_xlabel('Age interval (years)')",
      "execution_count": 103,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910c25978>",
            "image/png": 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D0YgRI5SZmamuXbvKYrF4rjmSJIvFou+//z4gBQIAAKD6soU3U1h4RLDLAMrFazjKzMyU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\nRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Proportion of **local** population susceptible, June model."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.p_susceptible, custom_labels=age_groups)",
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9ce8c2ba8>",
            "image/png": 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JUnEGjAJ8FokkSTMZMCRJUnEGDEmSVJwBQ5IkFddot2BEbATuBe7JzM0RcSDw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\nkiSpOAOGJEkqzoAhSZKKM2BIkqTi/n9LTPe201JG+wAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Proportion of **local** population susceptible, June model with no confirmation correction"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june_noconf.p_susceptible, custom_labels=age_groups)",
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910debeb8>",
            "image/png": 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Nzk/JkiRJtTOASJKk2hlAJElS7QwgkiSpdgaQDti8eStDQ0PdHoYkSfOGAUSS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\nSaqdAUSSJNXOACJJkmpnAJEkSbX7/374JRPm4foZAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Epidemic intensity estimates at June or July observation time, by age group."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.λ_t, custom_labels=age_groups)",
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910e89550>",
            "image/png": 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P8tyJmIsiMz+w2MeUJEnN+c1KkiSpOAOGJEkqzoAhSZKKM2BIkqTiDBgd2rJlExs2bOh2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\n4gwYkiSpOAOGJEkqzoAhSZKK+/8Bkb9ASz1LX9wAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_july.λ_t, custom_labels=age_groups)",
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910f61400>",
            "image/png": 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iYcCQJEnFGTAkSVJxBgxJklScAUOSJBVnwJAkScUZMArwWSSSJI1mwJAkScUZ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\n820AAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Time series of epidemic intensities for lab- versus clinical-confirmation models, for each age group."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june.λ_t.trace().std()",
      "execution_count": 108,
      "outputs": [
        {
          "execution_count": 108,
          "data": {
            "text/plain": "90.823122374204431"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lam_june = model_june.λ_t.stats()",
      "execution_count": 109,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "lam_june = model_june.λ.stats()\n\nfig, axes = plt.subplots(2, 1, sharey=True)\n\naxes[0].plot(lam_june['quantiles'][50], 'b-', alpha=0.4)\naxes[0].set_ylabel('Epidemic intensity')\naxes[0].set_xlabel('time (2-week periods)')\naxes[0].set_title('Lab confirmation')\n\nlam_june_noconf = model_june_noconf.λ.stats()\n\naxes[1].plot(lam_june_noconf['quantiles'][50], 'b-', alpha=0.4)\naxes[1].set_ylabel('Epidemic intensity')\naxes[1].set_xlabel('time (2-week periods)')\naxes[1].set_title('Clinical confirmation')\n\nplt.tight_layout()",
      "execution_count": 110,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910b1bb70>",
            "image/png": 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w5JNu9tJ116ULHMGIGFDIAF8FPoRL9mitahiGYRjD4MCBjBPvVVdlLC6+Dw895Kw0ubOX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\nMgLPPKO5jzxPLSs33DC2/8rp0x6PPJJhcFBjyNxxR47aWovKuxAoR8j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Wv0ZGEpFQVaUC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GxC78nvich/nelyzVUik/0J59y2aNWrMCfqySDAbc65Guech9anOU9PjGJL6/3AO6LlXwCsIzgx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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Uncertainty in susceptibility\n\nPlots of susceptibility parameters for June model with confirmation."
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.vax, main='Vaccination coverage by year')",
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910c4d7f0>",
            "image/png": 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nAl0Y+1qPfvV1UMLHmRZXM3h2dq6uOmoxPj7KzMyxZe/bt69wMWvMcr3tdNax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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# SIA susceptibility\nMatplot.summary_plot(model_june.sia_susc)",
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb91106f9b0>",
            "image/png": 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PH5uEck8WlgKr3H1iD5mFWcAh+esTSKHI59z9bmAKCjgKIYQQdSnzMsRaesgs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\nRK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# Vaccination susceptibility\nMatplot.summary_plot(model_june.vacc_susc)",
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9110eab70>",
            "image/png": 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r9CgiIpJ002/OtYqbYOWlh14avAwhIiLSCipuClSruCk71cyOB+4CilNXcVMV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\niIjUpQWDiIiI1PX/A78brW/0QZw7AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.natural_susc)",
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9cc1acef0>",
