Non-Spatial Measles Model (PyMC 3).ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "sb.set()\n",
    "import pdb\n",
    "\n",
    "from pymc3 import Model, sample, find_MAP, Metropolis, NUTS\n",
    "from pymc3 import (Normal, HalfCauchy, Uniform, MvNormal, Beta, Binomial,\n",
    "                   Dirichlet, Multinomial, Bernoulli, Poisson, Wishart\n",
    "                  )\n",
    "from pymc3 import Potential, Deterministic\n",
    "\n",
    "import theano.tensor as T\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open(\"styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
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   "execution_count": 2,
   "metadata": {
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   "source": [
    "data_dir = \"data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import outbreak data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0)\n",
    "measles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\n",
    "measles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\n",
    "measles_data.ONSET = pd.to_datetime(measles_data.ONSET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = (measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})\n",
    "                   .drop('AGE', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sao Paulo population by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "_names = sp_pop.index.values\n",
    "_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\n",
    "sp_pop.set_index(_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "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",
       "    <tr>\n",
       "      <th>ARICANDUVA</th>\n",
       "      <td>7732</td>\n",
       "      <td>7730</td>\n",
       "      <td>8373</td>\n",
       "      <td>8956</td>\n",
       "      <td>9182</td>\n",
       "      <td>8531</td>\n",
       "      <td>7813</td>\n",
       "      <td>7365</td>\n",
       "      <td>6551</td>\n",
       "      <td>5554</td>\n",
       "      <td>4887</td>\n",
       "      <td>3858</td>\n",
       "      <td>3320</td>\n",
       "      <td>2449</td>\n",
       "      <td>1611</td>\n",
       "      <td>1723</td>\n",
       "      <td>95635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTUR ALVIM</th>\n",
       "      <td>9031</td>\n",
       "      <td>9078</td>\n",
       "      <td>10000</td>\n",
       "      <td>11058</td>\n",
       "      <td>11387</td>\n",
       "      <td>10347</td>\n",
       "      <td>9125</td>\n",
       "      <td>8658</td>\n",
       "      <td>7830</td>\n",
       "      <td>7055</td>\n",
       "      <td>5919</td>\n",
       "      <td>4612</td>\n",
       "      <td>3756</td>\n",
       "      <td>2633</td>\n",
       "      <td>1727</td>\n",
       "      <td>1724</td>\n",
       "      <td>113940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\n",
       "AGUA RASA                5411        5750          6450          7122   \n",
       "ALTO DE PINHEIROS        2070        2369          2953          3661   \n",
       "ANHANGUERA               3068        3006          2755          2431   \n",
       "ARICANDUVA               7732        7730          8373          8956   \n",
       "ARTUR ALVIM              9031        9078         10000         11058   \n",
       "\n",
       "                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\n",
       "AGUA RASA                  7621          7340          6999          6984   \n",
       "ALTO DE PINHEIROS          4612          4190          3539          3633   \n",
       "ANHANGUERA                 2426          2636          2695          2308   \n",
       "ARICANDUVA                 9182          8531          7813          7365   \n",
       "ARTUR ALVIM               11387         10347          9125          8658   \n",
       "\n",
       "                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\n",
       "AGUA RASA                  6346          5608          4987          4212   \n",
       "ALTO DE PINHEIROS          3448          3289          3040          2533   \n",
       "ANHANGUERA                 1653          1107           753           509   \n",
       "ARICANDUVA                 6551          5554          4887          3858   \n",
       "ARTUR ALVIM                7830          7055          5919          4612   \n",
       "\n",
       "                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\n",
       "AGUA RASA                  4152          3595          2937         3637   \n",
       "ALTO DE PINHEIROS          2298          1732          1305         1823   \n",
       "ANHANGUERA                  352           217           162          171   \n",
       "ARICANDUVA                 3320          2449          1611         1723   \n",
       "ARTUR ALVIM                3756          2633          1727         1724   \n",
       "\n",
       "                    Total  \n",
       "AGUA RASA           89151  \n",
       "ALTO DE PINHEIROS   46495  \n",
       "ANHANGUERA          26249  \n",
       "ARICANDUVA          95635  \n",
       "ARTUR ALVIM        113940  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of cumulative cases by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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+DehE590+5nIpoxSvTSILLsTlbRx23rYNUh1PgeYgtuiTuP0tp/38ZCnF/97+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\nJ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vLGgVS2ECS9MLbzBvt+k2jdtLyZidQWM2O9fSM0CbTG8yx0PQpv+97hf//P5y4mtFCZ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\nAwSei7v2LggQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc58466c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\n",
    "measles_onset_dist.cumsum().plot(legend=False, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_district_cases = measles_onset_dist.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 5 districts by number of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DISTRICT\n",
       "GRAJAU             1074\n",
       "JARDIM ANGELA       944\n",
       "CAPAO REDONDO       849\n",
       "JARDIM SAO LUIZ     778\n",
       "CAMPO LIMPO         692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = measles_onset_dist.sum()\n",
    "totals.sort(ascending=False)\n",
    "totals[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of cases, by confirmation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PcPvtbZk37wOaNGkGwOefFyz9f/fdPTh37izTpyexffuP+Pr6EBhYlZEjR/++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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc584837240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_conclusion = measles_data.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\n",
    "counts_by_cause = by_conclusion.size().unstack().fillna(0)\n",
    "ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</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</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</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</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</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BIRTHS   VAX        POP  SIA\n",
       "YEAR                               \n",
       "1980  3896442  0.57  121740438    0\n",
       "1981  3933136  0.73  124610790    0\n",
       "1982  3952137  0.66  127525420    0\n",
       "1983  3952735  0.68  130455659    0\n",
       "1984  3935224  0.73  133364277    0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\n",
    "vaccination_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "sia_susc = np.ones(len(vax_97))\n",
    "birth_year = np.arange(1922, 1998)[::-1]\n",
    "by_mask = (birth_year > 1983) & (birth_year < 1992)\n",
    "by_mask = (birth_year > 1972) & (birth_year < 1986)\n",
    "sia_susc[by_mask] *= 0.2\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]\n",
    "n = len(vax_97)\n",
    "FOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ],\n",
       "       [ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ],\n",
       "       [ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ],\n",
       "       ..., \n",
       "       [ 0.307,  0.307,  0.307, ...,  0.307,  0.307,  0.307],\n",
       "       [ 0.217,  0.217,  0.217, ...,  0.217,  0.217,  0.217],\n",
       "       [ 0.28 ,  0.28 ,  0.28 , ...,  0.28 ,  0.28 ,  0.28 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOI_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vacc_susc = (1 - vax_97*0.9)[::-1]\n",
    "vacc_susc[0] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5  ,  0.217,  0.307,  0.235,  0.181,  0.235,  0.298,  0.46 ,\n",
       "        0.442,  0.424,  0.397,  0.397,  0.343,  0.388,  0.406,  0.343,\n",
       "        0.487,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vacc_susc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sia_susc = np.ones(len(vax_97))\n",
    "birth_year = np.arange(1922, 1998)[::-1]\n",
    "by_mask = ((birth_year > 1983) & (birth_year < 1992)) | ((birth_year > 1972) & (birth_year < 1986))\n",
    "sia_susc[by_mask] *= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  0.2,  0.2,  0.2,  0.2,  0.2,\n",
       "        0.2,  0.2,  0.2,  0.2,  0.2,  0.2,  0.2,  0.2,  0.2,  0.2,  0.2,\n",
       "        0.2,  0.2,  0.2,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "        1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "        1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "        1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,\n",
       "        1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ,  1. ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia_susc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Disease Transmission Model\n",
    "\n",
    "As a baseline for comparison, we can fit a model to all the clinically-confirmed cases, regardless of lab confirmation status. For this, we will use a simple SIR disease model, which will be fit using MCMC.\n",
    "\n",
    "This model fits the series of 2-week infection totals in each district $i$ as a set of Poisson models:\n",
    "\n",
    "\\\\[Pr(I(t)_{i} | \\lambda(t)_i) = \\text{Poisson}(\\lambda(t)_i) \\\\]\n",
    "\n",
    "Where the outbreak intensity is modeled as:\n",
    "\n",
    "\\\\[\\lambda(t)_i = \\beta [I^{(w)}(t-1)_i]^{\\alpha} S(t-1)_i\\\\]\n",
    "\n",
    "\\\\[\\alpha \\sim \\text{Exp}(1)\\\\]\n",
    "\n",
    "We will assume here that the transmission rate is constant over time (and across districts):\n",
    "\n",
    "\\\\[\\beta \\sim \\text{Gamma}(1, 0.1)\\\\]\n",
    "\n",
    "To account for the influence of infected individuals from neighboring districts on new infections, the outbreak intensity was modeled using a spatial-weighted average of infecteds across districts, where populations were weighted as an exponential function of the distance between district centroids:\n",
    "\n",
    "\\\\[w_{d} = \\text{exp}(-\\theta d)\\\\]\n",
    "\n",
    "\\\\[\\theta \\sim \\text{Exp}(1)\\\\]\n",
    "\n",
    "### Confirmation Sub-model\n",
    "\n",
    "Rather than assume all clinical cases are true cases, we can adjust the model to account for lab confirmation probability. This is done by including a sub-model that estimates age group-specific probabilities of confirmation, and using these probabilities to estimate the number of lab-confirmed cases. These estimates are then plugged into the model in place of the clinically-confirmed cases.\n",
    "\n",
    "We specified a structured confirmation model to retrospectively determine the age group-specific probabilities of lab confirmation for measles, conditional on clinical diagnosis. Individual lab confirmation events $c_i$ were modeled as Bernoulli random variables, with the probability of confirmation being allowed to vary by age group:\n",
