Non-spatial Measles Model.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": 1,
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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",
    "from pymc import MCMC, Matplot\n",
    "from pymc import (Uniform, DiscreteUniform, Beta, Lambda, Binomial, Normal, \n",
    "                  Poisson, NegativeBinomial, observed, negative_binomial_like, poisson_like,\n",
    "                  Lognormal, Exponential, binomial_like, stochastic, potential, \n",
    "                  invlogit, TruncatedNormal, Binomial, Gamma, HalfCauchy, normal_like,\n",
    "                  deterministic, MvNormalCov, Bernoulli, potential, Uninformative, \n",
    "                  Multinomial, rmultinomial, rbinomial, AdaptiveMetropolis,\n",
    "                  Dirichlet, multinomial_like)\n",
    "\n",
    "import ipdb\n",
    "\n",
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "data_dir = \"data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import outbreak data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0)\n",
    "measles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\n",
    "measles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\n",
    "measles_data.ONSET = pd.to_datetime(measles_data.ONSET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})"
   ]
  },
  {
   "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)"
   ]
  },
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   "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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0 a 4 anos</th>\n",
       "      <th>5 a 9 anos</th>\n",
       "      <th>10 a 14 anos</th>\n",
       "      <th>15 a 19 anos</th>\n",
       "      <th>20 a 24 anos</th>\n",
       "      <th>25 a 29 anos</th>\n",
       "      <th>30 a 34 anos</th>\n",
       "      <th>35 a 39 anos</th>\n",
       "      <th>40 a 44 anos</th>\n",
       "      <th>45 a 49 anos</th>\n",
       "      <th>50 a 54 anos</th>\n",
       "      <th>55 a 59 anos</th>\n",
       "      <th>60 a 64 anos</th>\n",
       "      <th>65 a 69 anos</th>\n",
       "      <th>70 a 74 anos</th>\n",
       "      <th>75 anos e +</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGUA RASA</th>\n",
       "      <td>5411</td>\n",
       "      <td>5750</td>\n",
       "      <td>6450</td>\n",
       "      <td>7122</td>\n",
       "      <td>7621</td>\n",
       "      <td>7340</td>\n",
       "      <td>6999</td>\n",
       "      <td>6984</td>\n",
       "      <td>6346</td>\n",
       "      <td>5608</td>\n",
       "      <td>4987</td>\n",
       "      <td>4212</td>\n",
       "      <td>4152</td>\n",
       "      <td>3595</td>\n",
       "      <td>2937</td>\n",
       "      <td>3637</td>\n",
       "      <td>89151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTO DE PINHEIROS</th>\n",
       "      <td>2070</td>\n",
       "      <td>2369</td>\n",
       "      <td>2953</td>\n",
       "      <td>3661</td>\n",
       "      <td>4612</td>\n",
       "      <td>4190</td>\n",
       "      <td>3539</td>\n",
       "      <td>3633</td>\n",
       "      <td>3448</td>\n",
       "      <td>3289</td>\n",
       "      <td>3040</td>\n",
       "      <td>2533</td>\n",
       "      <td>2298</td>\n",
       "      <td>1732</td>\n",
       "      <td>1305</td>\n",
       "      <td>1823</td>\n",
       "      <td>46495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANHANGUERA</th>\n",
       "      <td>3068</td>\n",
       "      <td>3006</td>\n",
       "      <td>2755</td>\n",
       "      <td>2431</td>\n",
       "      <td>2426</td>\n",
       "      <td>2636</td>\n",
       "      <td>2695</td>\n",
       "      <td>2308</td>\n",
       "      <td>1653</td>\n",
       "      <td>1107</td>\n",
       "      <td>753</td>\n",
       "      <td>509</td>\n",
       "      <td>352</td>\n",
       "      <td>217</td>\n",
       "      <td>162</td>\n",
       "      <td>171</td>\n",
       "      <td>26249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARICANDUVA</th>\n",
       "      <td>7732</td>\n",
       "      <td>7730</td>\n",
       "      <td>8373</td>\n",
       "      <td>8956</td>\n",
       "      <td>9182</td>\n",
       "      <td>8531</td>\n",
       "      <td>7813</td>\n",
       "      <td>7365</td>\n",
       "      <td>6551</td>\n",
       "      <td>5554</td>\n",