            "image/png": 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29jA+frjtdZWsv7/PHrbJHravG3u4e3dr40dGetm2bS2Tkz3UatMMDU1Qr0819d7x8YVf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\niMEvSVJBDH5Jkgry/wHBBUXJ6FMhCQAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n                          model_june.R_t_local.trace()[:, -1]],\n                        columns=['June, total', 'June, local'])\n\nax = Rt_values.boxplot(return_type='axes');\nax.set_ylabel('R(t)')",
      "execution_count": 115,
      "outputs": [
        {
          "execution_count": 115,
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x7fb9cc1cb898>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910d8fba8>",
            "image/png": 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7Msln9hXxJNm9++EBRmVSXNkNDs4vvvXT0x4hRz1l3vY7JWu9gRo05FX1eGvtoiQ35Ed/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\n5ADQMSEHgI79X+xN27szjL9NAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n    \n    Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n                              model_june_noconf.R_t.trace()[:, -1],\n                             model_july.R_t.trace()[:, -1],\n                             model_july_noconf.R_t.trace()[:, -1]],\n                            columns=['June\\nage confirmation', 'June\\nclinical only',\n                                    'July\\nage confirmation', 'July\\nclinical only'])\n\n    ax = Rt_values.boxplot(return_type='axes', figsize=(14,6), grid=False);\n    ax.set_ylabel('R(t)')",
      "execution_count": 116,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910f09438>",
            "image/png": 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wnqMBn5ub2+r4fv36aceOHU6WAABAUOIqegAALETABxD6MgcA+IuADyD0ZQ4A8BcBDwCA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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "S_age_june = pd.DataFrame(model_june.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june.columns = 'Age', 'Iteration', 'S'\nS_age_june['Confirmation'] = 'Lab'\nS_age_june['Month'] = 'June'\n\nS_age_june_noconf = pd.DataFrame(model_june_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_june_noconf['Confirmation'] = 'Clinical'\nS_age_june_noconf['Month'] = 'June'\n\nS_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)\nS_age_june.to_csv('output/S_age_june.csv')",
      "execution_count": 117,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "S_age_june_local = pd.DataFrame(model_june.S_local.trace()[:,-1,:].squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_local.columns = 'Age', 'Iteration', 'S'\nS_age_june_local['Confirmation'] = 'Lab'\nS_age_june_local['Month'] = 'June'\n\nS_age_june_local_noconf = pd.DataFrame(model_june_noconf.S_local.trace()[:,-1,:].squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_local_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_june_local_noconf['Confirmation'] = 'Clinical'\nS_age_june_local_noconf['Month'] = 'June'\n\nS_age_june_local = pd.concat([S_age_june_local, S_age_june_local_noconf], ignore_index=True)\nS_age_june_local.to_csv('output/S_age_june_local.csv')",
      "execution_count": 118,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "S_age_july = pd.DataFrame(model_july.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_july.columns = 'Age', 'Iteration', 'S'\nS_age_july['Confirmation'] = 'Lab'\nS_age_july['Month'] = 'July'\n\nS_age_july_noconf = pd.DataFrame(model_july_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_july_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_july_noconf['Confirmation'] = 'Clinical'\nS_age_july_noconf['Month'] = 'July'\n\nS_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)\nS_age_july.to_csv('output/S_age_july.csv')",
      "execution_count": 119,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "S_age = pd.concat([S_age_june.assign(susceptibles='Migrant + Local'), \n                   S_age_june_local.assign(susceptibles='Local only')], ignore_index=True)\nS_age.to_csv('output/S_age.csv')",
      "execution_count": 120,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Numbers of suscepibles in each age group, under lab vs clinical confirmation"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "sns.despine()\n\nwith sns.axes_style(\"ticks\"):\n    \n    g = sns.factorplot(\"Age\", \"S1000\", \"Confirmation\", data=S_age.assign(S1000=S_age.S/1000), \n                      kind=\"box\", palette=\"hls\", row='susceptibles', size=6, aspect=2, linewidth=0.3, fliersize=0, \n                      order=age_group.categories, legend=False, facet_kws={'ylim':(0, 250)})\n    g.despine(offset=10, trim=True)\n    g.set_axis_labels(\"Age Interval (years)\", \"Susceptibles (1000's)\")\n    plt.tight_layout();",
      "execution_count": 121,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9109fb7f0>"
          },
          "output_type": "display_data",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb91839a0b8>",
