fonnesbeck/Simple Measles Model (PyMC 2).ipynb

## Simple Measles Model (PyMC 2).ipynb
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   "source": [
    "# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "outputs": [
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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 pdb\n",
    "\n",
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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'}})"
   ]
  },
  {
   "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": {
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   "outputs": [
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       "<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": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104b54240>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h4qJLMWpxVkyZad0+JIUtDs3hQLPHgej9h3h/Q76ZaFhfA5iANcAdQApwN9Ef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\n0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1BlBpHcmL9yW7BjOwhVnVcv4B3ADwijE74K/HZ9ey/wOuDM+ufvAafU/24H2k90359j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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104b656a0>"
      ]
     },
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "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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RR1VYGLpF8K233qoVK1aob9++OuaYY7Rx40adffbZmjhxojZs2KC77767xutZ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106045c50>"
      ]
     },
     "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": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR\n",
       "1980    0.57\n",
       "1981    0.73\n",
       "1982    0.66\n",
       "1983    0.68\n",
       "1984    0.73\n",
       "1985    0.67\n",
       "1986    0.67\n",
       "1987    0.64\n",
       "1988    0.62\n",
       "1989    0.60\n",
       "1990    0.78\n",
       "1991    0.85\n",
       "1992    0.91\n",
       "1993    0.85\n",
       "1994    0.77\n",
       "1995    0.87\n",
       "1996    0.80\n",
       "1997    0.99\n",
       "Name: VAX, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data.VAX[:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Mean age of infection for those born prior to vaccination coverage, assuming R0=16\n",
    "A = 4.37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 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.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  0.487,  0.343,  0.406,  0.388,  0.343,\n",
       "        0.397,  0.397,  0.424,  0.442,  0.46 ,  0.298,  0.235,  0.181,\n",
       "        0.235,  0.307,  0.217])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 - vax_97*0.9)[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 64.84 ,  63.84 ,  62.84 ,  61.84 ,  60.84 ,  59.84 ,  58.84 ,\n",
       "        57.84 ,  56.84 ,  55.84 ,  54.84 ,  53.84 ,  52.84 ,  51.84 ,\n",
       "        50.84 ,  49.84 ,  48.84 ,  47.84 ,  46.84 ,  45.84 ,  44.84 ,\n",
       "        43.84 ,  42.84 ,  41.84 ,  40.84 ,  39.84 ,  38.84 ,  37.84 ,\n",
       "        36.84 ,  35.84 ,  34.84 ,  33.84 ,  32.84 ,  31.84 ,  30.84 ,\n",
       "        29.84 ,  28.84 ,  27.84 ,  26.84 ,  25.84 ,  24.84 ,  23.84 ,\n",
       "        22.84 ,  21.84 ,  20.84 ,  19.84 ,  18.84 ,  17.84 ,  16.84 ,\n",
       "        15.84 ,  14.84 ,  13.84 ,  12.84 ,  11.84 ,  10.84 ,   9.84 ,\n",
       "         8.84 ,   7.84 ,   6.84 ,   5.84 ,   5.353,   5.01 ,   4.604,\n",
       "         4.216,   3.873,   3.476,   3.079,   2.655,   2.213,   1.753,\n",
       "         1.455,   1.22 ,   1.039,   0.804,   0.497,   0.28 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tril(FOI_mat).sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natural_susc = np.exp((-1/A) * np.tril(FOI_mat).sum(0))[::-1]\n",
    "vacc_susc = (1 - vax_97*0.9)[::-1]\n",
    "vacc_susc[0] = 0.5\n",
    "vacc_susc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\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",
    "sia_susc[by_mask] *= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "downsample = lambda x: np.array([x[s].mean() for s in age_slices])"
   ]
  },
  {
   "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": 21,
   "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.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": 22,
   "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
    "y = (lab_subset.CONCLUSION=='CONFIRMED').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oMvBcSqkBICLWRsRkSqN5meLAJGB1926KJEmSJPVvnZmFcizwekRcVn5dD4wD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\ndR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108379080>"