       "      <td>4887</td>\n",
       "      <td>3858</td>\n",
       "      <td>3320</td>\n",
       "      <td>2449</td>\n",
       "      <td>1611</td>\n",
       "      <td>1723</td>\n",
       "      <td>95635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTUR ALVIM</th>\n",
       "      <td>9031</td>\n",
       "      <td>9078</td>\n",
       "      <td>10000</td>\n",
       "      <td>11058</td>\n",
       "      <td>11387</td>\n",
       "      <td>10347</td>\n",
       "      <td>9125</td>\n",
       "      <td>8658</td>\n",
       "      <td>7830</td>\n",
       "      <td>7055</td>\n",
       "      <td>5919</td>\n",
       "      <td>4612</td>\n",
       "      <td>3756</td>\n",
       "      <td>2633</td>\n",
       "      <td>1727</td>\n",
       "      <td>1724</td>\n",
       "      <td>113940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\n",
       "AGUA RASA                5411        5750          6450          7122   \n",
       "ALTO DE PINHEIROS        2070        2369          2953          3661   \n",
       "ANHANGUERA               3068        3006          2755          2431   \n",
       "ARICANDUVA               7732        7730          8373          8956   \n",
       "ARTUR ALVIM              9031        9078         10000         11058   \n",
       "\n",
       "                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\n",
       "AGUA RASA                  7621          7340          6999          6984   \n",
       "ALTO DE PINHEIROS          4612          4190          3539          3633   \n",
       "ANHANGUERA                 2426          2636          2695          2308   \n",
       "ARICANDUVA                 9182          8531          7813          7365   \n",
       "ARTUR ALVIM               11387         10347          9125          8658   \n",
       "\n",
       "                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\n",
       "AGUA RASA                  6346          5608          4987          4212   \n",
       "ALTO DE PINHEIROS          3448          3289          3040          2533   \n",
       "ANHANGUERA                 1653          1107           753           509   \n",
       "ARICANDUVA                 6551          5554          4887          3858   \n",
       "ARTUR ALVIM                7830          7055          5919          4612   \n",
       "\n",
       "                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\n",
       "AGUA RASA                  4152          3595          2937         3637   \n",
       "ALTO DE PINHEIROS          2298          1732          1305         1823   \n",
       "ANHANGUERA                  352           217           162          171   \n",
       "ARICANDUVA                 3320          2449          1611         1723   \n",
       "ARTUR ALVIM                3756          2633          1727         1724   \n",
       "\n",
       "                    Total  \n",
       "AGUA RASA           89151  \n",
       "ALTO DE PINHEIROS   46495  \n",
       "ANHANGUERA          26249  \n",
       "ARICANDUVA          95635  \n",
       "ARTUR ALVIM        113940  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of cumulative cases by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10be6f908>"
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     "execution_count": 8,
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     "output_type": "execute_result"
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3+JLU4mLRnHE9fjfS25BvAJqBS1vPsQy4GUgEbif643ymdT+ti3ZFUVo11nt5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\nKkrlSDj3aroeehZn9jS+mthBQWwdV512P+MLXB5Zdjt1AxaKbxSTu00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bRbsIEmiYKBza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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be6f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\n",
    "measles_onset_dist.cumsum().plot(legend=False, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_district_cases = measles_onset_dist.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 5 districts by number of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DISTRICT\n",
       "GRAJAU             1074\n",
       "JARDIM ANGELA       944\n",
       "CAPAO REDONDO       849\n",
       "JARDIM SAO LUIZ     778\n",