    "\n",
    "$$c_i \\sim \\text{Bernoulli}(p_{a(i)})$$\n",
    "\n",
    "where $a(i)$ denotes the appropriate age group for the individual indexed by i. There were 16 age groups, the first 15 of which were 5-year age intervals $[0,5), [5, 10), \\ldots , [70, 75)$, with the 16th interval including all individuals 75 years and older.\n",
    "\n",
    "Since the age interval choices were arbitrary, and the confirmation probabilities of adjacent groups likely correlated, we modeled the correlation structure directly, using a multivariate logit-normal model. Specifically, we allowed first-order autocorrelation among the age groups, whereby the variance-covariance matrix retained a tridiagonal structure. \n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\Sigma = \\left[{\n",
    "\\begin{array}{c}\n",
    "  {\\sigma^2} & {\\sigma^2 \\rho} & 0& \\ldots & {0} & {0}  \\\\\n",
    "  {\\sigma^2 \\rho} & {\\sigma^2} &  \\sigma^2 \\rho & \\ldots & {0}  & {0} \\\\\n",
    "  {0} & \\sigma^2 \\rho & {\\sigma^2} & \\ldots & {0} & {0} \\\\\n",
    "  \\vdots & \\vdots & \\vdots &  & \\vdots & \\vdots\\\\\n",
    "  {0} & {0} & 0 & \\ldots &  {\\sigma^2} & \\sigma^2 \\rho  \\\\\n",
    "{0} & {0} & 0 & \\ldots & \\sigma^2 \\rho &  {\\sigma^2} \n",
    "\\end{array}\n",
    "}\\right]\n",
    "\\end{aligned}$$\n",
    "\n",
    "From this, the confirmation probabilities were specified as multivariate normal on the inverse-logit scale.\n",
    "\n",
    "$$ \\text{logit}(p_a) = \\{a\\} \\sim N(\\mu, \\Sigma)$$\n",
    "\n",
    "Priors for the confirmation sub-model were specified by:\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\mu_i &\\sim N(0, 100) \\\\\n",
    "\\sigma &\\sim \\text{HalfCauchy}(25) \\\\\n",
    "\\rho &\\sim U(-1, 1)\n",
    "\\end{aligned}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age classes are defined in 5-year intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_classes = [0,5,10,15,20,25,30,35,40,100]\n",
    "measles_data.dropna(subset=['YEAR_AGE'], inplace=True)\n",
    "measles_data['YEAR_AGE'] = measles_data.YEAR_AGE.astype(int)\n",
    "measles_data['AGE_GROUP'] = pd.cut(measles_data.YEAR_AGE, age_classes, right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab-checked observations are extracted for use in estimating lab confirmation probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CONFIRMED = measles_data.CONCLUSION == 'CONFIRMED'\n",
    "CLINICAL = measles_data.CONCLUSION == 'CLINICAL'\n",
    "DISCARDED = measles_data.CONCLUSION == 'DISCARDED'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract confirmed and clinical subset, with no missing county information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age = lab_subset.YEAR_AGE.values\n",
    "ages = lab_subset.YEAR_AGE.unique()\n",
    "counties = lab_subset.COUNTY.unique()\n",
    "confirmed = (lab_subset.CONCLUSION=='CONFIRMED').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clinic_subset = measles_data[CLINICAL & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pSY0aNdLs2bOVnJysOXPmKDY2tko7BQAAAAC1UZmnUHbq1Elz586VJDVs2FBn\nzpyR1WqVYRh29Xbv3i1/f3+5u7vLzc1NgYGB2rlzp1JSUhQcHCxJCgoK0q5du6qgGwAAAABQ+5UZ\n4Mxms+rXry9Jeu+999SzZ0+ZzWatXLlSw4cPV3R0tE6ePKmsrCx5eXnZtvPy8lJmZqZduclkktls\nVn5+fhV1BwAAAABqL4eugZOkzz//XO+//76WLl2qtLQ0eXp6ys/PT/Hx8VqwYIECAgLs6hedobvM\narVeXYsBAAAAoI5yaBXKr7/+WvHx8VqyZIk8PDzUpUsX+fn5SZL69Omj/fv3y8fHR5mZmbZtMjIy\n5OPjI29vb2VlZUmSbebNxcXh3AgAAAAA+J8yA1xubq5ef/11LVq0SA0aNJAkjRkzRt9//70kafv2\n7WrTpo38/f2Vlpam3Nxc5eXlKTU1VR07dlS3bt20ceNGSVJSUpI6d+5chd0BAAAAgNqrzKmwTz/9\nVDk5OXruuedkGIZMJpMGDhyoCRMmyN3dXe7u7po+fbrc3NwUHR2tyMhImc1mRUVFycPDQ6GhoUpO\nTlZERITc3Nw0c+bMa9EvAAAAAKh1TEZpF6tVo8zM0zpw4Ad1XdVRalrkySwp5ZGdatXqNltRiXVL\nqAcAAAAAzqBZswYlljt0DRwAAAAAoPoR4AAAAADASRDgAAAAAMBJEOAAAAAAwEkQ4AAAAADASRDg\nAAAAAMBJEOAAAAAAwEkQ4AAAAADASRDgAAAAAMBJuFR3AwDAUQUFBTp8+GCxcl/fW2WxWKqhRQAA\nANcWAQ6A0zh8+KC6LuwoeRYqzJFSRu9Uq1a3VVu7AAAArhUCHADn4impaXU3AgAAoHrU2ABXUGCV\nckp4Iud/zwEAAABAHVNjA5xkSCuXSGpRpPyoNMqojgYBKAeuVwMAAKh8NTbAXfoFr7ukNkWe2S+L\nJbcaWgSgPLheDQAAoPLV2AAHoBbgejUAAIBKxX3gAAAAAMBJMAMHwGGlXdcmcW0bAADAtUCAA+Cw\nEq9rk7i2DQAA4BohwAEoH65rAwAAqDYEOADVitMyAQAAHEeAA1CtOC0TAADAcQ4FuFmzZmnXrl0q\nKCjQE088ofbt2+v555+XYRhq1qyZZs2aJVdXV61fv14rVqyQxWJReHi4wsLClJ+fr5iYGKWnp8ti\nsWjGjBlq0aLozbkB1GmclgkAAOCQMgPctm3b9OOPP2rNmjXKycnRgAED1KVLFw0ZMkT33HOPYmNj\nlZCQoAfd/lQ1AAAgAElEQVQeeEBxcXFKSEiQi4uLwsLCFBISoqSkJDVq1EizZ89WcnKy5syZo9jY\n2GvRNwAAAACoVcq8D1ynTp00d+5cSVLDhg115swZ7dixQ71795Yk9erVS1u3btXu3bvl7+8vd3d3\nubm5KTAwUDt37lRKSoqCg4MlSUFBQdq1a1cVdgcAAAAAaq8yZ+DMZrPq168vSVq3bp169uypb775\nRq6urpKkJk2a6MSJE8rOzpaXl5dtOy8vL2VmZiorK8tWbjKZZDablZ+fLxcXLr8DUDVYGAUAANRW\nDqeozz//XAkJCVq6dKlCQkJs5YZhlFi/tHKr1VrOJgKoaqUFHmcNOyyMAgAAaiuHAtzXX3+t+Ph4\nLV26VB4eHnJ3d9eFCxdUr149ZWRkyMfHR97e3srMzLRtk5GRoYCAAHl7eysrK0tt27ZVfn7+pYMy\n+wbUKCUGHmcPOyyMAgAAaqEyr4HLzc3V66+/rkWLFqlBgwaSpK5duyoxMVGSlJiYqO7du8vf319p\naWnKzc1VXl6eUlNT1bFjR3Xr1k0bN26UJCUlJalz585V2B0AFXY58Fz+V3T2CgAAANWuzKmwTz/9\nVDk5OXruuedkGIZMJpNee+01TZo0Se+++66aN2+uAQMGyGKxKDo6WpGRkTKbzYqKipKHh4dCQ0OV\nnJysiIgIubm5aebMmdeiXwAAAABQ65QZ4B566CE99NBDxcqXLVtWrCwkJMTu+jjp0iIoM2bMuIom\nAgAAAAAkB06hBAAAAADUDAQ4AAAAAHAStWI5yIICq5RTpDDnf+UAAAAAUEvUigAnGdLKJZJaFCo7\nKo0q+V50AHBZbbsHHgAAqN1qRYC79EtWd0ltCpXul8WSW00tAuAsauU98AAAQK1VKwIcAFwVbvoN\nAACcBIuYAAAAAICTIMABAAAAgJMgwAEAAACAkyDAAQAAAICTIMABAAAAgJMgwAEAAACAkyDAAQAA\nAICTIMABAAAAgJMgwAEAAACAkyDAAQAAAICTIMABAAAAgJMgwAEAAACAkyDAAQAAAICTIMABAAAA\ngJMgwAEAAACAkyDAAQAAAICTcCjA7du3T3fffbdWrVolSZowYYLuv/9+DRs2TMOGDdOWLVskSevX\nr1dYWJgGDRqkdevWSZLy8/M1btw4RUREaOjQoTp69GgVdQUAAAAAajeXsiqcPXtWr732mrp162ZX\nPm7cOPXo0cOuXlxcnBISEuTi4qKwsDCFhIQoKSlJjRo10uzZs5WcnKw5c+YoNja28nsCAAAAALVc\nmTNwbm5uWrx4sZo2bXrFert375a/v7/c3d3l5uamwMBA7dy5UykpKQoODpYkBQUFadeuXZXTcgAA\nAACoY8oMcGazWfXq1StWvnLlSg0fPlzR0dE6efKksrKy5OXlZXvey8tLmZmZduUmk0lms1n5+fmV\n2AUAAAAAqBvKPIWyJA888IA8PT3l5+en+Ph4LViwQAEBAXZ1DMMocVur1VqRQwIAAABAnVehVSi7\ndOkiPz8/SVKfPn20f/9++fj4KDMz01YnIyNDPj4+8vb2VlZWliTZZt5cXCqUGwEAAACgTqtQgBsz\nZoy+//57SdL27dvVpk0b+fv7Ky0tTbm5ucrLy1Nqaqo6duyobt26aePGjZKkpKQkde7cufJaDwAA\nAAB1SJlTYbt379bkyZP1yy+/yGKxaM2aNRozZowmTJggd3d3ubu7a/r06XJzc1N0dLQiIyNlNpsV\nFRUlDw8PhYaGKjk5WREREXJzc9PMmTOvRb8AoFIVFBTo8OGDxcp9fW+VxWKphhYBAIC6qMwA16FD\nB/3jH/8oVn733XcXKwsJCVFISIhdmdls1owZM66iiQAqitBReQ4fPqiuCztKnoUKc6SU0TvVqtVt\n1dYuAABQt3AxGlCLEToqmaekK99RBQAAoEoR4IDajtABAABQa1RoERMAAAAAwLXHDBwAp1FQYJVy\nihTm/K8cAACgDiDAAXBYiQFKKjFEVU3YMqSVSyS1KFR2VBplXMU+AQAAnAcBDkA5lBSgpJJDVOWH\nrUsrZ3aX1KZQ6X5ZLLkV3icAAIAzIcABcFjJAUoqKUQRtgAAACofi5gAAAAAgJMgwAEAAACAk+AU\nSgDVqjwLowAAANR1BDgA1aw8C6MAAADUbQQ4ANWqPAujOIpZPQAAUFsR4ADUQszqAQCA2okAB6CK\nbrpdfapiVg8AAKAmIMABUFXcdBsAAACVjwAHgJtuV7KCggIdPnywWLmv763/e60BAAAqhgAHAJXs\n8OGD6rqwo+RZqDBHShm9U61a3VZt7QIAAM6PAAegTquy6/88JTW9ul0AAAAURYADUMdx/R8AAHAe\nBDgAdRrX/wEAAGdiru4GAAAAAAAcQ4ADAAAAACfhUIDbt2+f7r77bq1atUqSdPz4cQ0dOlRDhgzR\nX/7yF128eFGStH79eoWFhWnQoEFat26dJCk/P1/jxo1TRESEhg4dqqNHj1ZRVwAAAACgdiszwJ09\ne1avvfaaunXrZiubO3euhg4dqpUrV6ply5ZKSEjQ2bNnFRcXp7ffflsrVqzQ22+/rVOnTunjjz9W\no0aNtHr1ao0aNUpz5syp0g4BAAAAQG1VZoBzc3PT4sWL1bTpb+thb9++Xb169ZIk9erVS1u3btXu\n3bvl7+8vd3d3ubm5KTAwUDt37lRKSoqCg4MlSUFBQdq1a1cVdQUAnEtBQYEOHPih2L+CgoLqbhoA\nAKihylyF0mw2q169enZlZ8+elaurqySpSZMmOnHihLKzs+Xl5WWr4+XlpczMTGVlZdnKTSaTzGaz\n8vPz5eLCApgA6jZu+A0AAMrrqlOUYZR8r6TSyq3Wq7w5LgDUJtzwGwAAlEOFVqF0d3fXhQsXJEkZ\nGRny8fGRt7e3MjMzbXUKl2dlZUm6tKCJJGbfAAAAAKACKhTgunbtqsTERElSYmKiunfvLn9/f6Wl\npSk3N1d5eXlKTU1Vx44d1a1bN23cuFGSlJSUpM6dO1de6wEAAACgDilzKmz37t2aPHmyfvnlF1ks\nFq1Zs0ZLly5VTEyM3n33XTVv3lwDBgyQxWJRdHS0IiMjZTabFRUVJQ8PD4WGhio5OVkRERFyc3PT\nzJkzr0W/AAAAAKDWKTPAdejQQf/4xz+KlS9btqxYWUhIiEJCQuzKzGazZsyYcRVNBAAAAABIFTyF\nEgAAAABw7RHgAAAAAMBJsBwkADigoMAq5RQpzPlfOQAAwDVCgAMAhxjSyiWSWhQqOyqNKvmelwAA\nAFWBAAc4mYKCAh0+fLBYua/vrbJYLNXQorrh0mvbXVKbQqX7ZbHkVlOLAABAXUSAA5zM4cMH1XVh\nR8mzUGGOlDJ6p1q1us2uLqf91R4EdwAAIBHgAOfkKampIxU57a+2KE9wBwAAtRcBDqjFOO2vlnE4\nuAMAgNqK2wgAAAAAgJMgwAEAAACAkyDAAQAAAICT4Bo4AKhkrP4JAACqSp0KcCzDDeDaYPVPAABQ\nNepUgGMZbgDXQnWu/skfqgAAqN3qVICTxDLcAGo1/lAFAEDtVvcCHADUdvyhCgCAWotVKAEAAADA\nSRDgAAAAAMBJEOAAAAAAwElwDVwNxCpydRPjDgAAgLIQ4GogVpGrmxh3AAAAlIUAdw2Va4aFVeTq\nJsa9TikosEo5RQpz/lcOAABQggoFuO3bt+vZZ5/VbbfdJsMw1LZtWz322GN6/vnnZRiGmjVrplmz\nZsnV1VXr16/XihUrZLFYFB4errCwsMrug9OozhkWTs8DaiJDWrlEUotCZUelUUZ1NQgAANRwFZ6B\nu+uuuzR37lzb4wkTJmjo0KEKCQlRbGysEhIS9MADDyguLk4JCQlycXFRWFiYQkJC1LBhw0ppfFWq\nssBTyTMsjraT0/OAmufSZ7S7pDaFSvfLYsmtphYBAICarsIBzjDs/0K8fft2vfzyy5KkXr16admy\nZfL19ZW/v7/c3d0lSYGBgdq1a5d69uxZ8RZfI44Gnuqe2SpXMOP0PAAAAMCpVTjAHThwQE8//bR+\n/fVXjR49WufOnZOrq6skqUmTJjpx4oSys7Pl5eVl28bLy0uZmZlX3+prxYHAUyNmtghmQK1XFdfL\nVfcfoAAAQPlVKMDdfPPNeuaZZ9S3b1/99NNPGjZsmPLz823PF52dK6vc6RGgUAp+QUblqfzr5WrE\nH6AAAEC5VCjA+fj4qG/fvpKkm266SU2bNlVaWpouXLigevXqKSMjQz4+PvL29rabccvIyFBAQEDl\ntBxwAvyCjMpSZdfL8QcoAACcirkiG/3jH//QggULJEnZ2dnKzs7WwIEDtXHjRklSYmKiunfvLn9/\nf6WlpSk3N1d5eXlKTU1Vx44dK6/1gDO4/Avy5X+eV64OAAAAlKZCM3C9e/dWdHS0Bg8eLMMw9NJL\nL8nPz08vvPCC1q5dq+bNm2vAgAGyWCyKjo5WZGSkzGazoqKi5OHhUdl9AOoU7h0GAABQd1UowLm7\nu2vRokXFypctW1asLCQkRCEhIRU5DFBjVe+1bdw7DAAAoK6q8CqUQG1TnlBWnde2ce8wXAkztAAA\n1G4EONR6VXazcxZ/QI3EDC0AALUZAQ52Sgs7kvMufc/NzlGXMEMLAEDtRoCDnRLDjuT8S98TzIAK\n436GAADUHAQ4FOdg2OGXOqBu4H6GAADUHAQ4VFh1/lJXG0/1BGo0ZrEBAKgRCHC4OtX0S12tPdUT\nAAAAuII6FeBYXrt6VNlsGTMCwFXhOxEAAOdTpwIcy2tXj/LMltXl6+r4ZRrXXuV/J9blzzAAANdC\nnQpwLK9djRycLavbiyXwBwZcW1XxnVi3P8MAAFS9OhXgqpujMyx1fiamjp4ayR8YUGvU0c8wAADX\nAgHumnJ0hsXxmRhCIQBnxKmWAABUDAGuFFUReBydYSnfTEzlh0IAKKwqvg851RIAgIohwJXKscBT\n3TNbVRMKUVmq+/0BVI4q+gMQp1oCAFBuBLhSOB54atfMVomBQyJ0VFjten+gbqrOPwBV2W1IAABw\nUgS4q1T7ZrZKChySM4eOqpgFc3Sfte/9AVxb5bkNCQAAdQEBDnZKDhxSSaHDeU4PrIrTYZlZA0pS\nJd8LDp5qycIoAIC6gACHq3AVwUi6ql/qyrPPqjgdlpk1oDTVd/2wowujcFomAMCZEeBQYVcXjKSr\nm7Gq/H0SyoCrV+3XDzswW8dpmQAAZ0aAQ5WritMyy7NPADWPo0GvyhZWYgVMAICTIsChhuHaMgCF\nVe/CSlxXBwCoaQhwqFE4jRFAYVUxg1+eWb2quOE4oRAAcDWuSYCbMWOGdu/eLZPJpIkTJ6p9+/bX\n4rAAgDrF0Rn8cs7qOXC6ZXkWRqmKUAgAqDuqPMDt2LFDR44c0Zo1a3TgwAFNmjRJa9asqerDAgDq\nGEdn8KtiVq/cC6NcRSi8mpk6Zv8AwPlVeYBLSUlRcHCwJKlVq1Y6deqU8vLy5O7uXtWHBgDgKpXj\ntgilcOgUzhJC4YEDP+qPMztJDQoVnpa+idmhNm3aFtlngb766stix/7Tn3pVePbP0bBXnlBYFfsE\ngLqmygNcVlaW2rVrZ3vcuHFjZWVlEeAAADVe1dwupRynen5cQr2Y4qd5Hj58UIMG/VKsbkrKwSL3\nwCs5aJZU7miALE/QrOx9lhZcpeLh1dGQW959OhpIr1U7i9Yr7z6rM4xX9j7Lc2pzde/zWr3nSnt/\n1PSxLO8+r+b9fjXtLO8+HR2joq75IiaGUZ5Vww6VUtbMgbpXW68u7/NavO7Osk9nH8uq2KezjmVV\n7NPZx9JZ9lnz3x+X/rNtIemWYlsX/Y+4tLoVrfebko9vz5BWviTJp1BZRikrehrSxyXULRYgHa1X\n+fu8FFz3FKl3qW5Kys124bXkuo7WK71u1//rWCxopkzaeVX7rHg7i9cr7z4d6U95+15d+yyx3rXe\nZ9ePVfx1v6+a3nOlvz+qZCwd7Lsj9aqi7zXh/eHoGBVlMsqXqMptwYIF8vb21kMPPSRJCg4O1vr1\n63XddddV5WEBAAAAoNYxV/UBunXrpsTEREnSt99+Kx8fH8IbAAAAAFRAlZ9CGRAQoDvvvFMPP/yw\nLBaLXnzxxao+JAAAAADUSlV+CiUAAAAAoHJU+SmUAAAAAIDKQYADAAAAACdBgAMAAAAAJ0GAAwAA\nAAAnUWMDXF5eno4cOaIjR47ozJkz5d7+119/LVZW0notx48fL3Nfv/zyi0PHTElJcahefn6+fv75\nZ+Xn51fascujrHVrDMPQL7/8ouzs7Eo/NgAAAICKs0ybNm1adTeisD179ujZZ5/VypUrtXXrVn3+\n+edasmSJEhMT1a5dOzVt2tSh/YSHhysiIkKS9Nlnn+mxxx7TokWL9MMPP6hr166qV6+eJOnpp5/W\ngAEDbNtt3rxZTz75pL744gvdcccdevTRR5WQkKClS5fK19dXvr6+kqQPP/xQ+/bts/3bu3evXn/9\ndTVo0ED79u2Tn5+fbZ+vvvqq/vSnP0mStm7dqhEjRuirr77SW2+9pVtvvVU333yzJGnLli1atmyZ\nevXqpZSUFA0bNkyffPKJlixZoptvvtl2bEkKDAzUiRMn1K5duyveV++bb77Rk08+qQ0bNqhNmzZ6\n5plnNH/+fL377rtq27atbrzxRlvdQ4cOKSYmRm+++aYWLlyojRs36s0339SePXsUEBAgDw8PSdLF\nixf13nvvKT4+XitWrFBCQoK+/vpr5eXlqW3btjKbHfu7wLRp09SzZ09JUnZ2thYuXKgNGzbouuuu\nU4sWLWz1Xn75ZfXo0cP2+OTJk/r73/+uo0ePys/PT4sXL9Zbb72lb7/9VnfccYfq169f6jGHDh2q\ngQMHFivfsmWL7fXNycnR66+/riVLlujbb79V+/btbfs8ffq0tm7dqltuuUWnTp3S3LlztWLFihKP\n/eKLL6pp06by8fG54utw+vRprVixQocOHdLtt9+u1atXa/Xq1dq/f79uv/1223vVarXq008/1fLl\ny7V27VqtX79eu3btkslksntvSJUzRoXHR3J8jK5mfKSSx8jR8bn8ejoyRpU9PpLjY1TbPkPVOT6X\n98ln6DdXM0bV+R0n1d3PkMQYlaamjBHfcyWrSd9zjr6e5el3dfbnSmrcbQQGDx6sV199Va1atbIr\n//bbbzV9+nStWrXKVlb456IWL16sr776StKlMPfWW2+pYcOGeu+99/T+++9ryZIlatCggYYOHaq/\n//3vtu0GDRqk2NhYpaena/z48YqLi5Ofn5+ysrI0atQorVu3TpJ09913y9PT0+7L4r333lN4eLgk\n6ZlnnrGVFz7GI488opkzZ+qmm25Sdna2Ro8erTVr1kiSHnzwQS1evFhNmzbVkCFDNGPGDN100006\nefKknnzySa1du9Zun2PGjNHChQt1ww03aODAgQoICJCLi/2t/QYPHqy5c+fq119/1bBhw7R8+XL5\n+fnp559/1vPPP6/Vq1fb6g4bNkyvvvqqWrZsqYMHD2rdunUaO3asNm7cqHfeecf2ev/lL39Ry5Yt\n1atXLzVp0kSGYSgjI0OJiYk6deqUZs2aZdvn2bNnSxwfwzD00EMP6eOPP5YkRUZGqk+fPvLy8tLq\n1avVpUsXjR492tauFStW2LZ9/PHH1aFDB504cULZ2dm65ZZbFBISov/85z/avHmzlixZIkny8/OT\nt7e3XF1dbbOOmZmZatasmUwmk7744gu7vl8+RnR0tNq2bas+ffpox44d2rx5sxYtWiRJevTRRxUa\nGqrw8HBFR0erdevW+uMf/6hvv/1WX3zxhd566y3bPvv166d27dopNzdXQ4YM0V133VXiazFq1CgF\nBATo119/VWpqqgICAhQUFKQ9e/Zo7969mjdvnqRLX0w+Pj4KCgpSSkqKzp8/r9atW+ujjz7Sbbfd\nphdeeMG2T0fHyNHxKc8YOTo+5RkjR8enPGNU2eNTnjGqbZ+h6hyf8owRn6Ga/R1XnjGqbZ8hxqjm\njxHfc2WPT3nGqCq+5xx9Pcvz3qzO/lyRUcMMGjTI4eeCg4ONqVOnGvPnzy/2LygoqNTtPvvsMyM8\nPNzIzs42hg4davfckCFDSt0uIiLC9vO5c+eMv/71r0ZUVJTx888/G4ZhGA899FCJ7S58jKeeesru\nuYcfftj2c79+/YwzZ84YhmEYo0aNMqxWq2EYhmG1Wo2wsLBS9/mf//zHmDJlinHPPfcYAwcONB5/\n/PES6919992l9rVofwsKCuzaFh4ebvv5kUceKbGfJT135513Gr169bL717t3b6NXr15Gu3btSmxL\nQUGBMXbsWGP+/PkltvNyn6xWqxESElJqn7766itjyJAhRmJioq3MkTEaNmxYqfssPA5F2zV48OAS\ntzt48KAxbdo0o1+/fsbkyZONlStXGp9++mmJx7733ntLbVfR40VGRtp+HjBggN1zjo6Ro+NT9PhX\nGiNHx8cwHB8jR8fHMBwfo8oen5KOV9oY1bbPUHWOT9Hj8xm6ujGqzu+4ko5ZVz5DhfdpGIxRTRwj\nvudq/veco69ned6b1dmfK3EpO+JdWx06dNCoUaMUHBwsLy8vSVJWVpYSExOLJen58+frtdde08SJ\nE+2m9yUpMTHR9nNgYKCeeOIJzZ07V/Xr11dwcLDc3Nw0YsQI5eTk2G3XpEkTLV26VCNHjrTNjB07\ndkzLly/X9ddfb6vn5uamv/zlLzp48KBefvllderUSVartcQ+/fDDD3r22WdlGIaOHDmiDRs2qG/f\nvlqwYIEaNWpkqzdy5Ej1799f3bp1k6enp55++mkFBARo27Zttpm9y4xCE6ft27dX+/btJUknTpxQ\nZmam7blGjRopNjZWJ0+elK+vr1588UV1795d//73v9WkSRO7fbZp00Zjx46Vv7+/vv76a3Xq1EmS\nNHnyZLsZUZPJpE2bNqlXr15ydXWVJF24cEGJiYnFxuH555/XqVOnFBUVVex1uf/++20/u7i4aMOG\nDbrnnntkNpv1+uuva8KECZoyZYry8vLstrt8DeGNN96oyZMn28r37dunixcv2h53795dnTt31qJF\ni/Txxx8rJiZGJpOpWDukS1PfW7ZskSTVq1fPdhrs999/b3f8li1bavr06br//vvVuXNnbdiwQZ06\nddJXX32lZs2a2e3z8rFuueUWTZ06VRcvXtSOHTu0Z88eHTp0SH379rX158iRI/rll1906tQp/fvf\n/9bvf/97HThwwK4/hmHom2++Ufv27bV582bbFP9XX31VbObVZDIpMTFRvXv3vuIYOTo+l8fo008/\n1b333nvFMXJ0fMozRpfHxzAMubq6ljo+5Rmjyh6fomP05ZdfljpGpY3Pxo0br+ozVNL4TJ48uco/\nQ4XH50qfn5LG59NPP9Vdd91V4fEpzxg5Oj6Xj1/S91zRMSrvZ8iR77mq/gyV5zuutPEpzxhV1WfI\nkfEpzxg56/9DjNG1/Z4ra3zKM0Y19Xvuan9XqK7vucvKej3L839wdf7uc0XlinvXyPbt2425c+ca\nU6ZMMaZMmWLMnz/f2LVrV4l1z5w5YxQUFBQr//e//233+J///GexeqdPnzbeffddu7KzZ88an3zy\niV3Znj17jOXLlxvnzp0rtc0ffPCB8dxzz5X43LZt2+z+HT9+3DAMw1i/fr2Rm5trV/fkyZPGJ598\nYsTHxxuLFy82EhISbPULe++990ptS2F5eXl2f3n46KOPjKlTpxrLli0z8vLy7OparVbjs88+M956\n6y1j8+bNtvK9e/faZgMNwzCOHTtmxMTEGL179za6du1qdO3a1QgODjamTJliZGRklPjaXJ5ZLGza\ntGm2n9PT040XXnihWL2PPvrICA0NtSvbtWuX8eyzz9qVJSYmGv369TN2795d4utw8OBB48knn7T9\nVSQnJ8fu+ZiYGLt/KSkphmEYRlRUlLFp0yZbvYsXLxqrVq0yHnvsMaNv377GvffeawwZMsSIj48v\n1vcxY8aU2Jaix9++fbsxcOBA44knnjAOHDhgjBgxwujatavRr18/u/f9gQMHjKeeesoIDQ01nnvu\nOdvM77x584r1+/IY9enTxwgKCrIbo/T0dLu6H3zwQbH34eXXpKR9Fq370UcfGcHBwbbHqamppY7P\njh07Snw9rFarcejQIbsxOnbsmF1bYmJijPHjxxcbnw8++MBuX5fH6PHHHzdCQ0PtxqjwPi+PT+H3\nduG+XrZjxw7jwQcfLHF8ivbnSmNUuO6VxiczM7NYe4p+hrKzsw3DMIw5c+YU22fReuvXry92NkFJ\nY/Tpp58a999/v5GWllbs+IZh2Man8FhfVtLnJzs723jmmWeMbdu22dUtPD6XP0OhoaHG4sWLjbNn\nz9rqlfT5udzvooqO0fDhw43bb7/d6Nevn5Gammqrd3l8/vznPxvPPfeckZ6ebmRnZxtz5841Dh06\nZLfPomMUFBRU6hgVHZ+tW7cahmE/Ppf3WfR7buvWrcXGqKTx+eSTT4wHHnig1PExDKPYZ6iwksZo\n69atRlRUlN0YlfQdN3jwYCM+Pr7Y/4EljdHlvhe2Y8eOYt9xd/1/e/cb09T1hwH8aQGJMl03JjFs\nRt8YiDgiTljmdGmtNqYgcQNk8wVmY9lwvFpkcxlgnL5zskQNqBgR9Y0ZpReWxSiodbAtZmFZxrKN\nP8kyYkfUmWwLy2RSOb8X5t7fbXvbngtlUnw+iYmUh+85pze99NDbbwsKwo6PEMbH6OuvvxZHjx4N\nOkb630Pr1q0TBQUFIjs7W3z44Yfi9u3bYXPQH6OJiQnh9/vFxMRE2GNIf3zUnKIoMR9DExMToqWl\nRbjd7qiPoTfffFOsXLlSBAKBsO+HHqMvv/xS+P3+sMdR6DHatGmTcDgcorGxMegxJET4MdKvXS/0\nGFVUVIgXXnhBFBYWRjxG6nludHRU+P1+8cknn0Q9Ro/ieU7/OIrHeU5/jGKd5/THJ9Z5zuj5XOjj\nKJrLz9oAAAmHSURBVNJzOaPzXOgxEuLBc4VY5zn1uUK081zo8zm90HqRGJ3fnU6n4bqN1jM5OSl1\n3u7q6pI+bzudTqm5q2blBo4SV1FRUdyz8aypboZn+zynm+vq6hJ2u10UFBSI999/X4yNjWnf019q\n0d3dLex2u3j++eej5vQ1jbL6ywOi5WRqqsdIn5Vdj35NsbKy8zQztmzW5/MJl8sldu7cKQYGBkRR\nUZFwu93CbrcH/fFECCGuXbumZQcHB8XWrVuF2+0WDodD+Hw+w5pqrrCwMCwXKWs0vlFOvWwmVk6t\nFzq2fj0DAwNB65GpGZqb7noi1VQURSiKIrxer/Zv06ZN2u2hOX1+8+bNYTkz2enWdDqdEWt6vV6p\nsfXrjjS2WktmPWot9f9G96XR+IqiGNY8cOCA9v+vvvpK2O12UVpaKhwOh+jp6QmqGSm7ceNG8cUX\nX0TNlZWVheXMjG+Uc7lcYuPGjdLzdDgcMeeprsfM2mPN02hsM2u/du2aqK+vF0I82Njb7faI56Ro\n2atXr0bNFRUVheXMjG+UU89zsXJGc5zu2DNxH0WqmZeXJ/bv3y/u3LkjopHN/Zc1jV7cWL16tVTN\n3t5esWXLFrFjxw7x/fffi5KSErF+/XrhcrnCNuP6bH9/vygtLRUbNmwQLpdLXL9+PWpOranPRcpm\nZWUZZiOZdZdQ0uwXrXlM6Mc3yGZZM75jNzc3Q1EULFq0CB6PB5WVlVrjHqG7/PbEiRNarq2tLWIu\ntGZoVv/Sf7ScbE0g+DJhMzX1a4q2dtmaU11PtLGPHTuG06dPY3R0FLt27QprlqRvjtTU1KRlq6qq\nwrJq9y99zWg5maw6frxy+rH164m2dtmxZ2I9ANDY2BjWqGpiYgJ+vz/omBvl/v3337Ccmex0a05n\nnmbGbmpqmnJNozmaGX9wcDDoZ86ePRvUHGzDhg3SWbVLtGwOAIaGhqTGNzPPWDVl52mmppqVHdtM\nzSNHjuDEiRNhObUxm/68ECvrcDhM5cyMH6/cTI4dz/tIXzMnJwdbtmzB7t27ozbEk8391zVDrVq1\nSqpmY2Mjzpw5ozX4a21tRVZWlmGDv9BspGaAsjmz40fCDRyZ1traihdffNHwIx3u378/pSxrxnfs\npKQk2Gw2AMD27dvx5JNPorKyEsePHw/abOlz5eXlSE9PN8zFysrmzNSMxzzjsfaZGHvevHnIzMxE\nZmYmMjIytI8deeqpp5CamhpUUzb7MGvOtfUAwOeff46mpiYMDQ3hgw8+QGZmJnp7e4M6DJvJJUrN\nRFmP/vH0+OOPY+nSpQAevI899HEpmzVTU28u1JxKLlY2EAggLS0NALBw4ULtY4tsNlvYH79Cs2qr\n9tCsbG46NSPNczpjz9aaFosF+fn5aG1txQ8//IC2tjbU19cjLS0N6enpaG5uNpVLlJopKSnIyMhA\nRkYGFi5ciKysLADA008/jaSkpKD7KDSr/t4IzcrmzI4fCTdwZJps8xgzWdaM79iyjXvMNPh5VGvO\nxNiyzZLMZB9mzbm2HkC+UZWZhlaJUDNR1iPbHMxMljXjWzO0MVt1dXXExmyyTdzMNHubas1I85zO\n2LO1pn5DF60hnmwuUWqaafAnm52JmlFJXWhJFEK2eYyZLGvGd2zZxj2yuUe5ZrzHNtMsSTb7MGvO\ntfUYidaoaiq5RKk5W9djpjmYbJY141tTCPnGbGayD7PmXFuPbEM82Vyi1DTT4E82OxM1o5l1H+RN\nRERERERExqwPewJEREREREQkhxs4IiIiIiKiBMENHBERERERUYJgF0oiIprVmpubMTQ0hEOHDmm3\ndXR0wOv1wu/3Y/HixdrHAAghsGTJEnz88cda9qOPPsKFCxfQ29urdXD95ptv8M4772DlypXaz1mt\nVtTV1WHFihUx59Tf34/y8nI0NDTA7XZrtwcCATQ1NcHn82HBggX4+++/sXbtWtTU1GD+/PmG41os\nFpSWlqK4uHj6dxYREc153MAREdGs9sYbb+Dll19GX18f1q5di7GxMRw5cgQtLS2orKzEoUOHtM+i\nCnXv3j1cvXoV69atQ3d3NwoLC7XvZWVl4ezZs9rXPT09qK2txaeffhpzTu3t7XjllVfg9XqDNnAN\nDQ34888/0dbWhuTkZAQCAezZswcNDQ2oq6szHJeIiMgMXkJJRESzWnJyMvbt24f9+/fj/v37OHz4\nMEpKSrB8+fKwD6YNdenSJaxZswbFxcXwer1Rs2vWrMHw8HDM+YyPj+Py5ct47733MDAwgFu3bgEA\n7t69i/b2dtTV1SE5OVmb+8GDB7XNGxER0XRxA0dERLPec889h9zcXOzduxfXr1/HW2+9JfVzHo8H\nRUVFWL9+PX788Udts2XE6/UiLy8vZs2LFy9i9erVsNlscLlc6OjoAACMjIwgMzMTaWlpQfmkpCSp\nuRIREcngJZRERJQQampq4HQ6cfjwYaSkpATdnpqaqr2fbOvWrSgrK8ONGzfw888/46WXXkJKSgqc\nTicURUFVVRUAYHBwEBUVFRBC4Ndff0VeXl7Qe+ciaW9vx2uvvQYAcLvdqK2txdtvv42kpCQEAgEt\n19/fr9X77bffcOXKlbBxAcBiseDgwYNYsmRJfO4oIiKa07iBIyKihGCz2WCz2bBs2bKg2yO9B87j\n8cBqtWL79u0AgH/++Qd9fX3aBk7/XrTW1lb89NNPSE9PjzqHkZERfPfdd/jrr79w8uRJTE5O4ubN\nm/j222/x7LPP4vfff8cff/yBJ554Arm5uTh37hwAIDs7W9uw8T1wREQ0HbyEkoiIEkas97ypJicn\n0dHRgVOnTkFRFCiKgkuXLsFqtaKvry8sX1FRgV9++QU+ny9qXY/Hg/Lycnz22WdQFAWdnZ2orq5G\ne3s75s2bh9dffx179+7F+Pi49jM+nw+pqamwWCym1kBERGSEr8AREVHCUDdB+q/VSyiB/7fl37lz\nJxYvXoycnJyg/Kuvvgqv14tt27YF3W61WnHgwAFUV1cjPz8fjz32WNjYk5OT6OzsREtLS9DtJSUl\nKCwsxPj4OKqqqnD+/Hns2LED8+fPx7179/DMM8/A4/Focx8eHkZFRUVQjby8PLz77rtTu1OIiOiR\nYhH8UyAREREREVFC4CtwREREOpcvX8aZM2eCXu1TX9nje9eIiOhh4ytwRERERERECYJNTIiIiIiI\niBIEN3BEREREREQJghs4IiIiIiKiBMENHBERERERUYLgBo6IiIiIiChBcANHRERERESUIP4H4Qyp\nY4s36iIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc583ae9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_lab_subset = lab_subset.replace({\"CONCLUSION\": {\"CLINICAL\": \"UNCONFIRMED\"}})\n",
    "by_conclusion = _lab_subset.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\n",
    "counts_by_cause = by_conclusion.size().unstack().fillna(0)\n",
    "ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), \n",
    "                          figsize=(15,5), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39982, 15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22097"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of lab-confirmed cases older than 20 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60257048468117846"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(measles_data[CONFIRMED].YEAR_AGE>20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5, 10, 15, 20, 25, 30, 35, 40, 100]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_group = pd.cut(age, age_classes, right=False)\n",
    "age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])\n",
    "age_groups = age_group.categories\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Extract cases by age and time.\n",
    "age_group = pd.cut(age, age_classes, right=False)\n",
    "age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_group.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_slice_endpoints = [g[1:-1].split(',') for g in age_groups]\n",
    "age_slices = [slice(int(i[0]), int(i[1])) for i in age_slice_endpoints]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get index from full crosstabulation to use as index for each district\n",
    "dates_index = measles_data.groupby(\n",
    "        ['ONSET', 'AGE_GROUP']).size().unstack().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excludes = ['BOM RETIRO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5)        844130\n",
       "[5, 10)       830880\n",
       "[10, 15)      858750\n",
       "[15, 20)      904972\n",
       "[20, 25)      945244\n",
       "[25, 30)      902086\n",
       "[30, 35)      835888\n",
       "[35, 40)      764605\n",
       "[40, 100)    2841133\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_age = N.iloc[:8]\n",
    "N_age.index = age_groups[:-1]\n",
    "N_age[age_groups[-1]] = N.iloc[8:].sum()\n",
    "N_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# All confirmed cases, by district\n",
    "confirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\n",
    "confirmed_counts = (confirmed_data.groupby(['ONSET', 'AGE_GROUP'])\n",
    "                    .size()\n",
    "                    .unstack()\n",
    "                    .reindex(dates_index)\n",
    "                    .fillna(0)\n",
    "                    .sum())\n",
    "\n",
    "all_confirmed_cases = (confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n",
    "                       .fillna(0).values.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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', how='sum'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# All clinical cases, by district\n",
    "clinical_counts = (clinic_subset.groupby(['ONSET', 'AGE_GROUP'])\n",
    "                   .size()\n",
    "                   .unstack()\n",
    "                   .reindex(dates_index)\n",
    "                   .fillna(0)\n",
    "                   .sum())\n",
    "\n",
    "all_clinical_cases = (clinical_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n",
    "                      .fillna(0).values.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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', how='sum'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[0, 5)', '[10, 15)', '[15, 20)', '[20, 25)', '[25, 30)',\n",
       "       '[30, 35)', '[35, 40)', '[40, 100)', '[5, 10)'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups = np.sort(measles_data['AGE_GROUP'].unique())\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert clinical_counts_2w.shape == (28, len(age_groups))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-02</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-16</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-02</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-16</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-30</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-13</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-27</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-11</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-25</th>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>71</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-08</th>\n",