      ]
     },
     "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), figsize=(15,5), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39982, 16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22097"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of lab-confirmed cases older than 20 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60257048468117846"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(measles_data[CONFIRMED].YEAR_AGE>20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5, 10, 15, 20, 25, 30, 35, 40, 100]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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])\n",
    "age_groups = age_group.categories\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excludes = ['BOM RETIRO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0 a 4 anos      844130\n",
       "5 a 9 anos      830880\n",
       "10 a 14 anos    858750\n",
       "15 a 19 anos    904972\n",
       "20 a 24 anos    945244\n",
       "25 a 29 anos    902086\n",
       "30 a 34 anos    835888\n",
       "35 a 39 anos    764605\n",
       "40 a 44 anos    662946\n",
       "45 a 49 anos    538872\n",
       "50 a 54 anos    437744\n",
       "55 a 59 anos    332195\n",
       "60 a 64 anos    282850\n",
       "65 a 69 anos    218202\n",
       "70 a 74 anos    164842\n",
       "75 anos e +     203482\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "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": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_counts_2w = lab_subset.groupby(\n",
    "    ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).resample('2W', how='sum')\n",
    "\n",
    "# All confirmed cases, by district\n",
    "confirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\n",
    "confirmed_counts = confirmed_data.groupby(\n",
    "    ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).sum()\n",
    "\n",
    "all_confirmed_cases = confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique()).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Ensure the age groups are ordered\n",
    "I_obs = sp_counts_2w.reindex_axis(measles_data['AGE_GROUP'].unique(), \n",
    "                            axis=1).fillna(0).values.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert I_obs.shape == (28, len(age_groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior distribution on susceptible proportion:\n",
    "\n",
    "$$p_s \\sim \\text{Beta}(2, 100)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.54700000e+03,   3.56100000e+03,   1.77800000e+03,\n",
       "          7.17000000e+02,   2.53000000e+02,   1.05000000e+02,\n",
       "          2.40000000e+01,   1.00000000e+01,   4.00000000e+00,\n",
       "          1.00000000e+00]),\n",
       " array([  1.19543150e-04,   1.23316291e-02,   2.45437151e-02,\n",
       "          3.67558011e-02,   4.89678870e-02,   6.11799730e-02,\n",
       "          7.33920590e-02,   8.56041450e-02,   9.78162309e-02,\n",
       "          1.10028317e-01,   1.22240403e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEBCAYAAACXArmGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA/t6s4qyIgE6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106725b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc import rbeta\n",
    "plt.hist(rbeta(2, 100, 10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1,    3,    0,    1,    0,    0,    0,    0,    1],\n",
       "       [   4,   13,    7,   18,    1,    2,    0,    1,    4],\n",
       "       [   3,   12,    2,   14,    0,    1,    1,    2,    5],\n",
       "       [   4,   10,    2,   17,    0,    2,    2,    2,    2],\n",
       "       [   6,   15,    7,   19,    1,    3,    1,    7,    6],\n",
       "       [  19,   27,   20,   34,    0,    7,    2,   13,    8],\n",
       "       [   9,   27,    6,   26,    1,    1,    1,    6,    8],\n",
       "       [  13,   27,   13,   20,    1,    4,    2,    5,    2],\n",
       "       [  28,   32,   16,   21,    2,    6,    1,    9,    9],\n",
       "       [  42,   39,   46,   31,    6,   17,    2,   13,   18],\n",
       "       [  93,   69,   72,   40,    4,   18,    6,   19,   26],\n",
       "       [ 157,   95,  153,   64,   12,   47,    5,   31,   42],\n",
       "       [ 359,  183,  315,  169,   26,   95,   18,   76,   68],\n",
       "       [ 807,  363,  622,  282,   65,  234,   34,  162,  136],\n",
       "       [1168,  660, 1035,  388,   87,  398,   63,  257,  166],\n",
       "       [1442,  913, 1193,  536,  137,  430,   48,  318,  292],\n",