       "CAMPO LIMPO         692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = measles_onset_dist.sum()\n",
    "totals.sort(ascending=False)\n",
    "totals[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of cases, by confirmation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33nmH0047LVPQ5bPP+fPn07dv36LkBTQ4MUZjxRWkTtm86aabGD16NHPnzmWr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\nkiSpRFnQSZIkSVKJ+v/WTYzEnInmKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104115dd8>"
      ]
     },
     "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": [
    "## 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": 12,
   "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": 13,
   "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lOKA9Ii4l63FrL8Vaqoyb0EmStJqo9GBy8OHkktQf/UnoFgHrASeSJWhXlmIt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3fbb00>"
      ]
     },
     "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=(-1,50), figsize=(15,5), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39982, 16)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22097"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60257048468117846"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(measles_data[CONFIRMED].YEAR_AGE>20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excludes = ['BOM RETIRO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N = sp_pop.ix[unique_districts, 'Total'].dropna()\n",
    "N = N.drop(excludes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_districts = N.index.values\n",
    "len(sp_districts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_district_data = []\n",
    "all_confirmed_cases = []\n",
    "for d in sp_districts:\n",
    "\n",
    "    # All bi-weekly unconfirmed and confirmed cases\n",
    "    district_data = lab_subset[lab_subset.DISTRICT==d]\n",
    "    district_counts_2w = district_data.groupby(\n",
    "        ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).resample('2W', how='sum')\n",
    "    all_district_data.append(district_counts_2w)\n",
    "\n",
    "    # All confirmed cases, by district\n",
    "    confirmed_data = district_data[district_data.CONCLUSION=='CONFIRMED']\n",
    "    confirmed_counts = confirmed_data.groupby(\n",
    "        ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).sum()\n",
    "    all_confirmed_cases.append(confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique()).fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series of cases by district, summarized in 2-week intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum over ages for susceptibles\n",
    "sp_cases_2w = [dist.sum(1) for dist in all_district_data]\n",
    "len(sp_cases_2w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measles_data.AGE_GROUP.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ensure the age groups are ordered\n",
    "I_obs = np.array([dist.reindex_axis(measles_data.AGE_GROUP.cat.categories, \n",
    "                            axis=1).fillna(0).values.astype(int) for dist in all_district_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Sum over districts\n",
    "I_obs = I_obs.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "641"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16640"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups = measles_data.AGE_GROUP.cat.categories\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.16300000e+03,   3.52400000e+03,   1.88500000e+03,\n",
       "          8.77000000e+02,   3.65000000e+02,   1.19000000e+02,\n",
       "          3.50000000e+01,   2.30000000e+01,   6.00000000e+00,\n",
       "          3.00000000e+00]),\n",
       " array([ 0.00020843,  0.01143471,  0.02266099,  0.03388727,  0.04511355,\n",
       "         0.05633983,  0.06756612,  0.0787924 ,  0.09001868,  0.10124496,\n",
       "         0.11247124]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YnPMFY4yzqz+q/mCz+PfmnO8YY7yo+rnqkeoL1dVzzkfGGJdXb6kerj4857x+z/cCAIAT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e3c13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc import rbeta\n",
    "plt.hist(rbeta(2, 100, 10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obs_date = '1997-06-15'\n",
    "obs_index = all_district_data[0].index <= obs_date\n",
    "I_obs_t = I_obs[obs_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Extract cases by age and time.\n",