       "      <td>91</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>109</td>\n",
       "      <td>105</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-22</th>\n",
       "      <td>212</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>250</td>\n",
       "      <td>209</td>\n",
       "      <td>65</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-06</th>\n",
       "      <td>380</td>\n",
       "      <td>62</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>547</td>\n",
       "      <td>435</td>\n",
       "      <td>155</td>\n",
       "      <td>46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-20</th>\n",
       "      <td>567</td>\n",
       "      <td>104</td>\n",
       "      <td>46</td>\n",
       "      <td>146</td>\n",
       "      <td>765</td>\n",
       "      <td>652</td>\n",
       "      <td>252</td>\n",
       "      <td>61</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-03</th>\n",
       "      <td>815</td>\n",
       "      <td>146</td>\n",
       "      <td>119</td>\n",
       "      <td>178</td>\n",
       "      <td>904</td>\n",
       "      <td>728</td>\n",
       "      <td>250</td>\n",
       "      <td>70</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-17</th>\n",
       "      <td>835</td>\n",
       "      <td>167</td>\n",
       "      <td>107</td>\n",
       "      <td>167</td>\n",
       "      <td>753</td>\n",
       "      <td>737</td>\n",
       "      <td>262</td>\n",
       "      <td>71</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-31</th>\n",
       "      <td>571</td>\n",
       "      <td>200</td>\n",
       "      <td>89</td>\n",
       "      <td>169</td>\n",
       "      <td>742</td>\n",
       "      <td>716</td>\n",
       "      <td>257</td>\n",
       "      <td>77</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-14</th>\n",
       "      <td>417</td>\n",
       "      <td>150</td>\n",
       "      <td>103</td>\n",
       "      <td>171</td>\n",
       "      <td>690</td>\n",
       "      <td>616</td>\n",
       "      <td>252</td>\n",
       "      <td>79</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-28</th>\n",
       "      <td>219</td>\n",
       "      <td>111</td>\n",
       "      <td>51</td>\n",
       "      <td>104</td>\n",
       "      <td>421</td>\n",
       "      <td>348</td>\n",
       "      <td>159</td>\n",
       "      <td>51</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-12</th>\n",
       "      <td>151</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>66</td>\n",
       "      <td>231</td>\n",
       "      <td>247</td>\n",
       "      <td>103</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-26</th>\n",
       "      <td>97</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>103</td>\n",
       "      <td>113</td>\n",
       "      <td>61</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-09</th>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-23</th>\n",
       "      <td>49</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-07</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-21</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-04</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\n",
       "ONSET                                                                           \n",
       "1997-01-05       0        0         0         0         1         0         0   \n",
       "1997-01-19       0        1         0         0         3         4         0   \n",
       "1997-02-02       4        1         0         0         2         1         0   \n",
       "1997-02-16       4        0         0         0         2         1         1   \n",
       "1997-03-02       9        0         0         2         4         5         1   \n",
       "1997-03-16      13        2         1         3        12        17         4   \n",
       "1997-03-30      17        2         2         3         6         5         1   \n",
       "1997-04-13      18        3         0         1         7        10         3   \n",
       "1997-04-27      17        3         4         5        23        10         4   \n",
       "1997-05-11      31        4         5         8        32        37        11   \n",
       "1997-05-25      59       10         7        11        71        49        12   \n",
       "1997-06-08      91       11        18        18       109       105        31   \n",
       "1997-06-22     212       28        18        38       250       209        65   \n",
       "1997-07-06     380       62        35        92       547       435       155   \n",
       "1997-07-20     567      104        46       146       765       652       252   \n",
       "1997-08-03     815      146       119       178       904       728       250   \n",
       "1997-08-17     835      167       107       167       753       737       262   \n",
       "1997-08-31     571      200        89       169       742       716       257   \n",
       "1997-09-14     417      150       103       171       690       616       252   \n",
       "1997-09-28     219      111        51       104       421       348       159   \n",
       "1997-10-12     151       50        31        66       231       247       103   \n",
       "1997-10-26      97       21        20        25       103       113        61   \n",
       "1997-11-09      71       32        14        10        84        64        34   \n",
       "1997-11-23      49       20         2         4        40        42        25   \n",
       "1997-12-07      16        3         3         7        26        33         9   \n",
       "1997-12-21      23        3         1         4         9        22         4   \n",
       "1998-01-04       7        0         0         1         5         3         3   \n",
       "1998-01-18       0        0         0         0         1         2         0   \n",
       "\n",
       "AGE_GROUP   [35, 40)  [40, 100)  \n",
       "ONSET                            \n",
       "1997-01-05         0          0  \n",
       "1997-01-19         0          0  \n",
       "1997-02-02         0          0  \n",
       "1997-02-16         0          0  \n",
       "1997-03-02         0          1  \n",
       "1997-03-16         0          0  \n",
       "1997-03-30         0          0  \n",
       "1997-04-13         1          0  \n",
       "1997-04-27         0          1  \n",
       "1997-05-11         5          0  \n",
       "1997-05-25         3          4  \n",
       "1997-06-08        10          3  \n",
       "1997-06-22        17         10  \n",
       "1997-07-06        46         17  \n",
       "1997-07-20        61         32  \n",
       "1997-08-03        70         25  \n",
       "1997-08-17        71         25  \n",
       "1997-08-31        77         37  \n",
       "1997-09-14        79         25  \n",
       "1997-09-28        51         29  \n",
       "1997-10-12        27          9  \n",
       "1997-10-26        16          3  \n",
       "1997-11-09        11          5  \n",
       "1997-11-23         1          2  \n",
       "1997-12-07         5          0  \n",
       "1997-12-21         1          1  \n",
       "1998-01-04         1          2  \n",
       "1998-01-18         0          0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_counts_2w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-19</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-02</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-16</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-02</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-16</th>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-30</th>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-13</th>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-27</th>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-11</th>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-25</th>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-08</th>\n",
       "      <td>57</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-22</th>\n",
       "      <td>126</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>107</td>\n",
       "      <td>105</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-06</th>\n",
       "      <td>239</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "      <td>254</td>\n",
       "      <td>183</td>\n",
       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-20</th>\n",
       "      <td>359</td>\n",
       "      <td>92</td>\n",
       "      <td>51</td>\n",
       "      <td>108</td>\n",
       "      <td>403</td>\n",
       "      <td>380</td>\n",
       "      <td>147</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-03</th>\n",
       "      <td>475</td>\n",
       "      <td>125</td>\n",
       "      <td>68</td>\n",
       "      <td>140</td>\n",
       "      <td>534</td>\n",
       "      <td>463</td>\n",
       "      <td>179</td>\n",
       "      <td>67</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-17</th>\n",
       "      <td>639</td>\n",
       "      <td>179</td>\n",
       "      <td>116</td>\n",
       "      <td>197</td>\n",
       "      <td>593</td>\n",
       "      <td>514</td>\n",
       "      <td>214</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-31</th>\n",
       "      <td>581</td>\n",
       "      <td>214</td>\n",
       "      <td>155</td>\n",
       "      <td>278</td>\n",
       "      <td>568</td>\n",
       "      <td>537</td>\n",
       "      <td>217</td>\n",
       "      <td>83</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-14</th>\n",
       "      <td>465</td>\n",
       "      <td>248</td>\n",
       "      <td>161</td>\n",
       "      <td>252</td>\n",
       "      <td>522</td>\n",
       "      <td>445</td>\n",
       "      <td>206</td>\n",
       "      <td>67</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-28</th>\n",
       "      <td>315</td>\n",
       "      <td>165</td>\n",
       "      <td>135</td>\n",
       "      <td>160</td>\n",
       "      <td>289</td>\n",
       "      <td>281</td>\n",
       "      <td>103</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-12</th>\n",
       "      <td>213</td>\n",
       "      <td>92</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-26</th>\n",
       "      <td>142</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-09</th>\n",
       "      <td>112</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-23</th>\n",
       "      <td>78</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-07</th>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-21</th>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-04</th>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-18</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\n",
       "ONSET                                                                           \n",
       "1997-01-05       3        1         1         0         0         0         0   \n",
       "1997-01-19      30        3         1         1         1         3         2   \n",
       "1997-02-02      22        4         0         2         1         1         1   \n",
       "1997-02-16      21        2         2         2         2         1         1   \n",
       "1997-03-02      24        5         2         5         2         2         2   \n",
       "1997-03-16      39       11         4        10         7         2         3   \n",
       "1997-03-30      33        5         2         3         3         1         0   \n",
       "1997-04-13      24        3         1         4         6         3         1   \n",
