fonnesbeck/Simple Measles Model (Matrix beta).ipynb

## Simple Measles Model (Matrix beta).ipynb
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   "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": 2,
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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": 4,
   "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": 5,
   "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": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "_names = sp_pop.index.values\n",
    "_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\n",
    "sp_pop.set_index(_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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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": 8,
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10848e0b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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    {
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J0PHAjxMI8w3j4b4PA+AwO0iamoS91K7Nk29EN8eJbpN3332XLVu2kJycTEBA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voBRj+AgwdKKe5+/27uW3wfN0y5gXNHn0uWO2tbeUekg98u+C0PL3mYe0+5l5kVM5kz5w/o+u9Y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\n0lLvTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10848e630>"
      ]
     },
     "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": 10,
   "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": 11,
   "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": 11,
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    },
    {
     "data": {
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9fgu5Pfvss8rIyAio9/DDD6tz585q2rSpOnTooLFjx+q7776rtN/09HTFxsYq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\nIYQQQgghhBhg1KgTQgghhBBCiAHm/wFacbdYlto8ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106fb3748>"
      ]
     },
     "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": 13,
   "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": 13,
     "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": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data.VAX[:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 - vax_97*0.9)[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tril(FOI_mat).sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "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": 20,
   "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": "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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qaGPIJOD6lNKalNJiYBEwMSJqgQEppXlZv1nA5DLzlyRJkqQNWpeuqYuIMcCu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108488668>"
      ]
     },
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    }
   ],
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    }
   ],
   "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([  2.89600000e+03,   3.47500000e+03,   2.02900000e+03,\n",
       "          9.67000000e+02,   3.92000000e+02,   1.57000000e+02,\n",
       "          5.00000000e+01,   2.30000000e+01,   1.00000000e+01,\n",
       "          1.00000000e+00]),\n",
       " array([ 0.00013217,  0.0106295 ,  0.02112683,  0.03162415,  0.04212148,\n",
       "         0.05261881,  0.06311613,  0.07361346,  0.08411078,  0.09460811,\n",
       "         0.10510544]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78GxVXQs8B+wASHIdcCewCbgVeCRJhjy+JGmEhg2FzLOP24Hd3fpu4I5u/Tbgiao6WVVH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\nagwFSVJjKEiSGkNBktT8Xy3/bKsGN1SIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1082a5eb8>"
      ]
     },
     "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([  1.03000000e+02,   1.42800000e+03,   3.69100000e+03,\n",
       "          2.99500000e+03,   1.34400000e+03,   3.63000000e+02,\n",
       "          6.50000000e+01,   9.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00]),\n",
       " array([  4.22868068,   8.07732095,  11.92596121,  15.77460147,\n",
       "         19.62324173,  23.471882  ,  27.32052226,  31.16916252,\n",
       "         35.01780278,  38.86644305,  42.71508331]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106f2dc50>"
      ]
     },
     "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": 86,
   "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_age_groups) \n",
    "    decay = Beta('decay', 1, 5, value=0.9)\n",
    "    \n",
    "    @deterministic\n",
    "    def B(b=beta, d=decay):\n",
    "        B = b*np.eye(n_age_groups)\n",
    "        for i in range(1, n_age_groups):\n",
    "            B += np.diag(np.ones(n_age_groups-i)*b[i:]*d**i, k=-i) \n",
    "            B += np.diag(np.ones(n_age_groups-i)*b[:-i]*d**i, k=i)\n",
    "        return B\n",
    "\n",
    "    # Downsample annual series to observed age groups\n",
    "    downsample = lambda x: np.array([x[s].mean() for s in age_slices])\n",
    "    \n",
    "    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",
    "    \n",
    "    @deterministic\n",
    "    def R(B=B, S=S): \n",
    "        return (S.dot(B) / N_age.values).T\n",
    "    \n",
    "    # Force of infection\n",
    "    @deterministic\n",
    "    def lam(B=B, I=I, S=S): \n",
    "        return (I.sum(1) * (S.dot(B) / 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 poisson_like(I[1:], lam[:-1])\n",
    "    \n",
    "\n",
    "    return locals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.  ,  1.8 ,  1.62,  1.46,  1.31,  1.18,  1.06,  0.96],\n",
       "       [ 2.7 ,  3.  ,  2.7 ,  2.43,  2.19,  1.97,  1.77,  1.59],\n",
       "       [ 3.24,  3.6 ,  4.  ,  3.6 ,  3.24,  2.92,  2.62,  2.36],\n",
       "       [ 3.65,  4.05,  4.5 ,  5.  ,  4.5 ,  4.05,  3.65,  3.28],\n",
       "       [ 3.94,  4.37,  4.86,  5.4 ,  6.  ,  5.4 ,  4.86,  4.37],\n",
       "       [ 4.13,  4.59,  5.1 ,  5.67,  6.3 ,  7.  ,  6.3 ,  5.67],\n",
       "       [ 4.25,  4.72,  5.25,  5.83,  6.48,  7.2 ,  8.  ,  7.2 ],\n",
       "       [ 4.3 ,  4.78,  5.31,  5.9 ,  6.56,  7.29,  8.1 ,  9.  ]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_age_groups = 8\n",
    "beta = np.arange(2, n_age_groups+2)\n",
    "B = beta*np.eye(n_age_groups)\n",
    "d = 0.9\n",
    "for i in range(1, n_age_groups):\n",
    "    B += np.diag(np.ones(n_age_groups-i)*beta[i:]*d**i, k=-i) \n",
    "    B += np.diag(np.ones(n_age_groups-i)*beta[:-i]*d**i, k=i)\n",
    "B.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S = np.ones(n_age_groups)*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11390.6558,  18351.093 ,  25582.36  ,  32670.5   ,  39204.6   ,\n",
       "        44769.13  ,  48936.248 ,  51257.9511])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.dot(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iterations = 100000\n",
    "burn = 90000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 373.1 sec"
     ]
    }
   ],
   "source": [
    "M = MCMC(measles_model('1997-06-15', confirmation=True))\n",
    "M.sample(iterations, burn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 373.1 sec"
     ]
    }
   ],
   "source": [
    "M.sample(iterations, burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chain = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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68QBOAIZKxQfUvY8JkMTq1c91u/X2noTU9EfNERHj0tEWkKRVwHeBAyT9TNKfFkUXAe+Q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\nUEREVCIJKCIiKpEEFBERlUgCioiISvx/f0uIluqQCoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1086166a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.p_susceptible, chain=chain, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tgesj4vdqjSVpShrGAzgT6MtUzyx5b2bWcJLo6Xl12K2z82zk7s6wNbQHJGkN8D1gpqSf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\nLyGAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e64780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.natural_susc, chain=chain, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d7NNYk0R3dzfd3d0Jn4iY1NTuXVaS5gBrbc9v++TS6cBG22vbraNU147AjcARtmsNxZmg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103ce6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.beta, chain=chain, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transmission decay parameter (for modeling decay in beta to other age groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting decay\n"
     ]
    },
    {
     "data": {
      "image/png": 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yswnALGAN8DP3prM+B7gL2BCY5O5Z5lwo3LPPPsunn35a8P6DBg1ik002ibFE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\nCyKa/IEgbNAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108cd67f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M.decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirmation probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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71lA8wwauERHTRtNhYycBNB+4FXhs9LtAVZJ0IDDX9i1jFCeAIiKq0f0Ammb6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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c7b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M.p_age, chain=chain, custom_labels=age_groups)"
   ]
  }
 ],
 "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
}