    "\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_index = pd.cut(lab_subset.AGE[lab_subset.ONSET < obs_date], age_classes, right=False, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "confirmed = (lab_subset[lab_subset.ONSET < obs_date].CONCLUSION=='CONFIRMED').values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empirical confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1206cdb70>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vN1qZJEmS5mSmgPd+4MPAI4FP7LfNU7SSJEnz1LQBLzPfDLw5Iv40M18+xJokSZJ0AAaZ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f5cfe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame({'age':age_index, \n",
    "                   'confirmed':confirmed}).groupby('age')['confirmed'].mean().plot(kind='bar')\n",
    "ax.set_xticklabels(age_group.categories);\n",
    "ax.set_ylabel('Confirmation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.11950395,  0.18038331,  0.06989853,  0.06087937,  0.24802706,\n",
       "        0.21984216,  0.06989853,  0.02029312,  0.01127396])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(I_obs_t, (0)) / float(I_obs_t.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   0,   0,   0,   0,   0,   0,   0],\n",
       "       [  3,   2,   1,   0,   0,   2,   0,   0,   0],\n",
       "       [  1,   5,   0,   0,   1,   0,   0,   0,   0],\n",
       "       [  4,   2,   0,   0,   1,   1,   0,   0,   0],\n",
       "       [  3,   1,   2,   4,   4,   1,   1,   0,   1],\n",
       "       [  6,   5,   2,   4,   8,  10,   4,   0,   0],\n",
       "       [  5,  14,   2,   5,   6,   2,   1,   0,   0],\n",
       "       [  8,  10,   2,   3,   6,   5,   1,   1,   1],\n",
       "       [  6,   9,   3,   4,  13,   6,   5,   0,   1],\n",
       "       [ 13,  21,   8,   8,  23,  26,   9,   5,   0],\n",
       "       [ 19,  38,  12,   9,  64,  41,  14,   4,   5],\n",
       "       [ 38,  52,  30,  17,  94, 101,  27,   8,   2]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs_t.sum((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def measles_model(obs_date, confirmation=True, spatial_weighting=False, all_traces=False):\n",
    "    \n",
    "    '''\n",
    "    Truncate data at observation period\n",
    "    '''\n",
    "    obs_index = all_district_data[0].index <= obs_date\n",
    "    I_obs_t = I_obs[obs_index]  \n",
    "\n",
    "    age = lab_subset[lab_subset.ONSET < obs_date].YEAR_AGE.values\n",
    "    age_cutpoints = [0,5,10,15,20,25,30,35,40,100]\n",
    "#     age_group = pd.cut(age, age_cutpoints, right=False)\n",
    "    age_index = pd.cut(age, age_cutpoints, right=False, labels=False)\n",
    "    # Indicators for lab-checked individuals who were confirmed\n",
    "    confirmed = (lab_subset[lab_subset.ONSET < obs_date].CONCLUSION=='CONFIRMED').values\n",
    "\n",
    "    # Index for observation date, used to index out values of interest \n",
    "    # from the model.\n",
    "    t_obs = obs_index.sum() - 1\n",
    "\n",
    "    n_periods, n_age_groups = I_obs_t.shape\n",
    "\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",
    "        tau = sig**-2\n",
    "\n",
    "        # Age-specific probabilities of confirmation as multivariate normal \n",
    "        # random variables\n",
    "        beta_age = Normal(\"beta_age\", mu=mu, tau=tau, \n",
    "                        value=[1]*len(age_classes))\n",
    "        p_age = Lambda('p_age', lambda t=beta_age: invlogit(t))\n",
    "\n",
    "        @deterministic()\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=confirmed, \n",
    "                                observed=True)\n",
    "\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: confirm by age, then re-combine\n",
    "            \n",
    "            return np.sum([binomial_like(value[:,a], n[:,a], p[a]) \n",
    "                            for a in range(n_age_groups)])\n",
    "\n",
    "        age_dist_init = np.sum(I.value, 0)/ float(I.value.sum())\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        I = I_obs_t\n",
    "        age_dist_init = np.sum(I, 0) / float(I.sum())\n",
    "        \n",
    "    assert I.shape == (t_obs +1, n_age_groups)\n",
    "        \n",
    "        \n",
    "    # Calcuate age distribution from observed distribution of infecteds to date\n",
    "    age_dist = Dirichlet('age_dist', np.ones(n_age_groups), value=age_dist_init[:-1]/age_dist_init.sum())\n",