       "1997-04-27      31        5         2         4         5         6         2   \n",
       "1997-05-11      36        8         4         5        10         9         6   \n",
       "1997-05-25      40       15         5         7        22        23         6   \n",
       "1997-06-08      57       15         9        13        48        48        16   \n",
       "1997-06-22     126       26        14        37       107       105        30   \n",
       "1997-07-06     239       35        44        66       254       183        79   \n",
       "1997-07-20     359       92        51       108       403       380       147   \n",
       "1997-08-03     475      125        68       140       534       463       179   \n",
       "1997-08-17     639      179       116       197       593       514       214   \n",
       "1997-08-31     581      214       155       278       568       537       217   \n",
       "1997-09-14     465      248       161       252       522       445       206   \n",
       "1997-09-28     315      165       135       160       289       281       103   \n",
       "1997-10-12     213       92        39        56       135       135        60   \n",
       "1997-10-26     142       52        28        18        77        77        36   \n",
       "1997-11-09     112       45        15        17        38        24         9   \n",
       "1997-11-23      78       21         9        10        32        20        11   \n",
       "1997-12-07      74       12         4        13         6         6         6   \n",
       "1997-12-21      57       10         8         4        11         7         5   \n",
       "1998-01-04      31        5         2         2         2         2         1   \n",
       "1998-01-18       2        0         0         0         0         0         1   \n",
       "\n",
       "AGE_GROUP   [35, 40)  [40, 100)  \n",
       "ONSET                            \n",
       "1997-01-05         0          0  \n",
       "1997-01-19         1          0  \n",
       "1997-02-02         0          1  \n",
       "1997-02-16         0          2  \n",
       "1997-03-02         1          0  \n",
       "1997-03-16         0          2  \n",
       "1997-03-30         1          1  \n",
       "1997-04-13         0          2  \n",
       "1997-04-27         2          0  \n",
       "1997-05-11         1          2  \n",
       "1997-05-25         1          2  \n",
       "1997-06-08         2          2  \n",
       "1997-06-22         9          8  \n",
       "1997-07-06        19         17  \n",
       "1997-07-20        26         31  \n",
       "1997-08-03        67         23  \n",
       "1997-08-17        45         43  \n",
       "1997-08-31        83         54  \n",
       "1997-09-14        67         50  \n",
       "1997-09-28        29         38  \n",
       "1997-10-12        20         17  \n",
       "1997-10-26        11          7  \n",
       "1997-11-09         3          5  \n",
       "1997-11-23         5          0  \n",
       "1997-12-07         2          2  \n",
       "1997-12-21         1          1  \n",
       "1998-01-04         0          1  \n",
       "1998-01-18         0          0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_counts_2w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obs_date = '1997-06-15'\n",
    "confirmed_obs_t = confirmed_counts_2w[confirmed_counts_2w.index <= obs_date].values\n",
    "clinical_obs_t = clinical_counts_2w[clinical_counts_2w.index <= obs_date].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june = Model()\n",
    "model_july = Model()\n",
    "model_june_noconf = Model()\n",
    "model_july_noconf = Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 844130,  830880,  858750,  904972,  945244,  902086,  835888,\n",
       "        764605, 2841133])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_age.values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Downsample annual series to observed age groups\n",
    "downsample = lambda x: np.array([x[s].mean() for s in age_slices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "invlogit = T.nnet.sigmoid\n",
    "\n",
    "confirmation = True\n",
    "\n",
    "def specify_model(model, observation_date, confirmation=True):\n",
    "\n",
    "    with model:\n",
    "\n",
    "        n_periods, n_age_groups = confirmed_counts_2w.shape\n",
    "\n",
    "        ### Confirmation sub-model\n",
    "\n",
    "        if confirmation:\n",
    "\n",
    "            # Specify priors on age-specific means\n",
    "            age_classes = np.unique(age_index)\n",
    "\n",
    "            p_age = Beta('p_age', 1, 1, shape=n_age_groups)\n",
    "            p_confirm = p_age[age_index]\n",
    "\n",
    "            # Confirmation likelihood\n",
    "            lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, \n",
    "                                      observed=confirmed)\n",
    "\n",
    "\n",
    "        '''\n",
    "        Truncate data at observation period\n",
    "        '''  \n",
    "        obs_index = confirmed_counts_2w.index <= observation_date\n",
    "        confirmed_obs_t = confirmed_counts_2w[obs_index].values\n",
    "        clinical_obs_t = clinical_counts_2w[obs_index].values\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",
    "        if confirmation:\n",
    "\n",
    "            clinical_cases = [Binomial('clinical_cases_%i' % n, i, \n",
    "                               p_age, \n",
    "                               shape=n_age_groups) for n,i in enumerate(clinical_obs_t)]\n",
    "            I_age = [Deterministic('I_age_%i' % i, clinical + confirmed_obs_t[i]) \n",
    "                                   for i,clinical in enumerate(clinical_cases)]\n",
    "            I = T.stacklists(I_age)\n",
    "\n",
    "        else:\n",
    "\n",
    "            I = confirmed_obs_t + clinical_obs_t\n",
    "\n",
    "        # Calcuate age distribution from observed distribution of infecteds to date\n",
    "        age_dist = Dirichlet('age_dist', np.ones(n_age_groups), \n",
    "                             shape=n_age_groups)\n",
    "\n",
    "        age_dist_like = Potential('age_dist_like', \n",
    "                        Multinomial.dist(I.sum(), age_dist).logp(I.sum(0)))\n",
    "\n",
    "        # Transmission parameter\n",
    "        beta = Uniform('beta', 1, 100)\n",
    "        \n",
    "        A = 75./(beta - 1)\n",
    "        lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])\n",
    "        natural_susc = Deterministic('natural_susc', T.exp((-1/A) * lt_sum))\n",
    "\n",
    "        # Weakly-informative prior on proportion susceptible being \n",
    "        # between 0 and 0.07\n",
    "#         natural_susc = Beta('natural_susc', 2, 100, shape=n_age_groups)\n",
    "        p_susceptible = Deterministic('p_susceptible', \n",
    "                            downsample(sia_susc) * downsample(vacc_susc) * natural_susc)\n",
    "\n",
    "        # Estimated total initial susceptibles\n",
    "        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible, \n",
    "                       shape=n_age_groups)\n",
    "\n",
    "        S = Deterministic('S', S_0 - I.cumsum(axis=0))\n",
    "\n",
    "        # Force of infection\n",
    "        λ = Deterministic('λ', beta * I.sum(1) * S.sum(1) / N_age.sum())\n",
    "\n",
    "        new_cases = Potential('new_cases', Poisson.dist(λ[:-1]).logp(I.sum(1)[1:]))\n",
    "\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from theano import config\n",
    "config.warn.reduce_join = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run models for June 15 and July 15 observation points, both with and without clinical confirmation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_iterations = 100000\n",
    "n_burn = 90000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied logodds-transform to p_age and added transformed p_age_logodds to model.\n",
      "Applied stickbreaking-transform to age_dist and added transformed age_dist_stickbreaking to model.\n",
      "Applied interval-transform to beta and added transformed beta_interval to model.\n",
      "Assigned NUTS to p_age_logodds"
     ]
    }
   ],
   "source": [
    "model_june = specify_model(model_june, '1997-06-15')\n",
    "\n",
    "with model_june:\n",
    "    june_trace = sample(n_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "July 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model_july = specify_model(model_july, '1997-07-15')\n",
    "\n",
    "with model_july:\n",
    "    july_trace = sample(n_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, no lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june_noconf = specify_model(model_june_noconf, '1997-06-15', \n",
    "                                  confirmation=False)\n",
    "\n",
    "with model_june_noconf:\n",
    "    june_noconf_trace = sample(n_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "July 15, no lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_july_noconf = specify_model(model_july_noconf, '1997-07-15', \n",
    "                                  confirmation=False)\n",
    "\n",
    "with model_july_noconf:\n",
    "    july_noconf_trace = sample(n_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of model output\n",
    "\n",
    "Age-specific confirmation, June model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x7fc520dbc400>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VhroMwJZqDRSX+/d//3dt27at/HmIi89VpKenq3PnzpWOtyxLP/3pT3XNNdeoffv2Sk5O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\nAMYIFAAAwBiBAgAAGPtfisrIJVirumAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc520dbcb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc3 import traceplot, forestplot\n",
    "\n",
    "forestplot(june_trace[n_burn:], vars=['p_age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab confirmation rates, June model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc51f5cc780>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M01RklCvUZYSd9vb3CAAAAASCz1B00kknKS8vT1VVVfrwww/1/vvvq0+fPsGorcOx7fol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\n8fdxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5210d6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_age = pd.DataFrame(june_trace.get_values('p_age'), columns=age_groups)\n",
    "\n",
    "f, axes = plt.subplots(figsize=(14,6))\n",
    "sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n",
    "          color=sb.color_palette(\"coolwarm\", 5)[0],\n",
    "          order=age_group.categories)\n",
    "axes.set_ylabel('Confirmation rate')\n",