       "       [1350, 1051, 1255,  643,  116,  476,   68,  366,  339],\n",
       "       [1314,  933, 1261,  525,  160,  474,   91,  448,  339],\n",
       "       [1218,  773, 1061,  444,  146,  458,   75,  424,  320],\n",
       "       [ 712,  485,  629,  292,   80,  262,   67,  267,  214],\n",
       "       [ 368,  295,  382,  187,   47,  163,   26,  122,   92],\n",
       "       [ 181,  162,  192,  130,   27,   97,   10,   43,   65],\n",
       "       [ 122,  151,   88,  102,   14,   43,   10,   27,   36],\n",
       "       [  72,   95,   63,   64,    6,   36,    2,   15,   18],\n",
       "       [  32,   46,   39,   52,    7,   15,    2,   20,   14],\n",
       "       [  20,   42,   30,   42,    2,    9,    2,    8,   17],\n",
       "       [   7,   23,    5,   15,    1,    4,    3,    3,    7],\n",
       "       [   1,    1,    2,    1,    0,    1,    0,    0,    0]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obs_date = '1997-12-01' #'1997-06-15'\n",
    "obs_index = sp_counts_2w.index <= obs_date\n",
    "I_obs_t = I_obs[obs_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24959899,  0.16916038,  0.22193589,  0.1069447 ,  0.02469168,\n",
       "        0.08609219,  0.01406821,  0.06923664,  0.05827132])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(I_obs_t, (0)) / float(I_obs_t.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymc import rgamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   79.,   883.,  2575.,  3032.,  2071.,   960.,   310.,    72.,\n",
       "           14.,     4.]),\n",
       " array([  4.85288825,   7.95871261,  11.06453697,  14.17036133,\n",
       "         17.27618569,  20.38201005,  23.48783441,  26.59365877,\n",
       "         29.69948313,  32.80530749,  35.91113185]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vfQn/EnBk6P5Mj36yjnIL+GDaixhh+CVpn/f0Xi+de7OvST4LPFZV3+1GvdzPEesc1pv9\n7LzD4N8YWqSHeznk7jqH9WkvHwH+LMm/A59i0PFLjLGnvQh/Vd1JMgfc/RiHU1Nczj0l+QnwEIOX\nf09PeTkfkeR54CvAwSSfrKqn+rin91jnKwxeXvdpX38KvJ1kAXizqp7p434yep292s9uPQ8wiOd3\nqurDPu7lynV2s979P19VrwKvAnS/afSJqnpznD31IxskqTF9+a0eSdI2MfyS1BjDL0mNMfyS1BjD\nL0mNMfyS1BjDL0mNMfyS1Jj/A20D4hYOmW97AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088c5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rgamma(16,1,size=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1772247118855148"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "75./age.mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc import MCMC, Matplot, AdaptiveMetropolis, Slicer, MAP\n",
    "from pymc import (Uniform, DiscreteUniform, Beta, Binomial, Normal, CompletedDirichlet,\n",
    "                  Poisson, NegativeBinomial, negative_binomial_like, poisson_like,\n",
    "                  Lognormal, Exponential, binomial_like,\n",
    "                  TruncatedNormal, Binomial, Gamma, HalfCauchy, normal_like,\n",
    "                  MvNormalCov, Bernoulli, Uninformative, \n",
    "                  Multinomial, rmultinomial, rbinomial,\n",
    "                  Dirichlet, multinomial_like)\n",
    "from pymc import (Lambda, observed, invlogit, deterministic, potential, stochastic,)\n",
    "\n",
    "def measles_model(obs_date, confirmation=True, spatial_weighting=False, all_traces=True):\n",
    "    \n",
    "    n_periods, n_age_groups = I_obs.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",
    "        mu = Normal(\"mu\", mu=0, tau=0.0001, value=[0]*len(age_classes))\n",
    "        sig = HalfCauchy('sig', 0, 25, value=1)\n",
    "        var = sig**2\n",
    "        cor = Uniform('cor', -1, 1, value=0)\n",
    "\n",
    "        # Build variance-covariance matrix with first-order correlation \n",
    "        # among age classes\n",
    "        @deterministic\n",
    "        def Sigma(var=var, cor=cor):\n",
    "            I = np.eye(len(age_classes))*var\n",
    "            E = np.diag(np.ones(len(age_classes)-1), k=-1)*var*cor\n",
    "            return I + E + E.T\n",
    "\n",
    "        # Age-specific probabilities of confirmation as multivariate normal \n",
    "        # random variables\n",
    "        beta_age = MvNormalCov(\"beta_age\", mu=mu, C=Sigma, \n",
    "                        value=[1]*len(age_classes))\n",
    "        p_age = Lambda('p_age', lambda t=beta_age: invlogit(t))\n",
    "\n",
    "        @deterministic(trace=False)\n",
    "        def p_confirm(beta=beta_age):\n",
    "            return invlogit(beta[age_index])\n",
    "\n",
    "\n",
    "        # Confirmation likelihood\n",
    "        lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, value=y, \n",
    "                                observed=True)\n",