    "    @potential\n",
    "    def age_dist_like(p=age_dist, I=I):\n",
    "        p = np.append(p, 1-p.sum())\n",
    "        return multinomial_like(I.sum(0), I.sum(), p)\n",
    "\n",
    "    # Weakly-informative prior on proportion susceptible being \n",
    "    # between 0 and 0.07\n",
    "    p_susceptible = 0.04 #Beta('p_susceptible', 2, 100, value=0.06)\n",
    "    \n",
    "    # Estimated total initial susceptibles\n",
    "    S_0 = Binomial('S_0', n=int(N.sum()), p=p_susceptible)\n",
    "\n",
    "    # Calculate susceptibles from total number of infections\n",
    "    S = Lambda('S', lambda I=I, S_0=S_0: S_0 - I.sum(1).cumsum())\n",
    "    \n",
    "    # Check shape\n",
    "    assert S.value.shape == t_obs+1\n",
    "    \n",
    "    # Susceptibles at time t, by age\n",
    "    @deterministic\n",
    "    def S_age(S=S, p=age_dist):\n",
    "        p = np.append(p, 1-p.sum())\n",
    "        return rmultinomial(S[-1], p)\n",
    "    \n",
    "    assert S_age.value.shape == (n_age_groups,)\n",
    "    \n",
    "    # Transmission parameter\n",
    "    β = Uniform('β', 1, 50, value=10)         \n",
    "\n",
    "    \n",
    "    α = 1 #= Exponential('α', 1, value=1)\n",
    "    \n",
    "    # Force of infection\n",
    "    @deterministic\n",
    "    def λ(β=β, I=I, S=S, α=α): \n",
    "\n",
    "         return β * I.sum(1)**α * S / N.sum()\n",
    "    \n",
    "    # Check shape\n",
    "    assert λ.value.shape == (t_obs+1,)\n",
    "    \n",
    "    # FOI in observation period\n",
    "    λ_t = Lambda('λ_t', lambda λ=λ: λ[-1])\n",
    "    \n",
    "    # Poisson likelihood for observed cases\n",
    "    @potential\n",
    "    def new_cases(I=I, λ=λ): \n",
    "#         x = I.sum(1)\n",
    "#         return negative_binomial_like(x[1:], λ[:-1], x[:-1]+1)\n",
    "        return poisson_like(I.sum(1)[1:], λ[:-1])\n",
    "    \n",
    "    \n",
    "    '''\n",
    "    Vaccination targets\n",
    "    '''\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_5(S=S_age):\n",
    "        # Vaccination of 15 and under\n",
    "        p = [0.95] + [0]*(n_age_groups-1)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of susceptibles vaccinated\n",
    "    pct_5 = Lambda('pct_5', \n",
    "                lambda V=vacc_5, S=S_age: float(V.sum())/S.sum())\n",
    "\n",
    "\n",
    "    @deterministic\n",
    "    def vacc_15(S=S_age):\n",
    "        # Vaccination of 15 and under\n",
    "        p = [0.95]*3 + [0]*(n_age_groups-3)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of susceptibles vaccinated\n",
    "    pct_15 = Lambda('pct_15', \n",
    "            lambda V=vacc_15, S=S_age: float(V.sum())/S.sum())\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_30(S=S_age):\n",
    "        # Vaccination of 30 and under\n",
    "        p = [0.95]*6 + [0]*(n_age_groups-6)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of 30 and under susceptibles vaccinated\n",
    "    pct_30 = Lambda('pct_30', \n",
    "            lambda V=vacc_30, S=S_age: float(V.sum())/S.sum())\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_adult(S=S_age):\n",
    "        # Vaccination of adults under 30 (and young kids)\n",
    "        p = [0.95, 0, 0, 0, 0.95, 0.95] + [0]*(n_age_groups-6)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of adults under 30 (and young kids)\n",
    "    pct_adult = Lambda('pct_adult', \n",
    "            lambda V=vacc_adult, S=S_age: float(V.sum())/S.sum())\n",
    "\n",
    "    return locals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run models for June 15 and July 15 observation points, both with and without clinical confirmation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = 'ram'\n",
    "n_iterations = 50000\n",
    "n_burn = 40000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = measles_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc import Slicer\n",
    "\n",
    "model_june = MCMC(model('1997-06-15'), db=db, dbname='model_june')\n",
    "model_june.use_step_method(AdaptiveMetropolis, model_june.beta_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{<pymc.distributions.new_dist_class.<locals>.new_class 'sig' at 0x120de9e10>,\n",
       " <pymc.distributions.new_dist_class.<locals>.new_class 'β' at 0x10e4aac18>,\n",