    "axes.set_xlabel('Age group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x7fc520427f60>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8jokLLz4+Rj7fsXCXgQbQo8hHj2pr6AMNIR4kl8ulgoICjR07VpL02GOPhXR8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc522905358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(june_trace, vars=['p_susceptible'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model with no confirmation correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x7fc51d12c2e8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8p2PiwkhMjJPffyTSZeA06E/0o0d1NfaBhhAPkcvlUmFhocaOHStJevTRR8M6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5213e9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(june_noconf_trace[n_burn:], vars=['p_susceptible'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity estimates at June and July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x7fc51c7aa748>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SffHFF1q4cKHVQ59wEyYkqKLiHy+JYcRr2bKAPJ5QBKsCAMB5LA0gb775pt5//339/ve/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\ngAAAANsRQAAAgO3+H4CRYdmy5HyiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51d970320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(june_trace[n_burn:], vars=['λ'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x7fc53c6345f8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EAIAWZNkakHbt2mnOnDkN7jvdeL0heb3V4S4DAADHsuRtuH6/Xzk5OfW2l5SU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\nLawX5oTKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51c83ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(july_trace[n_burn:], vars=['λ'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transmission paramter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc51f8387f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc51cf9d390>]], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FiA7zfQMkv9Foy7WKVmb+akGhVNnieh+GAYrKVW6TPHCZZdR4upCnKYUXrtch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\n0uGAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc521faba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traceplot(june_trace[n_burn:], vars=['beta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc522143400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc51d09fd30>]], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CeH+6B9LOmwWtlVQoYbLYwALL41qJpKACrEmTJmHGjBmYNWsWsrOzgz74oUOH\nUFZWhs2bN0OlUmHmzJkeARaLxcIHH3yAmJjOT3NNiDenypvnXPG4bDw+u2cFV048Lhv3TB2InDQx\nPtp9Hm9tOYlZk3JR9DtaJoF0P3w+HxMmTMCmTZvwyCOPRLs4AIDT5fLAG3URFmv0h3/VyPVIlATf\nmDwXwnC58trQhzV1RHtuSFtsDtdQsFAXS/a1JpHJYkNcy9DA1u1qhcaEFL0F5yqVHiMYqn30xgUi\nV3kuEK1uGXaaHB+DiisaZCbHhZxcKdA6cO4MIQ5BPX1JAQfDgMdh+1xY2r3XrrJei9TE2DZD1wJh\nGAbHyxpdj+Pj+IiN4eJCldrVq+mrB1RrtOBkN/gcaZ2x9NQlBTKS2mYNbL2IeDA9ija7w6MXKpRr\nojXnvDMGjKvXMdKCCrC2bNmCb775BsuXL0dMTAzuvPNOFBYWgs/339V27bXXYsSIEQAAiUQCo9Ho\n8eHja8w7IdFwsVqFd78oAZvNwtJZIzAgq3sPCwxk/PB0ZCTH4Z3tp7H1x3JYrA7MuKFftItFSECf\nf/65x+O6ujrU1we3PktnCDTsrisxWaNf1hq5DjXy4Bv3akPwQ+6atO1vlLXHsQtyZKd4T0ut1ptR\ncUWDnDSxx7AzX43NWrkOJoPFa9a1YCg0Jp/PafXNdeg+BM0SxFBApcYEcSwPRosdQj7XZ4DyW50G\nNrsDCo0Jap0FI3LbjpJgGAY2uwM87tV1xowta0CFItRmpLMnpXWPjsPRXJ4mrTnkgLD1MMrL9Vpk\nyTznPLVn2GtXZ7LY2/R8BpNKviyI+Vzeepy7k6ACrJSUFDzwwAN44IEHUFpaiuXLl+Oll15yLewo\nEHi/88RmsyEUNo/j3bp1KyZMmNDmzs7KlStRXV2NgoKCkIdhEBIuNY06rNt6CnY7gz/PGt5l1riK\ntH7pEqy4bwzW/OcYvvy5OSsPBVmkqzt69KjHY5FIhLfeeitKpel+2rPYrK9kDt2drx6g9rLa7bh0\nxfdQyvomQ5skEt7Y7A5crG3O2tbfzxxgTUuiDh6X0xKwBPe3dQap7hklg9nzQrUKbBbLFZyMGtA2\nlb+TM1OmzcccGucwwFEDkl0Jl/z12lT4qFebw+FaUDjY1+9NcanvmzTuLVeGYTzWqQKurvvkVKvQ\nQ+pnHbmewrnmVKgsNu+fJ96G+XZXQSe5OHjwILZt24ajR49i+vTpmDlzJvbt24elS5e2GdPe2t69\ne7F9+3aZ6d9kAAAgAElEQVRs3LjR4/dLly7FjTfeCKlUikWLFmHPnj2YNm2a32NFIpViT0N1FJh7\nHTU0GfDW56dgMNvw+N2jMbmgTxRL1vlkMjHW/OlGrFj/C778uQJxsXzcXZhP11EQqI6iY9WqVdEu\nQq9hdzigbhlO1h0FmndibGfK6o4wmT0bl97K6P6rSwGGOR690IhxQ9JQelnlNQuizeFo0yPjTILg\nHmxr3HoGbfbmRBneEgy4l/dEmRzSOO+BhM5t6J5zwV93ziFfRrMdMXyuR0ZHb/xlD3QeP5jFh9vD\nvYeqvEbTJvuctxsQXXGAVnuSfXSmcM8Ti6agAqwpU6YgKysLc+bMwSuvvAIer3kSZm5uLvbu3et3\n359++gkbNmzAxo0bIRJ5XvgzZsxw/XzTTTfhwoULAQOsjqxT0Bu4r5tBvHOvI53RilX/PgqF2oQ5\nkwZgeE5Cr6w/FoAn7xqFNf85hv/sOY9YIQ/XD6aEM/7Qey2wcAeg3kZBuNu3b19Yz0eauWduIx1X\nLdchyy1ldmVdx9OJOxyM3xTzoQ4FdU8KEYgqhNT2/nSk98kplKGmFqsDAh4n8Ibw7FmTa9pm09N6\nGbbanmQ3kear56g3K69RR28drI0bN4JhGPTt2xcAcPbsWQwZMgQA8J///MfnfjqdDq+++io+/PBD\niMXiNs89+uij2LhxIwQCAY4cOYLCwsJ2vgxCQme22rHu85O4ojCgcGw2bv5d7+q5ai0pPgZP3TMa\nL39yFBt3liCGMxzXDOx+a3+Rnsvf941G0/Wz8HVX7RlSSPxzHwrlPUtfaHV+/GLXT4gQSAczu4dM\nqTW1Sb4QTl0tM2hP6h0Kp3Asi+FNUAHW9u3b0dDQ4BqW8f777yM7OxtPPvmk37uJ33zzDVQqFR57\n7DFXcotx48Zh4MCBmDJlCgoLC3HXXXchLi4OgwcPpgCLdBq7w4H3vzyD8hoNxg1NxexJA6JdpC4h\nOV6IpbNGYM1/jmPDzjN4+t5remQmRdI9ZWZmun4uKytDU1Nzz4rFYsFLL72Eb7/9NlpFIyQkgdb2\nCVU4F2COFhY6OcIiJIKCCrAOHTqEzZs3ux6vW7cOc+fODbjfnDlzMGfOHJ/Pz5s3D/PmzQumCISE\nDcMw+PeeCzhRJseQvgl4cPpgrwsJ9lZ90yR46r4CvLTpENZ9fgrPzh8TcEI2IZ3ppZdewi+//AK5\nXI4+ffrg8uXLePDBB6NdLEI6TG+ywmS2Q9WBlNTdTaPKCB7Xd8p0QrqjoK5mq9UKi+Xq+FK9Xg97\nD7hbQnqnzd9fwP4TteiTKsLimcNDTsfaG4wdmoa5U/Kg0Vvw9uenXGtIENIVlJSU4Ntvv0V+fj62\nbduGf/3rX9DrA6cGJqSrO31JgYs1qogNW+qKlFoTSioUOF3ufZFYQrqjoHqw5s6di+nTp2PYsGFw\nOBw4ffo0lixZEumyERJ2/+9kLf7zXSmS42Pw+OyRHgvkEU9TC7JRpzTgx2M12Pj1OSy6fVjIC2gS\nEgkcTvPEdKvVCoZhMGzYMKxevTrKpSKEdISvdO6EdEdBtS5nz56N8ePH4/Tp02CxWHjmmWeQnp4e\n6bIRElYny+T4ePd5iGP5WHbXKMSLev4aFR119+/zUNOox9HzjfjqwG+4dTytkUWiLzc3F//+979R\nUFCABQsWoF+/ftDpAmcPKy4uxtKlS5GXlweGYTBo0CA8/PDDWL58ORiGgUwmw9q1a12ZcgHAYDDg\n6aefhlqthtVqxeLFi3HDDTdE8uURQgjp5oIKsMxmM86ePQudTgeGYfDLL78AAGbNmhXRwhESLuW1\navxzRwm4HBaee/h3SIrlBd6JgMthY9HMYXjxw8P44qcKZKWIMDqPMguS6HrhhRegVqshFovx1Vdf\nQalU4n/+53+C2nfs2LFYt26d6/EzzzyDefPmYdq0aXjzzTexbds2jznGX3zxBfr374/HH38cDQ0N\nuP/++ymZBiGEEL+Cmnzy0EMP4eOPP8aRI0dw9OhR179grF27FnPnzsXs2bPx/fffezx34MABzJ49\nG3PnzsX69etDLz0hQahXGrBu6ylY7Q48OmMY8nMSo12kbkUSy8ef7hwBPpeNDbvOoqax42u2ENIR\nc+bMwVdffQWNRoPbbrsNDzzwANLS0oLal2mVqri4uBiTJk0CAEyaNAkHDhzweD4xMdGVrVCtViMx\nkT4/CCGE+BdUD5bNZvPIIhisQ4cOoaysDJs3b4ZKpcLMmTMxdepU1/Mvv/wyNm3ahJSUFNx3330o\nLCxEbm5uyOchxBeN3oI3t5yEzmjF/TcPwqi85GgXqVvqkyrGg38YjPe+PIO3t53C3+6/FiIh9QKS\n6Hj66afx7bffYubMmcjPz8eMGTMwefJk8Pn8gPuWl5dj0aJFUKvVWLx4MUwmk2tIYFJSEhobGz22\nLyoqwhdffIFp06ZBq9Viw4YNEXlNhBBCeo6gAqwBAwagqakJCQkJIR382muvxYgRIwAAEokERqPR\ntR5WVVUVpFIpUlNTAQATJkzAwYMHKcAiYWO2NC8k3KAy4tbr+2LCqMzAOxGfxg5ORXWjDl8dqMQ/\nd5Rg2V0jwWFTBkbS+caMGYMxY8bgr3/9K4qLi7Fz5048//zzOHjwoN/9cnJysGTJEhQVFaGqqgrz\n58+HzXY1Q2br3i0A2LlzJ9LS0rBhwwaUlpbib3/7G7Zu3RqwjBIxLW0QCNVRYFRHgVEdBUZ11PmC\nCrDq6uowbdo05ObmurI3AcCnn37qdz82mw2hsPmPunXrVkyYMMGVhUwul3sMtUhMTERVVVXIL4AQ\nb+wOB/75ZQkqrmhxw/B03H4jJWcIh9tv7I/qBj1OlMnxf/8twz1TB0a7SKSX0mg02Lt3L3bv3o2q\nqircddddAfdJTU1FUVERACA7OxvJyckoKSmBxWIBn89HfX09UlJSPPY5duwYbrzxRgBAfn4+6urq\nXDcK/ZZP23vSbLeHRCykOgqA6igwqqPAqI6iI6gA65FHHunQSfbu3Yvt27dj48aNPrfxdufQG5lM\n3KGy9Aa9vY4YhsE/tpzAqXIFrslPwRPzCtqsddXb6ygYvuromQVjsfwfP2Hv0Wrk909C4bi+nVuw\nLoSuo+h46KGHcPHiRUydOhWPPvoorrnmmqD227VrFyorK7FkyRIoFAooFArccccd2L17N2677TZ8\n9913rmDKKScnBydOnMDUqVNRU1OD2NhYWq6AEEKIX0EFWGPHjsW+fftQXV2N++67D5cvX0Z2dnZQ\nJ/jpp5+wYcMGbNy4ESKRyPX7lJQUj7Hu3u4cetPYqA3qvL2VTCbu9XW046dL+L74MnLSxHioKB9N\nSs8FSKmOAgtUR4tmDMVLHx/FP7edgoDDwtC+vW/iP11HgUUqAJ0/fz5uuOEGjxEVwZg8eTKeeOIJ\n3H333WAYBn//+9+Rn5+Pp59+Glu2bEFGRgZmzpwJAFi2bBlWr16Nu+66CytWrMC8efNgt9vx4osv\nRuIlEUII6UFYTBBdR6+++ioqKytRW1uL7du3491334VSqcTf/vY3v/vpdDrcc889+PDDD71mXrr1\n1lvx/vvvIyUlBXPnzsXrr7+OnJwcv8ekBo1/vb3Rt+94DT7+7jxk0hismFeA+Li2k957ex0FI5g6\nulClwmubj4PH5WDFvDHITI7rpNJ1DXQdBdZbe/j2H6umITkB0LClwKiOAqM6CozqKLBbJ+SF/ZhB\n9WAdPnwYW7Zswbx58wAAixcv9lgnxJdvvvkGKpUKjz32mGvM+rhx4zBw4EBMmTIFK1euxLJlywAA\nt9xyS8DgihB/jl9sxCd7zkMk5GHZnFFegysSPgOzpVhQNBj/+9VZrNt6Es/OL4CE6pwQQgghvVxQ\nAZZAIAAA17hzu90Ou90ecL85c+Zgzpw5Pp8vKChoV/p3Qlq7WK3Ce1+eAY/LxmOzRyI1MTbaReoV\nrhuWhvomA3b+8hv+se0Unrx7NAS80IZtEUIIIYT0JEHlWL7mmmvwzDPPoKGhAf/6179w3333YezY\nsZEuGyFBqWnUYd3WU3A4GCy6fTj6Z0iiXaReZcYN/TBuaCrKazXYsPMMHI7gEtYQ0l6lpaW44447\ncPPNNwMA3n33XZw8eTLKpSKEEEKaBRVgPf7445gwYQKuu+461NXVYcGCBVi+fHmky0ZIQAq1CW9s\nOQmD2YYF0/MxIjcp2kXqdVgsFh6cPhiDcxJw/KIcn35/IeisoIS0xwsvvIBXXnkFMpkMADB9+nSs\nWrUqyqUihBBCmgU1RLCqqgpDhw7F0KFDPX4XbCZBQiJBZ7TijS0n0KQ1Y86kAbh+WHq0i9RrcTls\nLJ45HKs/PYYfj9cgUSLAH67rG+1ikR6Ky+UiPz/f9bhfv37gcoP6OiOEEEIiLqhvpPvvv981/8pi\nsUCpVCIvLw87duyIaOEI8cVsseOtrSdxRWFA4dhs3Py7PtEuUq8XG8PF43NG4pVPjmDb/ksQx/Jx\n08iMaBeL9EBcLhdVVVWu76X9+/dTrykhhJAuI6gA64cffvB4fPHiRXz++ecRKRAhgdjsDrz7xWlc\nqtXg+mFpmD1pQLSLRFokiAV4fM4orP70GD7aXQqhgItr8wOvb0dIKJ566iksWrQIFRUVGDNmDDIz\nM7FmzZpoF4sQQggBEGSA1VpeXh7OnDkT1LalpaX405/+hAceeAD33nuvx3OTJ09GRkYGWCwWWCwW\nXnvttaAWGya9l4NhsOnrcyipUGJEbhIeKMoHu+UuNukaMpLj8PickXj1s+PYsPMMYvgcDO9Pc+NI\n+OTn52PXrl1QKpXg8/kei9gTQggh0RZUgLVu3TqPx3V1ddBoNAH3MxqNWLNmDcaPH+/1eRaLhQ8+\n+AAxMTHBFIP0cgzDYPN/L+Lg2XrkZkqw8PZh4HKCytNCOlm/dAmWzhqBN7acxLvbT2PZXaMwMFsa\n7WKRbm758uWuYYHerF27thNLQwghhHgXVIDF4XiuazNo0CA89thjAfcTCAR4//33sWHDBq/PMwxD\n4+ZJ0L7+tRJ7j1QjIzkOS2eNpPWWurhBfRKw6PZheGf7aby55SQemz0Cg/okRLtYpBu7/vrrO7R/\ncXExli5diry8PDAMg0GDBuHhhx/G8uXLwTAMZDIZ1q5dCx6P57Hfzp07sXHjRnC5XPz5z3/GhAkT\nOlQOQgghPVtQAdbChQu93jV0OBwAADbbey8Cm80Gn8/3e+yVK1eiuroaBQUFWLZsWTDFIb3QvhM1\n2P7/LiFJIsCyOSMhEvIC70SibuSAZCy8fRj+uaMEb249iaWzRmJwDgVZpH1mzpzp+vnChQsoKysD\ni8XCoEGD0L9//6COMXbsWI9RGc888wzmzZuHadOm4c0338S2bdswd+5c1/MqlQrvvvsuduzYAb1e\nj7fffpsCLEIIIX4FFWCNHDkSdru9ze8ZhgGLxcK5c+fadfKlS5fixhtvhFQqxaJFi7Bnzx5MmzbN\n7z4ymbhd5+pNelod/XKqFv/+7jwkcXy8tHA8slI6/vp6Wh1FQrjqqFAmRoI0Fqs+Oox1n5/C3x4c\ni1EDe8ZcS7qOomPNmjX473//i+HDh8PhcOD111/HLbfcEtTIitajJoqLi/HCCy8AACZNmoRNmzZ5\nBFgHDhzA+PHjIRQKIRQKXdsSQgghvgQVYC1evBgDBgzA+PHjwWKx8OOPP6K8vBx/+tOfOnTyGTNm\nuH6+6aabcOHChYABVmOjtkPn7OlkMnGPqqMzFUqs+/wkeDwOls4aAQGr49dAT6ujSAh3HfVLicOS\nO4bhne0l+PsHh/DIrUNQ0M2zC9J1FFikAtBDhw7h66+/dg3ls1gsmDt3blABVnl5ORYtWgS1Wo3F\nixfDZDK5jpOUlITGxkaP7WtqamA0GrFw4UJotVosXrwY1113XfhfFCGEkB4jqADr4MGDWLhwoevx\n9OnTMX/+/A6dWKfT4dFHH8XGjRshEAhw5MgRFBYWduiYpGcpq1HjH9tPAQD+dMdw9EuXRLlEpCNG\n5CZj6ewReGf7afxzRwnunpKHKQW0WDkJXXJyssfCwjweD1lZWQH3y8nJwZIlS1BUVISqqirMnz8f\nNpvN9by3OcEMw0ClUmH9+vWorq7G/Pnz8eOPPwY8l0QsDPLV9F5UR4FRHQVGdRQY1VHnCyrAUqlU\n2L9/PwoKCgAAR44cQVNTU8D9Tp48iWeffRZKpRIcDgebN2/GnXfeiaysLEyZMgWFhYW46667EBcX\nh8GDB1OARVwu12vx1paTsNkYLJ45DEP6Jka7SCQMhvZNxF/uuQZvbT2J/+y9CKXWjFkTcynVPglJ\nQkIC7rzzTowbNw4Mw+Dw4cPo06ePa27V0qVLve6XmpqKoqIiAEB2djaSk5NRUlICi8UCPp+P+vr6\nNkuFJCcnY/To0WCxWMjOzkZcXByUSiUSE/1/Jmm0xjC80p5LIhZSHQVAdRQY1VFgVEfREVSA9eKL\nL2L16tV4/PHHAQADBw7EypUrA+43cuRI7Nq1y+fz8+bNw7x584IsKukt6pQGvPF/J2Aw2/DHW4Zg\n9EBZtItEwignTYy/zhuDN7acxO5Dl9GoMuKhPwxGDL9dy/KRXig7OxvZ2Vd7PydOnBjUfrt27UJl\nZSWWLFkChUIBhUKBO+64A7t378Ztt92G7777DjfeeKPHPuPHj8eKFSvwxz/+ESqVCgaDIWBwRQgh\npHdjMSHkSXcmtYgmmvPgX3efF1KnNGDNf45BrbPg3qkD8fsxgYf9hKq711Fn6Iw60hmteHf7aZyv\nUiFLJsKf7xyOZGn3GcZA11FgXS0JiF6vxxNPPAG1Wg2GYbB48WLk5+fj6aefhsViQUZGBlatWgUO\nh4Nly5Zh9erV4PP52LJlC7Zu3QoWi4VFixYFDOj2H6umO8YB0F31wKiOAqM6CozqKLBbJ+SF/ZhB\nBVilpaVYsWIFDAYDdu/ejfXr12P8+PEYOXJk2AsUCDVo/OvOjb76luBKpbNg7uQBmDa2T0TO053r\nqLN0Vh3Z7A589t+L+PFYDURCHhbdPgz53SSNO11HgUUqwHr//ffxwQcfQKfTAeh4Rttw8xZg9c+I\nh9FkwxWlPkql6lqo0RcY1VFgVEeBUR0FFokAy/sCVq288MILeOWVVyCTNQ/VKioqwqpVq8JeGNJ7\nNTQZsPaz41DpLLgrgsEV6Vq4HDbmTRuE+TcPgtFsw2ubT+C74su0ADnxa8eOHdixYwdKSkpQUlKC\nM2fOoKSkJNrFCojLobmGhBDSGwQVYHG5XOTn57se9+vXzyODEyEdUSPXY/Wnx9CkNWPOpAEopOCq\n15k4KhNP3TMa4jge/u+HMrz35RmYLLbAO5JeKS8vD2lpaeBwOB7/eqK4GFpUnRBfOBwWkiXdZ2h5\nZ0gQCaJdhC4tPq5z6ifoAKuqqso1/2r//v1B32EuLS3F1KlT8emnn7Z57sCBA5g9ezbmzp2L9evX\nh1Bs0lNUXNFgzadXhwXe/DsKrnqrvCwpVj5wLfKy4nG4tAEvfXwUVxQ0nIq0ddttt+HWW2/FU089\nhWeeecb1ryfKSQt9mGV+nwTEx/IjUJrQpCXG9tgAMVEcE+0ikBaclp5hykbbrH8GLWnjS3K8EHlZ\n8Z1yrqC6oZ5++mksWrQIFRUVGDNmDDIzM7F27dqA+xmNRqxZswbjx4/3+vzLL7+MTZs2ISUlBffd\ndx8KCwuRm5sb2isg3db5y01Y9/kpmK12LCjKx40jM6JdJBJlUpEAy+8ejS0/lmHvkWq8+NERPDh9\ncLdflJiE15o1azBjxgykpqZGuygh6ayBr1KRAFfk0b85kSiJQd80Cc7+poTGYIl2ccKqX7oESq0p\n2sUgALJkIjgYBgkiAS5Uq6JdnKjjcTlgs1hwhGGofaI4JmLXeYo0Fg0qg99t4mJ40JusYTtn/05c\nTzWoAEsqlWLXrl1QKpXg8/kQiURBHVwgEOD999/Hhg0b2jxXVVUFqVTq+oKcMGECDh48SAFWL3Gk\ntAH/+9VZOBwMFs4YRg1o4sLlsHHPlIHonyHBh9+WYv2OEtw8tg/unNgfHHZQne6kh+vTpw+WLFkS\n7WKE3aDsBJyvCrzGpD/9Mzrn7qy7gVlSrw3bYPoTeBwOrHZ7+Avlh5DPhbGDQ5B5XO+fRf3SJai4\nounQsUnwBDwOeFw2cjPifTbE2SwWWCwW7A6Hx+8H90nAucsde79FU7ZMhKpGXcSOz2WzMSAzHsWl\nkQmw+mdI0KQ1wWp3+NzG32fIyNxknCyXh3RONpsFh6NzbnUF1Vp58sknAQCJiYlBB1cAwGazwed7\nH6Ygl8s91hJJTExEQ0ND0Mcm3RPDMNh96DL+uaMEbDYLS2eNoOCKeDVuSBr+Nr8AqYmx2F18Ga9+\ndgIqnTnaxSJdwMiRI/H222/j559/xq+//ur615WxgIBdWPGi0If1iWJ4GDckDdKWeQWJ4ub/O3NJ\nlURJDMbmp2LsYM8eRefL9bf8gkjIRXJ82+eDmScRziFhY/yst9gvhLveqQmx4ShOr5QlC9y+HJLj\nuQbdsNxk188sH83xUQOSUTDI8+87on8SxHGRHUbLjfANwUyZKKLnEAq4YLO77rBLoYCLUQOSA28Y\nJUH1YPXr1w9PPfUURo8eDR7v6njqWbNmha0gwc7p6mrrqnRFXbWO7HYHNuw4jW8O/IZESQxWPjwO\n/TM7/24r0HXrqCvpCnUkk4nxdv9kvP1/J/DLqVq88NERPHVfAYZ3kQ/VrlBHvdHhw4c9/geaA4rr\nrrsuWkXy0N6Gv7f9hPzgknfk5yTA7nC4enn7potRUctA3UlD8/w1xFKkQlyqVXt9jsViYUBmPOTq\nq2mkhXwuOAEadjKpEDKpEGd/U4Zc1tbVnCiOAY/ru55bl0QsbG6YD+uXhNLKJtgcvu/Ak+DE8Lht\n5ut56w0Ux3puIxRw4ezDiY3hQiYVolHlmZKcxWp7wyE2hhfxbLUjByTh6IVGj99lyUSoDqLXKT6W\nH9R7t3+GBGU