    "\n",
    "\n",
    "    '''\n",
    "    Truncate data at observation period\n",
    "    '''\n",
    "    obs_index = sp_counts_2w.index <= obs_date\n",
    "    I_obs_t = I_obs[obs_index]  \n",
    "                        \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",
    "        @stochastic(trace=all_traces, dtype=int)\n",
    "        def I(value=(I_obs_t).astype(int), n=I_obs_t, p=p_age):\n",
    "            # Binomial confirmation process\n",
    "            return np.sum([binomial_like(x, x.sum(), p) for x in value])\n",
    "\n",
    "    else:\n",
    "        \n",
    "        I = I_obs_t\n",
    "        \n",
    "    assert I.shape == (t_obs +1, n_age_groups)\n",
    "    \n",
    "    # Transmission parameter\n",
    "    beta = HalfCauchy('beta', 0, 25, value=8) \n",
    "\n",
    "#     # Downsample annual series to observed age groups\n",
    "#     downsample = lambda x: np.array([x[s].mean() for s in age_slices])\n",
    "    \n",
    "#     A = Lambda('A', lambda beta=beta: 75./(beta - 1))\n",
    "#     lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])\n",
    "#     natural_susc = Lambda('natural_susc', lambda A=A: np.exp((-1/A) * lt_sum))\n",
    "    \n",
    "\n",
    "    natural_susc = Beta('natural_susc', 1, 1, value=[0.02]*n_age_groups)\n",
    "    @deterministic\n",
    "    def p_susceptible(natural_susc=natural_susc): \n",
    "        return downsample(sia_susc) * downsample(vacc_susc) * natural_susc\n",
    "    \n",
    "    # Estimated total initial susceptibles\n",
    "    S_0 = Binomial('S_0', n=N_age.astype(int), p=p_susceptible)\n",
    "\n",
    "    S = Lambda('S', lambda I=I, S_0=S_0: S_0 - I.cumsum(0))\n",
    "    \n",
    "    # Check shape\n",
    "    assert S.value.shape == (t_obs+1., n_age_groups)\n",
    "\n",
    "    S_t = Lambda('S_t', lambda S=S: S[-1])      \n",
    "        \n",
    "    # Force of infection\n",
    "    @deterministic\n",
    "    def lam(beta=beta, I=I, S=S): \n",
    "        return (beta * I.sum(1) * (S / N_age.values).T).T\n",
    "    \n",
    "    # Check shape\n",
    "    assert lam.value.shape == (t_obs+1, n_age_groups)\n",
    "    \n",
    "    # Poisson likelihood for observed cases\n",
    "    @potential\n",
    "    def new_cases(I=I, lam=lam):\n",
    "        return negative_binomial_like(I[1:], lam[:-1], I[:-1]+1)\n",
    "    \n",
    "\n",
    "    return locals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iterations = 500000\n",
    "burn = 400000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 500000 of 500000 complete in 981.2 sec"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/GitHub/pymc/pymc/StepMethods.py:1272: UserWarning: \n",
      "Covariance was not positive definite and proposal_sd cannot be computed by \n",
      "Cholesky decomposition. The next jumps will be based on the last \n",
      "valid covariance matrix. This situation may have arisen because no \n",
      "jumps were accepted during the last `interval`. One solution is to \n",
      "increase the interval, or specify an initial covariance matrix with \n",
      "a smaller variance. For this simulation, each time a similar error \n",
      "occurs, proposal_sd will be reduced by a factor .9 to reduce the \n",
      "jumps and increase the likelihood of accepted jumps.\n",
      "  warnings.warn(adjustmentwarning)\n"
     ]
    }
   ],
   "source": [
    "M = MCMC(measles_model('1997-06-15', confirmation=True))\n",
    "M.use_step_method(AdaptiveMetropolis, M.natural_susc)\n",
    "M.sample(iterations, burn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 500000 of 500000 complete in 968.8 sec"
     ]
    }
   ],
   "source": [
    "M.sample(iterations, burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c06bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.p_susceptible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting beta\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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ARz/4mpUZvBebyndP557y4Ieen7NUeXbNIXHj6p+nmb6WplHeT3jU9+InJ6YFg5ge9NvG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\nB+OH4xl6WvOeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088b6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M.beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m6fFpPTMzS47DyczMkuNwMjOz5DiczMwsOT0ZTpJOlvS1/DG4n7YjkqqSNks6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\nw8nMzJLz/wGopg42JQuLXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10735c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.p_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}