            "image/png": 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ORC0AAAAAUC94vUYKAAAAAFA7ghQAAAAA2OR3ah8AIHCmacrtdnvdX1RU5LcN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\nAJhEkAIAAAAAkwhSAAAAAGAS90jVIpWZlc+bvLy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69Wrdc889kqTP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\nAAAAJv0/S6jYwKfx/I0AAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Vaccination coverage by strategy"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.166            0.006            0.0              [ 0.156  0.175]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.156            0.164           0.165          0.172         0.181\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.256            0.009            0.0              [ 0.241  0.268]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.241            0.252           0.254          0.264         0.277\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.732            0.002            0.0              [ 0.729  0.736]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.729            0.731           0.732          0.734         0.737\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.516            0.001            0.0              [ 0.514  0.519]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.513            0.515           0.517          0.517         0.519\n\t\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "june_coverage = pd.DataFrame({name: model_june.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njune_coverage['Month'] = 'June'\njune_coverage['Confirmation'] = 'Lab'",
      "execution_count": 123,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "june_noconf_coverage = pd.DataFrame({name: model_june_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njune_noconf_coverage['Month'] = 'June'\njune_noconf_coverage['Confirmation'] = 'Clinical'\n\njuly_coverage = pd.DataFrame({name: model_july.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njuly_coverage['Month'] = 'July'\njuly_coverage['Confirmation'] = 'Lab'\n\njuly_noconf_coverage = pd.DataFrame({name: model_july_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njuly_noconf_coverage['Month'] = 'July'\njuly_noconf_coverage['Confirmation'] = 'Clinical'",
      "execution_count": 124,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], \n                     ignore_index=True)\n\ncoverage.to_csv('output/coverage.csv')",
      "execution_count": 125,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "sns.factorplot(row=\"Month\", col=\"Confirmation\", data=coverage, kind='box',\n              row_order=['June', 'July'],\n              order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'],\n               palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)",
      "execution_count": 126,
      "outputs": [
        {
          "execution_count": 126,
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7fb958e1f400>"
          },
          "output_type": "execute_result",
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb958e1f4e0>",
            "image/png": 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jga0UoXfJ2EERMQt4EcXVJpoaGdk9gTIlqVoGBwcmvI59VZLqa9RXm06ZyMx9wJnADcAt\nwJrMvDUilkXEGTVDXwVcn5n3tVmvJEmSdMB462ZJ6kFedk2SOstbN0uSJEl1GIglSZJUaQZiSZIk\nVZqBWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqB\nWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYkSVKl9Xe7AEmSJHXX6tVXsn79ukmv\nv3HjRoaGhtqqYcGCY1m6dFlb25isvtHR0a7seHh4V3d2LEnTwODgQN9E17GvStW0/LxzuXvnzq7W\nsP1nW5n75Kd0tYbDZ81ixXsvrLu8UV/1CLEkSdI0tumnd7Bjx46u1vDAnvvZc999Xa3hF7NnT3pd\njxBLUg/yCLGk6WT58rNYseKSbpfRUKO+aiCWpB5kIJZ0IFVhDnHbgTgiFgGrKK5KsTozV44z5neA\nvwIOAYYz88WNtmnjlqT6DMSS1FmN+mrTy65FxAzgCuAU4HhgSUQcN2bMLOCvgZdn5gnAa9uqWJIk\nSTpAWrkO8UJgXWZuyMy9wBrg1DFjXgd8KjO3AGTm9s6WKUmSJE2NVq4yMQ/YVPN4M0VIrvV04JCI\n+CrwJODyzLy2MyVKkiRJU6dTl13rB04EXgI8EfhmRHwzM39Sb4U5cw6jv39mh3YvSbKvStLktBKI\ntwC1pw3OL5+rtRnYnpn3A/dHxNeA5wB1A/HIyO4JlipJ1TE4ODDhdeyrklRfo77aSiBeCxwTEUcD\nW4HFwJIxY64DPhgRM4HHA88DPjCpaiVJkqQDqOlJdZm5DzgTuAG4BViTmbdGxLKIOKMccxtwPfBD\n4FvAVZn546krW5IkSeoMb8whST3I6xBLUme1dR1iSZIk6WBmIJYkSVKlGYglSZJUaQZiSZIkVZqB\nWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIk\nSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYkSVKlGYglSZJUaf3dLkCSJmr16itZv37d\npNffuHEjQ0NDbdWwYMGxLF26rK1tSJJ6Q9/o6GhXdjw8vKs7O5bUVcvPO5e7d+7sag3bf7aVuU9+\nSldrOHzWLFa898K6ywcHB/omuk37qiTV16ivtnSEOCIWAasopliszsyVY5a/CLgOuL186tOZecHk\nypV0MNv00zvYsWNHV2t4YM/97Lnvvq7W8IvZs7u6f0nSI5oG4oiYAVwBvBS4E1gbEddl5m1jhn4t\nM185BTVKOohc+5E1bW/DKROSpE5q5QjxQmBdZm4AiIg1wKnA2EA84a/3JGkyDKKSpE5qJRDPAzbV\nPN5MEZLH+s2I+D6wBXhXZv64A/VJkiRJU6pTl137DjCUmc+lmF7xmQ5tV5IkSZpSrRwh3gLUTrab\nXz73sMy8t+bnL0TEhyLi8My8u95G58w5jP7+mROtV5JUh31VkianlUC8FjgmIo4GtgKLgSW1AyLi\nqMz8efnzQqCvURgGGBnZPbmKJakCBgcHJryOfVWS6mvUV5sG4szcFxFnAjfwyGXXbo2IZcBoZl4F\n/GFEvBXYC9wHnNaRyiVJkqQp5o05JKkHeWMOSeqsRn21UyfVSZIkSdOSgViSJEmVZiCWJElSpRmI\nJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmS\nVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkG\nYkmSJFWagViSJEmV1t/KoIhYBKyiCNCrM3NlnXEnA98ATsvMT3esSkmSJGmKND1CHBEzgCuAU4Dj\ngSURcVydcRcB13e6SEmSJGmqtDJlYiGwLjM3ZOZeYA1w6jjj3gZ8EtjWwfokSZKkKdVKIJ4HbKp5\nvLl87mER8VTgVZn5N0Bf58qTJEmSplZLc4hbsAo4p+Zx01A8Z85h9PfP7NDuJUn2VUmanFYC8RZg\nqObx/PK5WicBayKiD5gL/F5E7M3Mz9bb6MjI7onWKkmVMTg4MOF17KuSVF+jvtpKIF4LHBMRRwNb\ngcXAktoBmfmr+3+OiKuBf24UhiVJkqRe0XQOcWbuA84EbgBuAdZk5q0RsSwizhhnldEO1yhJkiRN\nmb7R0e7k1+HhXQZnSapjcHBgwico21clqb5GfdU71UmSJKnSDMSSJEmqNAOxJEmSKs1ALEmSpEoz\nEEuSJKnSOnWnOkmS1KLVq69k/fp1bW1j48aNDA0NNR9Yx4IFx7J06bK2apAOFl52TZJ6kJddUzPL\nl5/FihWXdLsMadrwsmuSJElSHQZiSZIkVZqBWJIkSZVmIJYkSVKlGYglSZJUaV5lQpJ6kFeZ6G1n\nLT+bbXeNdLWGndt/zqy5R3W1hiOPmMMlK97f1RqkVjXqq16HWJKkCdp21wjb5j2vu0XMg23drQC2\n/Hu3K5A6wikTkiRJqjSPEEuSNEE7t/+cvXd9qdtldN3O0fu6XYLUEQZiSZImaNbco9jT7SkTPWCW\nUyZ0kHDKhCRJkirNI8SSJE3QkUfM6foJZb1ylQnpYOBl1ySpB3nZNTWzfPlZrFhxSbfLkKaNRn3V\nKROSJEmqtJamTETEImAVRYBenZkrxyx/JXA+8BCwDzg7M7/S4VolSZKkjmt6hDgiZgBXAKcAxwNL\nIuK4McNuzMznZOavAW8Crup4pZIkSdIUaGXKxEJgXWZuyMy9wBrg1NoBmbm75uGTgO2dK1GSJEma\nOq1MmZgHbKp5vJkiJD9KRLwKuBB4MsXRZEmSJKnndeyya5n5GeAzEfEC4FogOrVtSZIOJqtXX8n6\n9eva2sbGjRtZvvysSa+/YMGxLF26rK0apINFK4F4CzBU83h++dy4MvPrEdEfEUdk5l31xs2Zcxj9\n/TNbr1SS1JB9dfp497snH2QldV4rgXgtcExEHA1sBRYDS2oHRMSCzFxf/nwiQKMwDDAysrvRYkmq\ntMHBgQmvY1+VpPoa9dWmgTgz90XEmcANPHLZtVsjYhkwmplXAa+JiDcADwC/AE7rSOWSJEnSFPNO\ndZLUg7xTnSR1lneqkyRJkuro2FUmdPDq1NnQQ0NDzQfW4dnQkiRpqjhlQgfE8uVnsWLFJd0uQ5o2\nnDIhSZ3llAlJkiSpDgOxJEmSKs1ALEmSpEpzDnEFnLX8bLbdNdLVGnZu/zmz5h7V1RqOPGIOl6x4\nf1drkFrVq3OI2z3Jtt0TbMGTbCVNTqO+6lUmKmDbXSNsm/e87hYxD7Z1twLY8u/drkCa9toNop5g\nK6kXOWVCkiRJlWYgliRJUqU5ZaICHtx9DzP+4+NdrWHv3j0ccsjju1rDg3PmdHX/kiSpNxmIK+Af\nr/2nbpfgvEFJktSzDMSSVBHvOuedbNu+vas17Lx7O6cvPb2rNRw5dy4Xr7y0qzVI6i0GYkmqiG3b\nt7Nt5uzuFjE4u/tXnOnyhwJJvcdALEkVsfPu7ezd2/U42nU7D/F8ckmPZiCWpIqYdfhc9nT7CHEP\nmLVvR7dLkNRjDMRqqt07U0Fxd6rly8+a9PremUpq35Fz53Z9usDOu7cz6/C5Xa3hyLnd3b+k3uOt\nmyWpB/XqrZvb5RVnJHVLo77qRCpJkiRVmoFYkiRJlWYgliRJUqU5h1iSelCvziFu9yTbjRs3MjQ0\n1FYNnmQraTIa9dWWAnFELAJWURxRXp2ZK8csfx1wTvlwF/DWzPxRo20aiCWpvl4NxJI0XbV1Ul1E\nzACuAE4BjgeWRMRxY4bdDrwwM58DXAB8ePLlSpIkSQdOK9chXgisy8wNABGxBjgVuG3/gMz8Vs34\nbwHzOlmkJEmSNFVaOaluHrCp5vFmGgfePwG+0E5RkiRJ0oHS0TvVRcSLgTcBL2g2ds6cw+jvn9nJ\n3UtSpdlXJWlyWgnEW4DaU4Lnl889SkQ8G7gKWJSZI802OjKyu9UaJalyBgcHJryOfVWS6mvUV1sJ\nxGuBYyLiaGArsBhYUjsgIoaATwF/nJnrJ1+qJEmSdGA1nUOcmfuAM4EbgFuANZl5a0Qsi4gzymHv\nAQ4HPhQR34uI/5iyiiVJkqQO8sYcktSDvA6xJHVWo77a0ZPqeo13VJIkSVIzHiFuYPnys1ix4pJu\nlyGpgjxCLEmd1dad6iRJkqSDWc8eIX7XOe9k2/btB6qcce28ezuzDp/b1RqOnDuXi1de2tUaJB14\nHiGWpM6alnOIt23fzraZs7tbxOBstnW3AujyhwJJkqSDnVMmJEmSVGk9e4R47/276dvxmBviHVAP\n7t1L/yGHdLWGvbPndHX/kiRJB7uenUPcC7zKhKRucQ6xJHWWV5mQJEmS6jAQS5IkqdIO6ikT3qlO\n0nTllAlJ6qxGffWgDsSSNF0ZiCWps5xDLEmSJNVhIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVm\nIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZXW38qgiFgErKII0Kszc+WY5QFcDZwILM/MD3S6UEmS\nJGkqND1CHBEzgCuAU4DjgSURcdyYYXcBbwMu7niFkiRJ0hRqZcrEQmBdZm7IzL3AGuDU2gGZuT0z\nvwM8OAU1SpIkSVOmlUA8D9hU83hz+ZwkSZI07XlSnSRJkiqtlZPqtgBDNY/nl8+1ZXBwoK/dbUiS\nHmFflaTJaSUQrwWOiYijga3AYmBJg/E2ZEmSJE0bfaOjo00HlZddu4xHLrt2UUQsA0Yz86qIOAr4\nNjAAPATcCzwzM++dutIlSZKk9rUUiCVJkqSDlSfVSZIkqdIMxJIkSao0A7EkSZIqzUAsSZKkSjMQ\nS5IkqdIMxJIkSao0A7EkSZIqzUAsSZKkSjMQS5IkqdIMxJIkSao0A7EkSZIqzUAsSZKkSjMQS5Ik\nqdIMxJIkSao0A7EkSZIqrb/bBeixIuKnwCHA/MwcLZ97I/B/gDMz80NtbPt04KbM/EnN45dn5mvb\nLLve/h4CnpSZuzsxbsw65wF3ZOZH6yx/PfBO4AnAL4B1wNmZubnVfYyzzauBk4CbgX8Afjszz5ns\n9lrY3yzgjMy8uOa5DwPXZOZNU7XfFuo6D3hiZp49gXW+ClycmZ+fuspUNfbLlrdtv+yiiDgFeA8w\nCOwEfgacm5m31P4+I+JzwNsy844m22vrddmPH8tA3JtGgTuBU4Avls+dDnynA9t+IzAM/GTM/qZK\nq9vuaA0R8SfAnwGvzMzby+deCDwZmFSDj4ijgFdn5qyapz9XZ+yMzHxoMvsZYw5wNvBwg8/Mt3Rg\nu9LBwn7ZJvvl1IqI/wJ8GDg1M79XPvds4CnALdT8PjPz5a1ssxde18HGQNy7rgHeBHwxIp4GPBH4\n0f6FEfFE4IPAyRT/MV27/1Nx+clvLfCbFP/BfTwzl5dHTU4CLo+IC4Czys3Niog1wAnACPCazNzW\nodfRV1Pzo45qjHeUIyL+EHjj/qYQEY8DfgosnOCRir8E3ry/uQNk5tdq9vMGitf/ELAeWJaZ28sj\nQK+jeB8efj+A3cBXgEMj4rvAR4AdlEeLIuJFwOUU/wg/F/iLiHgtsAc4FvhV4DqKfxDOA+YBqzLz\n8rKei4EXAo8Dtpe1bwKuoPj9fBfYnZkvqP1kHxFHAn8LLChf2iWZeW25zTuAjwK/S/EP26WZ+dcT\neA8nLCJeAlwAPJ6iv6zIzH+qGfK75ZGqOcAnMvN/TmU9qoxrsF/aL3u3X74H+N/7wzBAZv6wZnnt\n7/0O4A8y88fj/G1+IjPPLcfVvq5fAv6K4u97H/D/MvPtLfRj1XAOcW8aBf4VOKH8Cuh0iobSVzPm\nL4G+zHwW8Hzg9PIrmf1+OTN/GzgReEtELMjMa4BvA2/PzBMz8yvl2JOAP8/ME4BbgbeNV1REXBYR\n363zv6e1+LrqPd7/2v4vcHxEHF0+Pg345kSae0QMUjTQ/6iz/HjgQuBlmflcik/oV9QMecz7kZn3\nAr8P7Cjfu8vGeQ3PBP62XP75mudOKf9/CbCk/L28AHhfRBxWjrswM5+Xmb8GrAHeXz7/pzX7fME4\nL+dy4EeKuhIuAAAccElEQVSZ+ZxyPxdFxDNrlh+amb8FvLhcdtjYDUTEMyLie3V+ryvHew8b+A7w\n/Mz8dYp/WC4p/4b3ewbwG8CvAa+IiN+f4PalseyX9ste75cnUuf9bUHt3+afRMSCccasAu7NzGeX\n78n/Kp9v1o9VwyPEvamPonF8nKIpnAb8FkXj2e+lwNsBMnNXRHwMeBlwfbn8E+WyeyLiVopPxOvr\n7O+mzLyz/Plb5XYeIzPfMdkXVOpr8pjM3BcRVwL/DTgX+O9Ap48ivhj4l5qjOlcC369Z3tL7MY51\nmTm26X0mMx8EHoyIBP4FIDPvjIi7gfnAfwJ/EBH/HXgSxX+XrX4l+jLgz8tt/iwiPl++vh+Xy9eU\nyzaM2d/DMvNWioDaCUcCV0fEscCDFEeCg0f+MfhIFvM8f1EeZXsJ4Bw2tcN+ab+crv2yFa38bb68\ntqbMvLv8sVk/Vg0DcW/7KPDvwL9m5khETGTd+2t+3kfj33VLYyPiMoqvqcYapfjasOFJAOW2Z5Tb\negKPbmK1P38Y+G5E/DMwq+bITEsyczgitgALgRtbWGXsPzQTee9q3TvOc2O3Vfv4IaA/IoaADwC/\nnpkbI+I3KU5AaUWzfwges7+xAyLiGcA/ltuqfS9GgS/lxE6C+Rvgusx8dbntpDhJR5pq9kv7ZTPd\n6pffoXh/fzjOsmZaeX/H1rKf/XgCDMQ9LDPviIjljP9p7kZgKfCNiBgAFlN+8m3iHmBSX5l04IjH\nOoo5Tl+lmHdW6+H/mDPzroj4MsWn9Yl+Zb/fBcAHIuJV+chJIr8N3Ffu/90RcWR51OMtwJda3O54\nTaddv0Qxd+7nETEDeGvNsnuAwyJiZmbuG2fdGynqf29EPBn4PeDSiey8jSMe470XsyjmMBIRvwsc\nM2b5H0XExyma8n8Flk9iv9Jj2C/tl/Ruv3wfcFVEfCcfOanuWcBRmdnKh5BmPge8C3hHue0jMvMu\nmvdj1XAOcW+qPeP072om39d+uj0fmBERPwJuovgq+kvjjBv7+CrgvHK+00s6XPejRMRMik/ZD5RP\nvZOiKawF5jaoEeDvgNkUR30mLDOvophX9smI+HFE3EzxteLWzLwFeDdwY0R8H3gWZSNpwUTO7m70\ne3j4cWbeDHySYv7dN6n5OiwzRyiOfvwoIr4+znbeATw3In5A8fXvOZl5W4v7b9cZEbExIjaV//8W\nivf10ihOavlD4Adj9n8b8A3ge8A/p5f8Ufvsl/bL/a+jJ/tlZl4PLAP+OiJuK/8O30dxdZSx+6r3\nc6Nlfw78UkTcHBHfoziJD4ppNI36sWr0jY42f08iYhHFpO0ZwOrMXDlm+WyKaz4uoPhE+ebM/PFj\nNqRKiYiTgTWZOd5JAM3W/QuKT8/jnrBSjml4XU1Jmi7sl1J3NZ0yUX4lcQXFSQl3Amsj4rqaT1VQ\nfO35vcx8dRQTt/6a1ifW6yAUERdRTPSv26AbrHszsJfiLGBJOqjZL6Xua2UO8UKKs0E3AJRnhp9K\n8dXnfs+kuCwLmZkR8SsRMZiZw50uWNNDZr6b4mu2yax7QotD/cpH0rRnv5S6r5VAPA/YVPN4M0VI\nrvUD4NXATRGxEBiiuFyJgVhTJjP/d7drkKTpwH4pNdapq0xcBFxWTtz+EcUJM+Od4fmwBx/cN9rf\nP7NDu5ekg86Ez9C3r0pSQ3X7aiuBeAvFEd/95pfPPSwzdwFv3v84ilsP3k4DIyO7Gy2WpEobHByY\n8Dr2VUmqr1FfbSUQrwWOieLWkFsprt+4pHZAeSvA3Zm5t7z00r+Vt26UJEmSelrT6xCXF7c+E7iB\n4h7mazLz1ohYFhFnlMOeAdxc3lbwFFq/RqEkSZLUVS1dh3gqDA/v8oxXSapjcHBgwnOI7auSVF+j\nvuqd6iRJklRpBmJJkiRVmoFYkiRJlWYgliRJUqUZiCVJklRpBmJJkiRVmoFYkiRJlWYgliRJUqUZ\niCVJklRpBmJJkiRVmoFYkiRJlWYgliRJUqUZiCVJklRpBmJJkiRVmoFYkiRJlWYgliRJUqUZiCVJ\nklRpBmJJkiRVmoFYkiRJlWYgliRJUqUZiCVJklRp/a0MiohFwCqKAL06M1eOWX4E8PfAU4CZwKWZ\neU1nS5UkSZI6r+kR4oiYAVwBnAIcDyyJiOPGDDsT+H5mPhd4MXBpRLQUtiVJkqRuamXKxEJgXWZu\nyMy9wBrg1DFjfgYMlD8PAHdl5oOdK1OSJEmaGq0cxZ0HbKp5vJkiJNf6MPDliLgTeBJwWmfKkyRJ\nkqZWp6Y1nAv8IDNfHBELgC9FxLMz8956K8yZcxj9/TM7tHtJkn1VkianlUC8BRiqeTy/fK7W84H3\nAWTm+oi4AzgO+Ha9jY6M7J5YpZJUIYODA80HjWFflaT6GvXVVgLxWuCYiDga2AosBpaMGXMr8DLg\npog4Cng6cPukqpUkSZIOoKYn1WXmPoqrSNwA3AKsycxbI2JZRJxRDrsQOCkifgB8CTg7M++eqqIl\nSZKkTukbHR3tyo6Hh3d1Z8eSNA0MDg70TXQd+6ok1deor3qnOkmSJFWagViSJEmVZiCWJElSpRmI\nJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmS\nVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkG\nYkmSJFWagViSJEmV1t/KoIhYBKyiCNCrM3PlmOVnAa8HRoFDgGcAczNzR2fLlSRJkjqrb3R0tOGA\niJgB/CfwUuBOYC2wODNvqzP+5cCfZebLGm13eHhX4x1LUoUNDg70TXQd+6ok1deor7ZyhHghsC4z\nNwBExBrgVGDcQAwsAT420SKlg93q1Veyfv26traxceNGhoaGJr3+ggXHsnTpsrZqkCTpYNNKIJ4H\nbKp5vJkiJD9GRBwKLAL+tP3SpN7y+jcsZseOka7WsHfPHkbu2Tnp9X9w84/49HWfbquG2bPn8A8f\nXdPWNiRJ6iUtzSGegFcAX29l7vCcOYfR3z+zw7uXps7TjjmWn91196TXv/tnd/LA/fe1VcPjnvCE\nNtc/lMOf/NS2tvHkIw5ncHCgrW1oathXJWlyWgnEW4Da72jnl8+NZzEtTpcYGdndyjCpZ1zwl+d3\nu4SeMTy8q9slHPQm86HDvipJ9TXqq60E4rXAMRFxNLCVIvQuGTsoImYBL6K42oQkSZI0LTS9DnFm\n7gPOBG4AbgHWZOatEbEsIs6oGfoq4PrMbO87YUmSJOkAanrZtani5YEkqT4vuyZJndWor3qnOkmS\nJFWagViSJEmVZiCWJElSpXX6OsSSJEktec97zuGWW26e9Pr33Xcf+/bta6uGhx7ax4wZk79+98yZ\nMzn00EPbquH440/g/PNXtrUNtceT6iSpB3lSndTc6tVXsn79ura2sXHjRoaGhpoPrGPBgmNZunRZ\nWzXowGjUVw3EktSDDMSS1FleZUKSJEmqw0AsSZKkSjMQS5IkqdIMxJIkSao0A7EkSZIqzUAsSZKk\nSjMQS5IkqdIMxJIkSao0A7EkSZIqzUAsSZKkSjMQS5IkqdIMxJIkSao0A7EkSZIqzUAsSZKkSjMQ\nS5IkqdL6WxkUEYuAVRQBenVmrhxnzO8AfwUcAgxn5os7WKckSZI0JZoeIY6IGcAVwCnA8cCSiDhu\nzJhZwF8DL8/ME4DXTkGtkiRJUse1MmViIbAuMzdk5l5gDXDqmDGvAz6VmVsAMnN7Z8uUJEmSpkYr\nUybmAZtqHm+mCMm1ng4cEhFfBZ4EXJ6Z13amREmSJGnqtDSHuMXtnAi8BHgi8M2I+GZm/qTeCnPm\nHEZ//8wO7V6SZF+VpMlpJRBvAYZqHs8vn6u1GdiemfcD90fE14DnAHUD8cjI7gmWKknVMTg4MOF1\n7KuSVF+jvtpKIF4LHBMRRwNbgcXAkjFjrgM+GBEzgccDzwM+MKlqJUmSpAOo6Ul1mbkPOBO4AbgF\nWJOZt0bEsog4oxxzG3A98EPgW8BVmfnjqStbkiRJ6oy+0dHRrux4eHhXd3YsSdPA4OBA30TXsa9K\nUn2N+qp3qpMkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYkSVKlGYglSZJUaQZiSZIkVZqBWJIk\nSZVmIJYkSVKlGYglSZJUaQZiSZIkVVp/twuQJElSd61efSXr16+b9PobN25kaGiorRoWLDiWpUuX\ntbWNyeobHR3tyo6Hh3d1Z8eSNA0MDg70TXQd+6pUTe95zznccsvNXa1h1657GBj4pa7WcPzxJ3D+\n+SvrLm/UVw3EktSDDMSSDqQqHCE2EEvSNGMglqTOatRXPalOkiRJlWYgliRJUqUZiCVJklRpBmJJ\nkiRVmoFYkiRJlWYgliRJUqUZiCVJklRpLd26OSIWAasoAvTqzFw5ZvmLgOuA28unPp2ZF3SyUEmS\nJGkqNA3EETEDuAJ4KXAnsDYirsvM28YM/VpmvnIKapQkSZKmTCtTJhYC6zJzQ2buBdYAp44zbsJ3\nVZIkSZK6rZUpE/OATTWPN1OE5LF+MyK+D2wB3pWZP+5AfZIkSdKUamkOcQu+Awxl5u6I+D3gM8DT\nG60wZ85h9PfP7NDuJUn2VUmanFYC8RZgqObx/PK5h2XmvTU/fyEiPhQRh2fm3fU2OjKye6K1SlJl\nDA4OTHgd+6ok1deor7Yyh3gtcExEHB0RjwMWA5+tHRARR9X8vBDoaxSGJUmSpF7R9AhxZu6LiDOB\nG3jksmu3RsQyYDQzrwL+MCLeCuwF7gNOm8qiJU1fy887l7t37mxrG9t/tpU9993XoYom5/GHHsrc\nJz9l0usfPmsWK957YQcrkiRNVt/o6GhXdjw8vKs7O5bUVX98+mJ27NjR1jYe2HM/D+17qEMVTc6M\nmTN43OOfMOn1Z8+ezbUfWVN3+eDgwISv3GNflaT6GvVVA7Ek9SADsSR1VqO+6q2bJUmSVGkGYkmS\nJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWa\ngViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViSJEmVZiCWJElSpRmIJUmSVGkGYkmSJFWagViS\nJEmVZiCWJElSpfW3MigiFgGrKAL06sxcWWfcycA3gNMy89Mdq1KSJEmaIk2PEEfEDOAK4BTgeGBJ\nRBxXZ9xFwPWdLlKSJEmaKq1MmVgIrMvMDZm5F1gDnDrOuLcBnwS2dbA+SZIkaUq1EojnAZtqHm8u\nn3tYRDwVeFVm/g3Q17nyJEmSpKnV0hziFqwCzql53DQUz5lzGP39Mzu0e0mSfVWSJqeVQLwFGKp5\nPL98rtZJwJqI6APmAr8XEXsz87P1NjoysnuitUpSZQwODkx4HfuqJNXXqK+2EojXAsdExNHAVmAx\nsKR2QGb+6v6fI+Jq4J8bhWFJkiSpVzSdQ5yZ+4AzgRuAW4A1mXlrRCyLiDPGWWW0wzVKkiRJU6Zv\ndLQ7+XV4eJfBWZLqGBwcmPAJyvZVSaqvUV/1TnWSJEmqNAOxJEmSKs1ALEmSpEozEEuSJKnSDMSS\nJEmqNAOxJEmSKs1ALEmSpEozEEuSJKnSDMSSJEmqNAOxJEmSKs1ALEmSpEozEEuSJKnSDMSSJEmq\nNAOxJEmSKs1ALEmSpEozEEuSJKnSDMSSJEmqNAOxJEmSKs1ALEmSpEozEEuSJKnSDMSSJEmqtP5W\nBkXEImAVRYBenZkrxyx/JXA+8BCwDzg7M7/S4VolSZKkjmt6hDgiZgBXAKcAxwNLIuK4McNuzMzn\nZOavAW8Crup4pZIkSdIUaGXKxEJgXWZuyMy9wBrg1NoBmbm75uGTgO2dK1GSJEmaOq1MmZgHbKp5\nvJkiJD9KRLwKuBB4MsXRZEmSJKnndeykusz8TGY+A3gFcG2ntitJkiRNpVaOEG8Bhmoezy+fG1dm\nfj0i+iPiiMy8q964OXMOo79/ZuuVSpIasq9K0uS0EojXAsdExNHAVmAxsKR2QEQsyMz15c8nAjQK\nwwAjI7sbLZakShscHJjwOvZVSaqvUV9tGogzc19EnAncwCOXXbs1IpYBo5l5FfCaiHgD8ADwC+C0\njlQuSZIkTbG+0dHRrux4eHhXd3YsSdPA4OBA30TXsa9KUn2N+qp3qpMkSVKlGYglSZJUaQZiSZIk\nVVorV5mQJEkdtHr1laxfv66tbWzcuJGhoaHmA+tYsOBYli5d1lYN0sHCk+okqQd5Up2aWb78LFas\nuKTbZUjTRqO+aiCWpB5kIO5tZy0/m213jXS1hp3bf86suUd1tYYjj5jDJSve39UapFY16qtOmZAk\naYLW5W3c23dod4voO5R777qnqyXs3P7zru5f6hQDsSRJE3RsHOcRYuDII47u6v6lTnHKhCT1IKdM\nqBnnEEsT4xxitcWzoaUDz0B8cLOvSgeegVhd55EMaWIMxJLUWQbiivNs6IJnQ2s6MRBLUmd5lYmK\n82zogmdDS5Kk8RiIK8CzoQueDS1JksbjlAkdEM4hlibGKROS1FnOIVZbPBtaOvAMxJLUWQZiSZpm\nDMSS1FmN+uqMA1mIJEmS1GsMxJIkSao0A7EkSZIqzUAsSZKkSjMQS5IkqdJaujFHRCwCVlEE6NWZ\nuXLM8tcB55QPdwFvzcwfdbJQSVL3tXsZxnYvwQhehlFS5zUNxBExA7gCeClwJ7A2Iq7LzNtqht0O\nvDAzd5bh+cPAb0xFwZKk7mk3iHqTHkm9qJUjxAuBdZm5ASAi1gCnAg8H4sz8Vs34bwHzOlmkJKl9\n7zrnnWzbvr2rNey8ezunLz29qzUcOXcuF6+8tKs1SOotrQTiecCmmsebKUJyPX8CfKGdoiRJnbfu\nP2/j3r0PdbsM7v35tq7uf+fd3f1QIKn3tDSHuFUR8WLgTcALmo2dM+cw+vtndnL3klRpzfrqM084\nnq1dDqMjd21nzhFzu1rDU446ksHBga7WIKm3tBKItwC1Z0DML597lIh4NnAVsCgzR5ptdGRkd6s1\nSlLlTCawNeurK85//2TL6ZhemUM8PLyr2yVIOsAa9dVWAvFa4JiIOBrYCiwGltQOiIgh4FPAH2fm\n+smXKknqZZ24ysTy5We1VYNXmZDUaX2jo6NNB5VXjriMRy67dlFELANGM/OqiPgw8GpgA9AH7M3M\nRvOMGR7e1XzHklRRg4MDfRNdx74qSfU16qstBeKpYOOWpPoMxJLUWY36qneqkyRJUqUZiCVJklRp\nBmJJkiRVmoFYkiRJlWYgliRJUqUZiCVJklRpBmJJkiRVmoFYkiRJldbKrZunrU7cYnRoaKitGrzF\nqCRJUm/r2TvVveucd7Jt+/YDVc64dt69nVmHz+1qDUfOncvFKy/tag2SDjzvVCdJndWor/bsEeIt\nmzeyY8dIV2t4cO9eHnhgT1dr2Hv/7q7uX5Ik6WDXs0eIe8Hy5WexYsUl3S5DUgV5hFiSOqtRXz2o\nA7FziCVNVwZiSeqsygZiSZquDMSS1FmN+qqXXZMkSVKlGYglSZJUaQZiSZIkVZqBWJIkSZVmIJYk\nSVKlGYglSZJUaQZiSZIkVVpLt26OiEXAKooAvTozV45ZHsDVwInA8sz8QKcLlSRJkqZC0yPEETED\nuAI4BTgeWBIRx40ZdhfwNuDijlcoSZIkTaFWpkwsBNZl5obM3AusAU6tHZCZ2zPzO8CDU1CjJEmS\nNGVaCcTzgE01jzeXz0mSJEnTnifVSZIkqdJaOaluCzBU83h++VxbBgcH+trdhiTpEfZVSZqcVgLx\nWuCYiDga2AosBpY0GG9DliRJ0rTRNzo62nRQedm1y3jksmsXRcQyYDQzr4qIo4BvAwPAQ8C9wDMz\n896pK12SJElqX0uBWJIkSTpYeVKdJEmSKs1ALEmSpEozEEuSJKnSDMSSJEmqtFYuu1ZJEfEc4KmZ\n+YUGY14EXAfcXj716cy84EDU18tafO8CuBo4EViemR+oWfZTYCfFFUv2ZubCKS24R7X4Pr4SOJ/i\nvdoHnJ2ZXymXLQJW8cjVYVZOfdXd08r7NYFtfRV4Z2Z+t8GY84BdmfmBiDgduD4zf9buvg9m9tXJ\ns692hn11YqrUVw3E9T0XOAlo9kfwtcx85QGoZzpp5b27C3gb8Kpxlj0E/E5mjkxBbdNJK+/jjZn5\nWYCIeBbwfymuGz4DuAJ4KXAnsDb+//bu5jWuKg7j+De+Ndq0iCJFlAi6+GFrRWKtlhZXoiL1tRup\nVhrBhYQqunCpoGB15ab/gNhCRYUq1hcEF4qohIrSSH3oynfR6KIvqS9IXJyTejNkYu54JpnMfT6b\n3LmTe+fMM3d+c+bOOTMRr0v6qsttXkoLfc52w05gAnCHeH6uq51zXS3DdbWextTVRnWI84+LvAMc\nIr2DngAeANaT3vGtBH4HbgaeBgYjYjOwW9IrbXbbiB8iKZ2dpElgMiK2znFzA/TpcJ4u5DhVuTgE\nTObljcBRSV/n290P3Aksq8JdOq+IuI70neorgFPAqKSjETFIOrN2NSBgsLLNcUmr8vI2YKuk0cr1\n20gvGHsj4hSwSdIfRYPoYa6rnXNdLcN1tR7X1bn15ZPjPwSwR9Ja4Bjp3fR+YJeka4CbgJPAk8DL\nkkbmKdoAmyLi84g4GBFru934JVY6u3amgfciYjwiHirU9l5SNMeIuCsijgBvAY/k1ZcA31b+7bu8\nbjkqmdcRYIuka4GngN15/cPASUnr8voNlW1av6x91mVJr5F+mGh7vu3GdIYrXFc757pahutqPa6r\nLZrYIf5G0id5eR9wC/DDzJgWSSck/b3AfR0ChvPBswc4ULy1vaVkdvPZLGkEuA0Yi4gtBfbZS4rm\nKOmApCuBO4CXird26ZXM63zg1Yg4DLwAzHS2bgT25v0dBr6obLPQs5WNOKvZhutq51xXy3Bdrcd1\ntUUTO8StjnW6YT5gpvLy28DZEXFBsZb1vo6zm4+kH/PfX0hjt/p98keRHCV9CJwVERcC3wPDlasv\nzev6wf/J6xngfUnrgdupfITXolqEq2cu2v2/zea62jnX1TJcV+tpfF1tYod4OCKuz8vbgY+BiyNi\nA0BEDEXEmcBxYPV8O4qINZXljcCApN+60+yeUCy7FqefJBFxXkQM5eWVpDFMEyUa30NKHoNXVJZH\nACT9CoyTJoFcFhHnAPcCbxS/J4uj5HG3mn9fwEYr6z8A7sv7u4o05m3GT5GcAdzdZr91j/l+47ra\nOdfVMlxX63FdbTEwPd06jKN/VQaSj5PGsnwJ7ADWkT6aOxeYIo2dWQG8S5p42G4g+RhpjMxfpIHk\nj0n6tPv3ZPF1Ibs1pPFBq0izn0+QPma5iHT2Yjpvv0/Sc928b4upCzk+QZoM8SdpvNfjksbzdbeS\nJjrMfD3QssuxC3ndALxIOt4OAvdLurxl8scR0rjAMUmf5ckdzwM/k47ZIUkPxuyvB7oHeDa3pamT\n6lxXa3JdLcN1tR7X1bk1sUP8Zj6tbzU4uzKcYz3Oq/f5MeqcsyvDOdbjvObWxCETzXkHUJ6zK8M5\n1uO8ep8fo845uzKcYz3Oq0WjzhB3KiJ2Ao8y+wD6SNKupWnR8uHsynCO9Tiv3ufHqHPOrgznWE+/\n5+UOsZmZmZk1WhOHTJiZmZmZneYOsZmZmZk1mjvEZmZmZtZo7hCbmZmZWaO5Q2xmZmZmjfYPr+w8\nlVDNo2QAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "axes = sns.boxplot(data=june_coverage, order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n                  color=sns.color_palette(\"coolwarm\", 5)[0])\naxes.set_xticklabels(['Under 5', 'Under 15', 'Under 30', 'Under 5 + 20-30'])\naxes.set_ylabel('% susceptibles vaccinated')\nsns.despine(offset=10, trim=True)",
      "execution_count": 127,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9b68b27f0>",
            "image/png": 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EsXhbZj5Ua1UVcUhfktQ0I7nT3uHAH4AraN1t7w8R8Zxqy5MkSXUqcx3+J4BXZ2Zk5pOB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\ngS9JUgMY+JIkNYCBL0lSAxj4kiQ1wP8H8NWT+S/hN1gAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n    pc_values = np.maximum(0, 1- 1/Rt_values)\n    ax = pc_values.boxplot(return_type='axes', figsize=(14,6), grid=False)\n    ax.set_ylabel('$p_c$')\n    ax.semilogy();",
      "execution_count": 128,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb911181208>",
            "image/png": 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          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "qcoverage = coverage.groupby(['Month','Confirmation'], sort=False).quantile([0.025, 0.975])\nqcoverage",
      "execution_count": 129,
      "outputs": [
        {
          "execution_count": 129,
          "data": {
            "text/plain": "                            pct_15    pct_30     pct_5  pct_adult\nMonth Confirmation                                               \nJune  Lab          0.025  0.241274  0.729057  0.156162   0.513108\n                   0.975  0.277205  0.736786  0.181394   0.518855\n      Clinical     0.025  0.312788  0.791499  0.222198   0.618730\n                   0.975  0.358310  0.799303  0.258587   0.627796\nJuly  Lab          0.025  0.238358  0.700166  0.147069   0.485921\n                   0.975  0.255405  0.704341  0.158505   0.489398\n      Clinical     0.025  0.256499  0.671180  0.196884   0.544044\n                   0.975  0.275787  0.674649  0.212795   0.547529",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>pct_15</th>\n      <th>pct_30</th>\n      <th>pct_5</th>\n      <th>pct_adult</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th>Confirmation</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">June</th>\n      <th rowspan=\"2\" valign=\"top\">Lab</th>\n      <th>0.025</th>\n      <td>0.241274</td>\n      <td>0.729057</td>\n      <td>0.156162</td>\n      <td>0.513108</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.277205</td>\n      <td>0.736786</td>\n      <td>0.181394</td>\n      <td>0.518855</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Clinical</th>\n      <th>0.025</th>\n      <td>0.312788</td>\n      <td>0.791499</td>\n      <td>0.222198</td>\n      <td>0.618730</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.358310</td>\n      <td>0.799303</td>\n      <td>0.258587</td>\n      <td>0.627796</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">July</th>\n      <th rowspan=\"2\" valign=\"top\">Lab</th>\n      <th>0.025</th>\n      <td>0.238358</td>\n      <td>0.700166</td>\n      <td>0.147069</td>\n      <td>0.485921</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.255405</td>\n      <td>0.704341</td>\n      <td>0.158505</td>\n      <td>0.489398</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Clinical</th>\n      <th>0.025</th>\n      <td>0.256499</td>\n      <td>0.671180</td>\n      <td>0.196884</td>\n      <td>0.544044</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.275787</td>\n      <td>0.674649</td>\n      <td>0.212795</td>\n      <td>0.547529</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "mean_coverage = coverage.groupby(['Month','Confirmation'], sort=False).mean()\nmean_coverage",