16rAMB2yj68ZWLjF8zzCGy2YHfj920uvyG2CVlpYiPz8fVqsVHA4H+/fvR0LC1TVq\nOhJgpaSkoLHx6oVXX1+PlJTAPRm07ox/XXVtHp3Rivd3nsGZCiWyZCI8NnsExHx2VMraVeuoK+lq\ndfRg0SBky+Kw9ccy/PW9X3DHTf1RNC4nqpOau1oddUWRCkA/+eSTNr/77rvvInKu9hg7LA17DlwC\nh812DUvictjgxYZ+t5nH5aBgUAqOnA88wsN9CG0Mn4vBfRNx8GwdACBJEgOFpn1DfYbkJOJsZeiB\nTEckSSI398O9Ldo/XYJESWgJK5yJR0RCHgryU1x17K8HJjtFjKqG5s+LSA/t6uoykuJQ2yqB0ai8\nZDRp/Y9QcA718ycnVdwmwHJKTYhFfZPvOT8dHd6ZHC/0uFHgLWjPSI4LGGClJ8bBagsuaE+UxGCs\nJMZ1DTr1TRPj0hUNkuOF4Mfw2rUOVrTjK18JcoR8Lvr6SP4zvH8Sjpc1xxZBBVsR5DfAeuWVV/Dx\nxx+71ryaP38+3nvvvbCcODMzE3q9HrW1tUhJScG+ffvw+uuvh+XYpGuprNPine2nodCYMCI3Cf9z\n21AIBZTmnwSPxWJh2rXZ6J8uwT+/LMG2/ZdQelmFP94yBJIID/MgXU9tbS3+/e9/o6mpef6ExWLB\noUOHUFhYGOWSNRPwOBg3JA0VVzQeDTqpSIAR/ZNw6pKizT45qc0NBm+NQC7HMzBzDgdU6YMfMpuX\nJYWiVSPMeaxAx+Hzgg8MA02uHzs4FcXn6v0eo1+6pEMJQfhcDiw23/O6WKzmYMhqcyDFy5C+7BQx\nUhOEXoPa7BQxRELvDb/WjX+xkA+tsbkXQiTkYVi/JFisdpgs/uecBQoEIsXbHMBRA5Jxosz3PBdn\nIzaGzw16aQ2pSOARYHkbIgqgzZBa57w3f3MVuRw2UhNiIYnl42KN57xAsZCHej9TroIZVpsgEqDJ\ny1D1LJkIWTKRR4DlTTA3BYUxXNj07et5zm4J8lMSYl3XtjQhDgqFHuZW74n0xDi/C5+Lo5yJlNNq\nvpTzhpVIyEN8EKnorxkoQ3Gp/8+aSPL7qdnR7tOTJ0/i1ltvxWeffYb3338ft956Kz788EPs3bsX\nALBy5UosW7YM9913H2655Rbk5OR06Hyka2EYBj+dqsUr/z4KhcaEGTf0w59njaDgirTbgKx4rFxw\nLYb3T8KZCiVWbirGuXYMESLd29NPPw2pVIoTJ05g2LBhUCqVWLNmTbSLFZTYGJ7XFN/pSXEAgMzk\nuIDHaM9cLV8SJL4bKmwWC8P7J4V0vNF5vucyOY85or/vIb4j+id1+KbJALeh5wKe96F/WTKRz7lV\nmclx4HLYyO+TAHEsP+QeLm/i4/gQCXlIlMQgNsb/d2C0euaFgrZ1FSixkEwqRL80CYb2TfC7nbvW\nC247h4O6jwpt3RuYIo1Ffp/mc4iE/uuvX7oESfFX/2bOniRBy3DbWEH7lg6QxgkwqE/b18nncpCe\nFL65d+7Dgn1dv75keulF5XHZGNIvsc3vM5J9lzlBJECmLPBnUUeFsiZVoIXaY3itrosod8H5vUpb\nR/OhBlwjR47Erl27fD5fUFCAzZs3h3RM0j3ojFZ8/N15HCltgFDAxcLbh3XpyYik+5DE8rF09gjs\nKa7Ctv3leG3zCUy/LgczbujX5k4/6Zk4HA4eeeQR/PTTT7j33nsxa9YsLF261OeSIE7FxcVYunQp\n8vLywDAMBg0ahIcffhjLly8HwzCQyWRYu3atx1xjJ7PZjFtuuQWLFy/G7bff3qHyi2N5ruFvaYmx\nvu/g+9GeNnifFDEuN3gOa/V3GGfSilAW/faVXc9dbAwXcULPYUvOnjR+iA1Kj3Nz2Bg5INnjc2DU\ngGQcCtBj5otUJIC01Z3yUKpdEne1B6v1cbuLQNcZiwWkJrZtqPvqHcnLlLoCndYkcXzI4oVIlgoR\nHwtIi3EAACAASURBVMdHvfJqL1441nYSx/IxOCcRcQECXF98tYCzZHFhyXDrrBuRkIeGptCH9Pnj\nHqjJpEL0T5f47bFzDyT99RgmiJqDzvJadZuhmd56+1rPLeuXIUZSfAwcDgYXa1RtUrcLBVzoAqRp\nd55naL9Ejx4vNouF/D4JYAFRyRYZ0lXWmVmJSPd1+pICm745B7XOggFZ8Xj4liFI8ZNFipBQsVks\n3Py7PsjLjsf7X57B179WoqRCiUduHeLqCSA9l9FoRE1NDVgsFqqqqpCRkYG6urbD37wZO3Ys1q1b\n53r8zDPPYN68eZg2bRrefPNNbNu2DXPnzm2z3/r16yGVSsP2Gpz6pkV2XZYBGfGuBBTt7Rny9d0v\nkwqR5qVxHdqxm/9vnaQjVMkSITKSY13B1fD+SWCY5rKzwAITplXIgmkGjR4gg9pgQXJ8DGrk3ufb\n9E2T4Le6rp/O3T1Y9VaPMV6CpcxkEbJTRJCrTW1S8Dsb0+6cPVgsFgu5EU58FR+GIeWBhqC2F5vN\ncg0/ddY7n8eB2Rrec4mFvDbv6dSEWMTGcL3OQUsQCyCO5UPrJelG35ZeYJGQ53Pum7vW70IOm42E\nlsynSfFpAOARYOWkidEYYNjloD4JcDgYsNmsNnXlvJmRIo2FNIw9/8HwG2AdP34cEydOdD1WKBSY\nOHEiGIYBi8XCvn37Ilw80p2o9RZs/bEMB0rqwGGzcOeE/ij6XU6XTvNJurfcjHj8/cGx+M/eC/jl\ndB3+/q/DmDN5ACaNzqQbQj3YH//4Rxw+fBgPPfQQZsyYAQ6Hg1tuuSWofVuPxCguLsYLL7wAAJg0\naRI2bdrUJsC6dOkSKioqMGHChPC8AH/lC/Px3FOk+5o7FIiAx4EohtfmTnJuCGtu5WbEw+LW+Bnc\nNxEGvRl9Uq9OVg85uHKrrAFZnmXxNUHen8xkEfhB9MD54vzEEfA5SOH7v6kYrk+nUOY+BRJokJI4\nltdmwWiZ2/XVOgBLS4p1JfZwx2azkC0TgcNhw2i2ISOIYbGR0Po7IlEswCUf27JZLPRJDX6ZIn94\nHLbH2k/OpB/uGRIzZXFgsZp7uI9dbPR2mLBKTRAiNobnM8lHemKs1wDLKUUqjMj6b1wOG6MHyMDh\nsHDay9xVJ2c7U8DjQBYvbDOM2r0HlM1iudbCC/dCxh5l9/fk7t27I3JS0rPYHQ7sO16L7f/vEoxm\nG3JSxVgwPd/ji5OQSBEKuHjoD0MwMjcZH+0uxb/3XMDxC41YMH1wWOZOkK5nypQprp+Li4uh1+sR\nHx9cY7+8vByLFi2CWq3G4sWLYTKZXEMCk5KSPLLbOq1duxbPPfcctm/fHlI5I5UFms9tHkbUpDO3\neyK6oOXOuL/05O6G9U9qk6ksFLJWoxjihDwMzvEzbyfCKbS9yU7xkwWwJfNfqMP70hJjvQ5dDpSG\n3p9EcQz6pIpQVq1G33QJSip8Nzzba2CW1DVfekjfRNQ26pGaGOsRYOVlSv3eyPL3Er3NFYo2b38n\nkZAHndGKgvwU19y4jl6ag3MSUVKhcCWD6ZMqbtNe4nLYXaoN5f5dKosXtulViuQNTV9DSn0Jphd0\nZMuUlXqlARV1UQiwMjMzI3JS0nOcqVDi/34oQ3WjDkIBF/dNG4iJozKp14p0uoL8FORmxuPDb0tx\n+pICf9t4CHf/fiDGD0+j3qweQqfTYevWrViwYAEAYPPmzfjss8+Qk5OD5557DsnJ/ud55uTkYMmS\nJSgqKkJVVRXmz58Pm+3q3X9v84x37NiBa6+9FhkZGT638UYmE6PJaIPJ3rx9UlIcklrmWpkZoMlg\nc23nzmpzQFKn8zgOAEjEzetICQVcDMpNBsMAOVlmxIsEIX3eOo8zbng6mjQmpCXF4YrKezr01mVz\n7uvr+dbbJSeJ/Gb78pfGnyvgQdLkWS5xLB96kxUOB4MEaSySEuMgURoDHksiUXs0iuOEvJCXEJDJ\nxBjRMgypzfFbXm9iYlyb4/o6T1KSCA3aq8HK74al4VBJcwArlcZCb3W0HNszMOVy2BiTn4IYARfZ\nmc1Doy7Lm4dU3TAqE8fPN0BvbF+DUSYTQ9LQPG9qUO7VZCUyALk5zclOnNeKVCxA/gDPhCbDBjKo\nvKJBbk4i4kUCmBxXr3MA6J8ZH1K9W8GCQm91lc3JYrVDcqXte6Q159/F77XRso1ULGh+/S2PUxNj\nIUsQIile6Bq15TQCLFysupqhMCcrAaKWGx3u7xGZTIxB/Wy4Itd7/A4A+EI+LrTMCwpUJwVD2a5t\nM2RxqG3Ut9kv0GuVycQYM5SF8io18volu+Y6ut6ryWLECXl+j+N8rmB4Bg6frQeXw0Jmeryrblp/\nPmRnSGCv9ezVkooEAIfjt6yuv4lI4LFNfL0OJosd0oTYsC0B4n6NhVvE07mtWrUKJ0+eBIvFwooV\nKzB8+HDXc5MnT0ZGRkbzGGkWC6+99lpQa2GR6Ktp1GHLj+U4fUkBFoDxw9Mwa+KAsIxvJqS9EsQC\nPDZ7BH46dQWf/fciNn1zDkfON2B+4SDqzeoBnnvuOaSnpwMAKioq8MYbb+Ctt95CVVUVXn75Zbz5\n5pt+909NTUVRUREAIDs7G8nJySgpKYHFYgGfz/e6HuP+/ftRXV2NPXv2oK6uDgKBAGlpaQEXNW5s\n1KKpyeBK5KBQ6OFoGcqlVOpdv/e2llpGQgxKLzeBy766VmAsj4U6pQHZSYmQu83rUZhDaxyI+GzE\nCrjQqAzgtJzf2xo5LLDalK31dr7Wgbv6mnWweEnyAAReR06tM3ucj81iYUh2PI5f0MFss0PAAbgM\n47ceXeXRmDyGrtkstrCuYSfgAI0qI6wmYUjHdX99WrXR9TiWx4JGa4RELPTYZkhOYnPiDI0RzrM4\n3OpAqdBBozEGNeQpL1OKhiaDR8IBuVwbsD4Zmx1aowUsh6PNNnFcFgZmiGExWtBotKCp6ep1Pm5I\nmt/jemM1WaHRGhEfJ/DYz2qze9RLoOvQ77XRsk1+psTjvSARcOCI43ndl+e23+gBMhj1Zhhbljpo\nXa4EIRcJ2fGu3l/n8ZRunw2B6oQLYEh2PBwOBmar3et+/o7lfK/xAORnSaB2m+Pk3E8u1/3/9u49\nKOrz3AP4d+83lsuyF1xAUFCwKtZUTRVveAxIk0kax4Q0iYxnTpI2YGJGJsFqYlI7HVJscdKhqfWI\n0/bkTI0k0aM5EzCe2EQrCkZDotYkYiQgctkFFvcG7PKePxZWWJbbshfYfT4zjrA33n323ff9Pb/f\ne4FZzMecGfIRX2fgsR3tJiTHhDmf53o/4JgTKeYObzMEHIauu1aI+LwxPzceG1rHDF0WdPfaIeIC\nbVLPhju7am83e7RH2Hj4NMGqqalBfX09Dh8+jLq6OuzatWvIqoEcDgcHDx6EWEwHPtOFwdSD/zlz\nE5/WNoExYF5CFB7PSHZuvEhIoHE4HKxepMUPEqLwl4rr+LLOcTUrZ90crEqbQVezprGGhgaUlJQA\ncGwsvGHDBqxYsQIA8OGHH475/BMnTqC+vh5bt26FXq+HXq/Hxo0bUVFRgYcffhiVlZVYtWrVkOcM\nTtpKS0sRFxc3ZnLlzkSqXWSYCEtS1EOuliTGhCNWKRv3kL6RjGfYkSpSgplqz9v0gbk43lzVM9HN\nkuoDrx/IDccBx2bFcaqwCS+p7Xy+y1w2ZYQEUXIxLDYGo9HqHEo2nkVKBiIh4HERLhOOuLl0dIQY\nAgEXBi9vczH4Mx8YYhjh4TBWqViAJSlqN/XI+5/3REfdLE1VgzH3wwrdSYwJH1I/PFlwhsvl+Lyu\nj2cV0PGIiZa6LatCLoZcKnQubDGa6b7HpU8TrKqqKudY+aSkJHR1dcFkMkEmc0xoZIxNeq8t4h+9\nNjtO1jTgf6vqYe2xY0a0FI9lJGNRUjQdsJIpSRkpQUHOD3Hmyzt495Nv8ZePruPCtRbkbkiBxs3m\nomTqk0rvfW7V1dXYtGmT8/fxtEPr1q1DQUEBfvazn4Exhl/96ldITU1FYWEhjhw5Aq1Wi0cffRQA\nUFBQgKKiIgiFnnfysSqZc0Ws8EEHmeMpq7sDt8kmV2MRC/iw9toQLhVO6kDrh8lKmKy9k9rzUC4T\nQi4VIkImRF8fc7sSrVTMx9y4SMjGWLwjTiVDQ5sRCRo56lvujjrXyhMcDsfj5ArAsIU1BhYjSVbJ\n0XjHMOrGzSPVpPEsaCIRTvzzGZg7Np45ZJFhokktiw64/x4M7Iek8mB7A28Za0EW173uXFfbFHsQ\ne+DefKRoL4/IGGtvNsCxx914ErxkbYSz/g0sJjGAx+MgOsKz/lcd5Vg0ZTzJ2VTg0wRLp9NhwYIF\nzt+joqKg0+mcCRbg2Gy4sbERS5Yswfbt231ZHOIBxhg+/7oNR07fgM5gRZhEgKczk7B6kZb2HCJT\n3sDVrAWzFPivyq9RW6fH7rJqPJyeiKxlM6kOTzN2ux16vR4mkwmXL192Xl0yGo0wm81jPBuQyWTY\nv3//sNsPHTo07Lbf//73w27bunXrhMorEvCcQ6Omg3kJUdB3WaGMcH/wJhUJYB7HkESRkDfhiemu\nuBwO5icO3xxVKOSh22aHoP+7O56hv7GqMMyIloHL5UzJbRzGc4oyygd7Zwn4XPxorgqff+NY2GUg\naRjtIHqWNhyNrcZxJ6m+mDbA4XCm/Pdqbrz3t3QY4M33nqSNGDWBH2y8JxEGJ2vzZyn6Vw/loOOu\ndVL1IVYpgzpS7NUTTb68PuDzOViDuV6t2rZtG1atWoXIyEjk5eXh5MmTyMzMHPU1vDWxLZh5K0bf\nNRnwn8eu4Ks6Hfg8Dh5dm4zH18/1eKnfqYTq0diCKUYqlRy/fl6Js7VNOHDsK7z/6U1c/LoNP9+Y\nhoVJnm+AHUwxmg6effZZ/OQnP4HVasXWrVsREREBq9WKJ598Ejk5OYEu3rQV1b9QhkjIG3XJ7LSk\nya0k6A1zYiPQ0mGBVjmxs+DTdeGlhBg56poMiBshoeFwOJg3M8rjDZoHx0XA52J+osLt3lYDRAKe\nz/eq8paoMNGYVze9bd7MKJ9faXZHFSGB2IMrxq6re06GQi5G+13rkLrI53GdJzKl4slfOfZ2bJUR\nEhhMIy8/Pxk+TbDUajV0Op3z99bWVqhU91adeeSRR5w/r169Gt98882YCZY3J6YGo7EmDo9Hp7Eb\nRz+7ibNf3gGDY7hHzrpkaBRSWIxWWIzux3RPF96IUbAL1hilxobj1/+xDOWn63Cmtgk73/4n7v+B\nBo9nJE942EGwxsibvJ2ArlmzBmfPnkV3dzfCwhydtVgsxiuvvIKVK1d69W/5UlSYCLcAJEyRZZhT\nZo6yXPoUIxTwvD7EbypTRUrGPAh2t1IjY465cGNxHerm6bL/U1Eg6vVoq2a6WjAr2jnccbKmQtI7\nNz7SueHvdMHlcjAnzjdXG32aYKWnp6O0tBSPP/44rl69Co1G4xxDbzQa8Ytf/AJlZWUQiUS4ePEi\nsrKyfFkcMgZLtw0f1zTgowvfo7vXjliVDDkZyVgwOzrQRSPEa2RiAbZkp2L1Ii3+++OvceFaC774\nVoesZfHIXDpzXGPRSeAIBALnvlUDplNyBTiG0E31IU5jEQXgLH2wS9DI/bL9l9ZlmOT98zS+/6NT\nVKwyDJJJDmf1VDCMBnI1nZIrX/PpkcTixYsxf/58PPHEE+DxeNi9ezeOHj0KuVyO9evXIysrCzk5\nOZDJZJg3bx4lWAFitvbi1MVGfHyxASarDeFSAZ74t2SsTJsx5kROQqar2dpw7MpdgrNf3sEHn93E\n8X/ewv993ogHlydi3X2xHg+5ISQUuFvVj0yOt+eHqRUS6LosUEZIoOvfGHaGQjZsJclQXqgqlK6G\nThcp8VFoaDVCGcBFTLyBw6bZMn40JGd0Exm21NhqxNmv7uDMl02wdNshE/ORuTQe65fET2r1p6mO\nhnaNLdRi1N1jx6nPG/DR+e9h7rYhTCLA2sWxWHdfrGNjRDdCLUaeCOU5asFaN+6ae6AzWJEYI5/U\ngTl9f+CczzZvZpTboWWexOhft9phMPdAIRcPWWjBbO2FudsGhVwcVFcZvF2P2jotqGsyYFGSMmiO\ng+i7NjZf9FXBUXvIuLV2mPFlnR7nrjTjVrPjCxcuFeChjERkLI71eOlQQqYzkZCHB5cnYu3iWFRW\nN+D0pUZ8eO4WPjpfj2Xz1FixYAZSEyLpii4JeXKpMKjm6UwJXryCNEsbjoZW47CrVFKxAFJx8A1J\n87bxzHkjZDzoaDqIMcagN1hR19SFbxs7ceW7drR2OIYJcDjAoqRorEybgUXJSlqumhA45mdtXD0b\nDy5PQNWVZnx8sQFVV1tQdbUF4TIhls1T4745KiTHBX5CMSGEuBIL+T6btE8IGT9KsIJEH2PQdVrw\nzZ27uPJtK75vMeLWnS50me/tWSIW8rB4jhILZkdj8RzliEOfCAl1IgEPaxfHYvUPtbjRaMD5ay2o\n+VcLTl1sxKmLjRALefjhXBWSZoRjTlwE4lRhQTXshhBCCCGeowRrGrpr7kGTzoTbOhMa20xoaL2L\nxjYTunvsQx4XJRdhSYoKs7URmK0Nx2xtOF2pImQCuBwO5sZHYm58JJ5cPwfXbnXgq5t6XLmpx/kr\nzTh/xTGHQiTkIUkbjoQYORI0cszUyKGOkoxr13tCCCGEBBdKsKYom70PbZ0WtHRY0NpuRnO7GXf0\nZtzRm4ZclQIcB4EzoqWIU4dh3qxoKMIEmKmWI9wHO6gTEqr4PC7SkqKRluTYtsDG4aKqthE3Gg24\ncduAa7c6cO1Wh/PxQj4XMQoptEoZYqKlUEdJoI50/C8T80N65S5CiGO59Ca9CTLaGoKQoOPzb3VR\nURFqa2vB4XCwc+dOLFy40HnfuXPnsG/fPvB4PKxevRp5eXm+Ls6UwRiD0dILncHq+NdpQWunBa0d\nFrR1WqDvsg7bD4MDIDpCjEVJ4dCqZIhThkGrlEGrlEHAd1yZotViCPGPGUoZVqVpsSpNC8CxStf3\nLUbUt9zF9y13cVtnQrPejO9bjcOeKxLyEB0uhiJcBIVchMgwEaL6/w+XCREhEyJcJqQrzl5WXV2N\nbdu2Yc6cOWCMISUlBc888wxefvllMMagUqlQXFw8bJ+t4uJiXLp0CXa7Hc899xweeOCBAL0DEkxm\n9l/tJoQEH58mWDU1Naivr8fhw4dRV1eHXbt24fDhw877f/Ob3+DQoUNQq9V4+umnkZWVhaSkJF8W\nyecYY+jutaPL3Iu7ph50mXrQaepB591udBq70X63G+1dVrR3daO71+72NSJkQiTHRkAT5TjbrVFI\nMUMhhUYhgYA2dyRkSpKKBUhNiEJqQpTztr7+hWaa281o7Rh6AqW9y4omnWnU15SIeJCJBQiTCCCT\nCCAV8SER8SEV8yER8iAW8SER8iEROX6WivgQC3mOFcNEPGov3Fi2bBneeust5++//OUvsXnzZmRm\nZmLfvn14//338cQTTzjvv3DhAm7cuIHDhw+js7MTjz76KCVYhBBCRuXTBKuqqgrr168HACQlJaGr\nqwsmkwkymQwNDQ2IjIyERuPYQXzNmjU4f/68XxKs9i4rem19YHAkRADQxxw/9/Ux2PsY7HYGe18f\nem196LE5/u/utcPabYO1xw5rjx0may/MVhvM3TYYLb3Of722vlH/vkzMhyZKAkW4GMpIMVQREsf/\nkRKoIiQQBWhXcUKId3E5nFGX/bV029Bp7EbHXce/TmM3uky96DI7Ts4MtCm3daYx2xV3+DwupGI+\nZGJHUiYVCRzJmag/KRPyIRbwIBLyIBRwIeTzIOBzIeBxweNxwONyweNywOEAXC4HXA4H0RFiiKbx\nJsyuWz9WV1djz549AICMjAwcOnRoSIK1dOlSpKWlAQDCw8NhsVjAGKMhnoQQQkbk0wRLp9NhwYIF\nzt+joqKg0+kgk8mg0+mgUCic9ykUCjQ0NPiyOACAc1fu4OCH//L664qFPIRJBIhVyhAmFSBCKoRc\nJkS4VIjIMCEiw0SICBNCIRdTAkUIAYD+RIePGdGyMR/b02uHpdtxQsfcbYO12w5rz72fLT02WLpt\n/Y+xw2Lthan/BNBdcy9a2i3o88K+8gkaOV7/96WTfp1AqaurQ15eHgwGA/Lz82G1Wp1DAqOjo9HW\n1jbk8VwuFxKJI0EuLy/HmjVrKLkihBAyKr/OrHQ9czje+wab7G7Lj2TI8UjG3Em9xlTnix2pgw3F\naGwUo7FRjKaXhIQEbN26FdnZ2WhoaEBubi5sNpvz/tH6oVOnTuGDDz5AWVnZuP4W1Y2xUYzGRjEa\nG8VobBQj//PpDGq1Wg2dTuf8vbW1FSqVynnf4DOFLS0tUKvVviwOIYSQEKbRaJCdnQ0AiI+Ph1Kp\nRFdXF3p6egCM3A+dOXMGBw4cwMGDBxEWFubXMhNCCJl+fJpgpaeno7KyEgBw9epVaDQaSKVSAEBs\nbCxMJhOamppgs9nwj3/8AytXrvRlcQghhISwEydOoLS0FACg1+uh1+uxceNGVFRUAAAqKyuxatWq\nIc8xGo3Yu3cv9u/fD7mczgITQggZG4eNd2yeh0pKSlBdXQ0ej4fdu3fj2rVrkMvlWL9+PS5evIjf\n/e53AIANGzZgy5YtviwKIYSQEGYymVBQUACDwQDGGPLz85GamorCwkL09PRAq9WiqKgIPB4PBQUF\nKCoqwrFjx1BaWorExETn4hbFxcWIiYkJ9NshhBAyRfk8wSKEEEIIIYSQUEG7WBJCCCGEEEKIl1CC\nRQghhBBCCCFeQgkWIYQQQgghhHiJX/fBGk11dTW2bduGOXPmgDGGlJQUPPPMM3j55ZfBGINKpUJx\ncbFzQ8gBxcXFuHTpEux2O5577jk88MADAXoHvudpjACgu7sbDz30EPLz8/HTn/40AKX3D09jdPz4\ncZSVlYHP5+PFF1/EmjVrAvQOfM+TGJnNZhQWFsJgMKC3txf5+flBveqnuxi9+uqr+Otf/4q9e/ei\npqbGufnsYEVFRaitrQWHw8HOnTuxcOHCAJTePzyNUbC32aFUB0bi+hkvXLjQbfty/Phx/O1vfwOP\nx8Njjz2GTZs2wWazYceOHWhqagKPx0NRURHi4uIC/Za8bnCf/OMf/5ji44Zrv5ySkkJx6ueuT05O\nTqb49Lt+/TpeeOEFbNmyBU899RSam5snHZvr16/jjTfeAJfLRUpKCl5//fXRC8GmiAsXLrAXX3xx\nyG07duxglZWVjDHGSkpK2N///vch958/f549++yzjDHGOjo62Nq1a/1T2ADxJEYDSkpK2KZNm9jR\no0d9Xs5A8iRGHR0dLDMzk5nNZtbW1sZee+01v5U3EDyJ0TvvvMNKSkoYY4y1tLSwDRs2+KewAeIu\nRkePHmV/+MMfWEZGBjObzcOeU11dzX7+858zxhi7ceMGy8nJ8UtZA8WTGAV7mx1qdcAdd5/xjh07\nWEVFBWPsXvtiNptZVlYWMxqNzGq1soceeogZDAZ29OhRtmfPHsYYY2fPnmUvvfRSwN6LLw3uk921\nv6EeH3f9MsXpHnd9Mn3PHMxmM9uyZQt7/fXX2TvvvMMYc3+MM9HYbN68mV25coUxxtj