      "execution_count": 130,
      "outputs": [
        {
          "execution_count": 130,
          "data": {
            "text/plain": "                      pct_15    pct_30     pct_5  pct_adult\nMonth Confirmation                                         \nJune  Lab           0.255801  0.732447  0.166347   0.516359\n      Clinical      0.335176  0.795189  0.240441   0.624041\nJuly  Lab           0.246933  0.702224  0.152858   0.487683\n      Clinical      0.265873  0.672903  0.204333   0.545750",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>pct_15</th>\n      <th>pct_30</th>\n      <th>pct_5</th>\n      <th>pct_adult</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th>Confirmation</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">June</th>\n      <th>Lab</th>\n      <td>0.255801</td>\n      <td>0.732447</td>\n      <td>0.166347</td>\n      <td>0.516359</td>\n    </tr>\n    <tr>\n      <th>Clinical</th>\n      <td>0.335176</td>\n      <td>0.795189</td>\n      <td>0.240441</td>\n      <td>0.624041</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">July</th>\n      <th>Lab</th>\n      <td>0.246933</td>\n      <td>0.702224</td>\n      <td>0.152858</td>\n      <td>0.487683</td>\n    </tr>\n    <tr>\n      <th>Clinical</th>\n      <td>0.265873</td>\n      <td>0.672903</td>\n      <td>0.204333</td>\n      <td>0.545750</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "output_type": "execute_result",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "label_lookup = dict(zip(['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n    ['Under 5', 'Under 15', 'Under 30', 'Adults & young kids']))",
      "execution_count": 96,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "from matplotlib.patches import Rectangle\n\nplot_interval = True\n\nwith sns.axes_style(\"ticks\"):\n    \n    colors = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\"]\n    \n    fig = plt.figure(figsize=(14, 6))\n    ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.8])\n    ax1.semilogy()\n    ax2 = fig.add_axes([0.5, 0.1, 0.4, 0.8])\n    ax2.semilogy()\n    \n    q = pc_values.quantile([0.025, 0.975]).values + 1e-8\n    m = pc_values.mean().values\n    \n    months = ['June', 'July']\n    \n    for i,ax in enumerate([ax1, ax2]):\n        ax.set_yticks([0.01, 0.1, 0.2, 0.4, 0.7])\n        ax.set_yticklabels([0.01, 0.1, 0.2, 0.4, 0.7])\n        ax.set_ybound(0.0001, .999)\n        \n        ax.set_xticks([0.08, 0.27, 0.4])\n        ax.set_xticklabels(['age confirmation', 'clinical only'])\n        \n        ax.tick_params(length=0)\n        \n        ax.set_title(months[i])\n                \n        ax.add_patch(Rectangle((0.0, q[0, 2*i]), 0.15, q[1, 2*i]-q[0, 2*i], alpha=0.1))\n        ax.add_patch(Rectangle((0.2, q[0, 1+2*i]), 0.15, q[1, 1+2*i]-q[0, 1+2*i], alpha=0.1))\n        \n        # Add estimates\n        conf = ('Lab', 'Clinical')\n        handles = []\n        for j, pct in enumerate(['pct_5', 'pct_15', 'pct_30', 'pct_adult']):\n            \n            interval = qcoverage.ix[(months[i], conf[0])]\n            ax.add_patch(Rectangle((0.04*j, interval.loc[0.025, pct]), \n                                   0.01, interval.loc[0.975, pct]-interval.loc[0.025, pct], \n                                   lw=2, \n                                   color=colors[j]))\n\n            interval = qcoverage.ix[(months[i], conf[1])]\n            rect = Rectangle((0.04*j + 0.2, interval.loc[0.025, pct]), \n                                   0.01, interval.loc[0.975, pct]-interval.loc[0.025, pct],\n                                   lw=2, label=label_lookup[pct],\n                                   color=colors[j])\n            ax.add_patch(rect)\n            handles.append(rect)\n        \n        \n        ax.axhline(m[2*i], 0.13, 0.45, color='black', lw=0.7)\n        ax.axhline(m[1+2*i], 0.57, 0.88, color='black', lw=0.7)\n\n    ax2.set_yticks([])\n    ax1.set_ylabel('Estimated vaccination threshold required')\n    \n    ax2.legend(handles=handles, loc=4)\n    \n    sns.despine(bottom=True)",
      "execution_count": 98,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb910d30d68>",
            "image/png": 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Gdg9WGdLT05WRkaH8/HxnRwEAuCnGEgCAoxhWk5u6Tpw4oSlTpuibb76RYRi6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\nIiIKAv8DC6z3zalF+S8AAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.24             0.009            0.0              [ 0.22   0.255]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.222            0.234           0.24           0.247         0.259\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.335            0.011            0.0              [ 0.312  0.357]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.313            0.326           0.336          0.343         0.358\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.795            0.002            0.0              [ 0.792  0.799]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.791            0.794           0.795          0.797         0.799\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.624            0.003            0.0              [ 0.619  0.628]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.619            0.622           0.625          0.626         0.628\n\t\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_july.