27dvZZ599\nNmo5ptQQQeayoGF1dTUyMjIAABkZGTh37tyQ+5cuXYq33noLABAeHg6LxTLsNYLNRGMEADdv3sR3\n330X1FdlBptojM6dO4f09HRIJBIolUrs2bPHb2UNlInGSKFQoKOjAwBgMBigUCj8U9AAco1RVlYW\nXnjhhREfX1VVhfXr1wMAkpKS0NXVBZPJ5NMyBtpEYxTsbXYo1gFXrp+x2WxGTU0N1q1bB+Be+1Jb\nW4u0tDTIZDKIRCLcd999+Pzzz4fEcMWKFbh06VLA3ouvDO6TGWOoqakZ1v6GcnwA9/2yu34qVOPk\nrk+m75mDSCTCn//8ZyiVSudtk6k7ly9fRm9vLxobGzF//nwAwLp169webw82pRKsuro65OXl4amn\nnsK5c+dgtVqdw5Sio6PR1tY25PFcLtc5BKW8vBxr1qwBh8Pxe7n9aaIxAhzDNXbs2OHvogbMRGN0\n+/ZtWCwWPP/883j66adRVVUViGL71URjlJ2djebmZmRmZiI3Nzck6pNrjNwNdxtMp9MNSTyjoqKg\n0+l8XcyAmmiMgr3NDsU64GrwZ/zee+9h7dq1sFgsQ9qX1tZW6PX6IbFSKBRoa2sbEkMOhwMulwub\nzeb/N+JDrn0yxWc4d/2yaz8VynFy7ZMLCwupHvXjcrkQCoVDbptMbDgcDnQ6HSIjI4c9djRTZg5W\nQkICtm7diuzsbDQ0NCA3N3fIhz3aWc5Tp07hgw8+QFlZmT+KGjCexOjYsWNYunQptFrtiI8JJp7E\niDGGzs5OvP3222hsbERubi5Onz7tz2L7lScxOn78OGJiYnDgwAFcv34dr732GsrLy/1ZbL9yF6OP\nP/4YfP74m8xQ/K6NN0ah0mYHex0YzalTp/D++++jrKwMmZmZzttHislIt/f19fmkfIHi2ie7CvX4\nDBjol//4xz/i9u3byM3NHRKDUI/T4D7566+/xq5du4bcH+rxGc1EY8P6N5ifaHs+Za5gaTQaZGdn\nAwDi4+OhVCrR1dWFnp4eAEBLSwvUavWw5505cwYHDhzAwYMHERYW5tcy+5snMfr0009RUVGBnJwc\nlJeX409/+lNQX6HxJEZKpRKLFy8Gh8NBfHw8ZDIZ2tvb/V52f/EkRpcuXcKqVasAAKmpqWhubg7q\ng0d3MWppaQGAEa+4qNXqIVcrWltboVKpfF/YAPEkRkBwt9mhVgdG4voZy2SyIe2LRqOBWq0ecgZ4\n8O0DMRw48TORExtT3eA++b333sPbb78NqVRK8XEx0C9zuVxnv0z16J7BfXJKSgpaWlogkUgoPiOY\nTN1h/QtjdHZ2Dnmsu5xksCmTYJ04cQKlpaUAAL1eD71ej40bN6KiogIAUFlZ6axMA4xGI/buQQde\nKQAAArBJREFU3Yv9+/dDLpf7vcz+5kmM9u3bh/Lycrz77rt47LHHkJeXh+XLl/u97P7iSYzS09Nx\n4cIFMMbQ0dEBs9kc1HOMPIlRQkICvvjiCwCOoRtSqTSohna5co1Re3s7NBoNAMfZLHfJZXp6Oior\nKwEAV69ehUajgVQq9V+h/cyTGAV7mx1qdcAdd5/x8uXLnXEZaF/S0tJw5coVGI1GmEwmXL58GT/6\n0Y+Qnp7ubIs++eQT3H///QF7L74wuE/etGkT8vPzsXz58mHtb6jGZ4C7fpnidI+7PnnFihUUnxFM\ntg3i8XiYPXu2c67ayZMnhx0nueKwKXIa2mQyoaCgAAaDAYwx5OfnIzU1FYWFhejp6YFWq0VRURF4\nPB4KCgpQVFSEY8eOobS0FImJic5LeMXFxYiJiQn02/EJT2I0eBxqaWkp4uLignqZ9onEaPv27Xjz\nzTchFApx5MgRlJeXg8PhIC8vD2vXrg30W/EZT2Jks9mwc+dO6PV62O12vPTSS1i2bFmg34rPuMYo\nLy8PFy9exOnTp3Hr1i0kJCRgyZIleOONN4bUo5KSElRXV4PH42H37t1ISUkJ9FvxGU9iFAptdijV\nAXeOHDky7DP+7W9/i127dg1rX06ePImDBw+Cy+Vi8+bNePDBB9HX14ddu3ahvr4eIpEIb775pjNx\nDzYDffLKlSvxyiuvUHxcuPbLCxYsoDj1M5vNw/rkWbNmue3HQy0+tbW1ePXVV9He3g4ej4eIiAiU\nlZVhx44dk4pNXV0ddu/eDcYYFi1ahMLCwlHLMWUSLEIIIYQQQgiZ7qbMEEFCCCGEEEIIme4owSKE\nEEIIIYQQL6EEixBCCCGEEEK8hBIsQgghhBBCCPESSrAIIYQQQgghxEsowSKEEEIIIYQQL6EEixBC\nCCGEEEK85P8BAh3ELr/jR/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc520485c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traceplot(june_noconf_trace[n_burn:], vars=['beta'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity for lab- versus clinical-confirmation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymc3 import df_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JmDJlCrZu3YrExETI5XJERUUhMjLSGqiIiIjoxlFmqELG2QJUGE3Qeroi+CZPm/NtGmMz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\nB1Q9z9d5vr/3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5212657f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lam_june = df_summary(june_trace[n_burn:], vars='λ')\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharey=True)\n",
    "\n",
    "lam_june['mean'].plot(ax=axes[0], alpha=0.4)\n",
    "axes[0].set_ylabel('Epidemic intensity')\n",
    "axes[0].set_xlabel('time (2-week periods)')\n",
    "axes[0].set_title('Lab confirmation')\n",
    "\n",
    "lam_june_noconf = df_summary(june_noconf_trace[n_burn:], vars='λ')\n",
    "\n",
    "lam_june_noconf['mean'].plot(ax=axes[1], alpha=0.4)\n",
    "axes[1].set_ylabel('Epidemic intensity')\n",
    "axes[1].set_xlabel('time (2-week periods)')\n",
    "axes[1].set_title('Clinical confirmation')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6jMfj8XOu5ejRk6lE6Jfs7JG0tx+77Me53ELIEUIGCCNHCBnAHINJCBngyuQ420lDSh9p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\nFAhLXZKkQFjqkiQFwlKXJCkQ/w+FNGysOH34kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51fbbc0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(july_trace.get_values('S')[n_burn:,-1,:]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(june_trace.get_values('S')[n_burn:, -1, :], \n",
    "                          columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(june_trace.get_values('S')[n_burn:, -1, :], \n",
    "                          columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_june_noconf = pd.DataFrame(june_noconf_trace.get_values('S')[n_burn:, -1, :], \n",
    "                                 columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_july = pd.DataFrame(july_trace.get_values('S')[n_burn:, -1, :],  \n",
    "                          columns=age_group.categories).unstack().reset_index()\n",
    "S_age_july.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_july_noconf = pd.DataFrame(june_noconf_trace.get_values('S')[n_burn:, -1, :],  \n",
    "                                 columns=age_group.categories).unstack().reset_index()\n",
    "S_age_july_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numbers of suscepibles in each age group, under lab vs clinical confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
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J6LTe1NRUTZ06VTfffLN8Pp927dqlO+64o7trAwAAAIBexzRNNTc3B3XMiIiI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\nK9M0FR0drRUrVig0NFSFhYX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o27dvW1W2SUlJKioqSiml1G+//aYdtzl1ayxbpaRuTWVr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\nWxEHuyeXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51cda5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.factorplot(\"Age\", \"S\", \"Confirmation\", S_age_june, kind=\"box\",\n",
    "                   palette=\"hls\", size=6, aspect=2, linewidth=0.3, fliersize=0, \n",
    "                  order=age_group.categories)\n",
    "g.despine(offset=10, trim=True)\n",
    "g.set_axis_labels(\"Age Group\", \"Susceptibles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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crKlTp+qOO+6Qx+PR7t27de+993Z1bQAAAADQ43i9XjU1NQV0zLCwsEu+5/TY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\ntUqlpaVqa2vTT3/6U33rW9/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79OlTadk7d+4oLy8vUx1KrVOVbP38/FRmZmaV+ps/f77a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\nCCGEEKLG/Rd4Jj4fjCBoDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc51fb8cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.factorplot(\"Age\", \"S\", \"Confirmation\", S_age_july, kind=\"box\",\n",
    "                   palette=\"hls\", size=6, aspect=2, linewidth=0.3, fliersize=0, \n",
    "                  order=age_group.categories)\n",
    "g.despine(offset=10, trim=True)\n",
    "g.set_axis_labels(\"Age Group\", \"Susceptibles\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vaccination coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_age_groups = len(age_groups)\n",
    "\n",
    "coverage_5 = np.r_[[0.95], [0]*(n_age_groups - 1)]\n",
    "coverage_15 = np.r_[[0.95]*3, [0]*(n_age_groups - 3)]\n",
    "coverage_30 = np.r_[[0.95]*6, [0]*(n_age_groups - 6)]\n",
    "coverage_adult = np.r_[[0.95, 0, 0, 0, 0.95, 0.95] + [0]*(n_age_groups - 6)]\n",
    "\n",
    "coverage_dict = {'pct_5': coverage_5,\n",
    "                'pct_15': coverage_15,\n",
    "                'pct_30': coverage_30,\n",
    "                'pct_adult': coverage_adult}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def calc_coverage(trace, p):\n",
    "    n = trace.get_values('S')[:,-1,:].copy().astype(int)\n",
    "    n[n<0] = 0\n",
    "    vacc = np.random.binomial(n, p).sum(1)\n",
    "    pct = vacc/trace.get_values('S')[:,-1,:].sum(1)\n",
    "    \n",
    "    return pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = ['pct_5', 'pct_15', 'pct_30', 'pct_adult']\n",
    "\n",
    "june_coverage = pd.DataFrame({v: calc_coverage(june_trace[n_burn:], \n",
    "                                               coverage_dict[v]) for v in labels})\n",
    "june_coverage['Month'] = 'June'\n",
    "june_coverage['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_noconf_coverage = pd.DataFrame({v: calc_coverage(june_noconf_trace[n_burn:], \n",
    "                                               coverage_dict[v]) for v in labels})\n",
    "june_noconf_coverage['Month'] = 'June'\n",
    "june_noconf_coverage['Confirmation'] = 'Clinical'\n",
    "\n",
    "july_coverage = pd.DataFrame({v: calc_coverage(july_trace[n_burn:], \n",
    "                                               coverage_dict[v]) for v in labels})\n",
    "july_coverage['Month'] = 'July'\n",
    "july_coverage['Confirmation'] = 'Lab'\n",
    "\n",
    "july_noconf_coverage = pd.DataFrame({v: calc_coverage(july_noconf_trace[n_burn:], \n",
    "                                               coverage_dict[v]) for v in labels})\n",
    "july_noconf_coverage['Month'] = 'July'\n",
    "july_noconf_coverage['Confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], \n",
    "                     ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc520d09e10>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAICFkAwAAABYCMkAAACAhZAMAAAAWAjJAAAAgIWQDAAAAFgIyQAAAICFkAwAAABYCMkA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\nTAjJAAAAgMn/AfURS0KR/oseAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc520d09cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Mapping spatial effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "import geopandas as gpd\n",
    "\n",
    "lllat=-24\n",
    "urlat=-23.3\n",
    "lllon=-47\n",
    "urlon=-46.3\n",
    "\n",
    "SP_base = Basemap(ax=None, lon_0=(urlon + lllon) / 2, lat_0=(urlat + lllat) / 2,\n",
    "        llcrnrlat=lllat, urcrnrlat=urlat, llcrnrlon=lllon, urcrnrlon=urlon, \n",
    "                  resolution='i',\n",
    "                 epsg='4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist = gpd.GeoDataFrame.from_file('Sao Paulo/Brazil_full/BRA_adm3.shp').to_crs({'proj': 'longlat', \n",
    "                                                                               'ellps': 'WGS84', \n",
    "                                                                               'datum': 'WGS84'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist['DIST_NAME'] = [trans.trans(_).upper() for _ in SP_dist.NAME_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "λ_june = pd.Series(model_june.λ_t.stats()['mean'], index=sp_districts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "λ_june"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist_merged = SP_dist.merge(pd.DataFrame(λ_june, columns=['λ']), left_on='DIST_NAME', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_onset_conf = measles_data[CONFIRMED].groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_onset_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_rates = measles_onset_conf/sp_pop.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SP_dist_conf = SP_dist.merge(pd.DataFrame(_rates, columns=['rate']), left_on='DIST_NAME', right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated expected value for infecteds, by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import cm\n",
    "\n",
    "map_fig = plt.figure(figsize=(16,12))\n",
    "map_ax = plt.gca()\n",
    "SP_base.drawcoastlines()\n",
    "SP_base.drawrivers()\n",
    "\n",
    "SP_dist_merged.plot(column='λ', colormap=cm.Reds, axes=map_ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observed confirmed cases, by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map_fig = plt.figure(figsize=(16,12))\n",
    "map_ax = plt.gca()\n",
    "SP_base.drawcoastlines()\n",
    "SP_base.drawrivers()\n",
    "\n",
    "SP_dist_conf.plot(column='rate', colormap=cm.Reds, axes=map_ax)"
   ]
  }
 ],
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