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.153            0.003            0.0              [ 0.147  0.158]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.147            0.151           0.154          0.155         0.159\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.247            0.005            0.0              [ 0.238  0.255]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.238            0.244           0.248          0.249         0.255\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.702            0.001            0.0              [ 0.7    0.704]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.7              0.701           0.702          0.703         0.704\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.488            0.001            0.0              [ 0.486  0.489]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.486            0.487           0.488          0.488         0.489\n\t\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_july_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.204            0.004            0.0              [ 0.197  0.213]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.197            0.202           0.205          0.206         0.213\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.266            0.005            0.0              [ 0.257  0.276]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.256            0.263           0.266          0.269         0.276\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.673            0.001            0.0              [ 0.671  0.675]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.671            0.672           0.673          0.673         0.675\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.546            0.001            0.0              [ 0.544  0.547]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.544            0.545           0.546          0.546         0.548\n\t\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Initial migrant susceptibles (June model, with confirmation)"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june.summary(['N_migrant'])",
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nN_migrant:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t550455.475       34950.451        1331.743     [ 505830.  609750.]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t472729.0         517245.0        556334.0       563299.0      608547.0\n\t\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "By age group:"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.M_0, custom_labels=age_group.categories)",
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9cff929e8>",
            "image/png": 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IBgxJklScAUOSJBVnwJAkScUZMCRJUnEGDEmSVJwBowCfRSJJ0kgGDEmSVJwBQ5IkFWfA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\nGJIkqTgDhiRJKs6AIUmSivu/GHD/3JXnKQkAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "june_r = pd.DataFrame({'local': model_june.trace('R_t_local')[:, -1],\n                             'total': model_june.trace('R_t')[:, -1]})\njune_r['Month'] = 'June'\njune_r['Confirmation'] = 'Lab'\n\njune_noconf_r = pd.DataFrame({'local': model_june_noconf.trace('R_t_local')[:, -1],\n                             'total': model_june_noconf.trace('R_t')[:, -1]})\njune_noconf_r['Month'] = 'June'\njune_noconf_r['Confirmation'] = 'Clinical'\n\njuly_r = pd.DataFrame({'local': model_july.trace('R_t_local')[:, -1],\n                             'total': model_july.trace('R_t')[:, -1]})\njuly_r['Month'] = 'July'\njuly_r['Confirmation'] = 'Lab'\n\njuly_noconf_r = pd.DataFrame({'local': model_july_noconf.trace('R_t_local')[:, -1],\n                             'total': model_july_noconf.trace('R_t')[:, -1]})\njuly_noconf_r['Month'] = 'July'\njuly_noconf_r['Confirmation'] = 'Clinical'",
      "execution_count": 136,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "r_estimates = pd.concat([june_r, june_noconf_r, july_r, july_noconf_r], \n                     ignore_index=True)\n\nr_estimates.to_csv('output/r_estimates.csv')",
      "execution_count": 137,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "g = sns.factorplot(row=\"Month\", col=\"Confirmation\", data=r_estimates, kind='box',\n              row_order=['June', 'July'],\n              order=['local', 'total'], margin_titles=True,\n            palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)\ng.set_ylabels(r\"$R_t$\")\nfor ax in g.axes.ravel():\n    ax.hlines(1, -1, 2, linestyles='dashed')",
      "execution_count": 138,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fb9116f6748>",
            "image/png": 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wFjgzIlbXW5IkSZJUjdJmzrcBd2TmfcB9EfF14GBg81SdHnzwoebw8OIq6pOk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\nRK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Removing augmentation of migrant population"
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "model_june_nomigrant = MCMC(measles_model('1997-06-15', migrant=False))",
      "execution_count": 92,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "model_june_nomigrant.sample(n_iterations, n_burn)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "Matplot.plot(model_june_nomigrant.R0)",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Plotting R0\n",
          "name": "stdout"
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x1aa22ba58>",
            "image/png": 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vv/2Sw4cPsnz5aqytrW9LnRWK2m/73HOTsLa2qX5FIVqBvLxc9Ho9CoUCW1u7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\nDRZcfH1jIkREREbHysoKmzdvbnK3URcXl998BtXf3x8ffPBBs/