       " <pymc.distributions.new_dist_class.<locals>.new_class 'mu' at 0x120de9c50>,\n",
       " <pymc.distributions.new_dist_class.<locals>.new_class 'age_dist' at 0x10c609668>,\n",
       " <pymc.distributions.new_dist_class.<locals>.new_class 'S_0' at 0x120ce9908>,\n",
       " <pymc.PyMCObjects.Stochastic 'I' at 0x10c612d68>,\n",
       " <pymc.distributions.new_dist_class.<locals>.new_class 'beta_age' at 0x120de9f60>}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_june.stochastics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 50000 of 50000 complete in 56.2 sec"
     ]
    }
   ],
   "source": [
    "model_june.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### July 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_july = MCMC(model('1997-07-15'), db=db, dbname='model_july')\n",
    "model_july.use_step_method(AdaptiveMetropolis, model_july.beta_age)\n",
    "# model_july.use_step_method(AdaptiveMetropolis, [model_july.β, model_july.p_susceptible])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 50000 of 50000 complete in 66.3 sec"
     ]
    }
   ],
   "source": [
    "model_july.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, no lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june_noconf = MCMC(model('1997-06-15', confirmation=False), \n",
    "                         db=db, dbname='model_june_noconf')\n",
    "# model_june_noconf.use_step_method(AdaptiveMetropolis, model_june_noconf.age_dist)\n",
    "# model_june_noconf.use_step_method(Slicer, model_june_noconf.beta_age)\n",
    "# model_june_noconf.use_step_method(Slicer, model_june_noconf.p_susceptible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 50000 of 50000 complete in 24.4 sec"
     ]
    }
   ],
   "source": [
    "model_june_noconf.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "July 15, no lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_july_noconf = MCMC(model('1997-07-15', \n",
    "                                       confirmation=False), \n",
    "                         db=db, dbname='model_july_noconf')\n",
    "# model_july_noconf.use_step_method(AdaptiveMetropolis, [model_july_noconf.β, model_july_noconf.p_susceptible])\n",
    "# model_july_noconf.use_step_method(AdaptiveMetropolis, model_july_noconf.beta_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 50000 of 50000 complete in 23.7 sec"
     ]
    }
   ],
   "source": [
    "model_july_noconf.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of model output\n",
    "\n",
    "Distance weighting parameter for june model with confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting S_0\n"
     ]
    },
    {
     "data": {
      "image/png": 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VbKis5oG3Vyc9HGsphMINc+rWbwsEoT/726cJH2P5+kpGPfkJz33yLRC97ZLJwQsdI1L+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\nIYQQHeB/o27uWeZO1DwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120cfe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.S_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab confirmation rates, June model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b733f98>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaDNwEUBmbouIbRRB57py/ZHANyLiEuCnwB9VWIskSRLTO4fjWjbtdguARmPwZyYUfbJx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b715128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "\n",
    "p_age = pd.DataFrame(model_june.p_age.trace(), columns=age_group.categories)\n",
    "\n",
    "f, axes = plt.subplots(figsize=(14,6))\n",
    "sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n",
    "          color=sb.color_palette(\"coolwarm\", 5)[0],\n",
    "          order=age_group.categories)\n",
    "axes.set_ylabel('Confirmation rate')\n",
    "axes.set_xlabel('Age group')\n",
    "axes.set_title('July confirmation estimates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1185ca470>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JHJKkiZgcSZLapr+/v+uGe++2eCRJ3cvkSJKkGWDbtm33tYZ1WrfEMWpgYID+fi9pJE2d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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118594828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "\n",
    "p_age = pd.DataFrame(model_july.p_age.trace(), columns=age_group.categories)\n",
    "\n",
    "f, axes = plt.subplots(figsize=(14,6))\n",
    "sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n",
    "          color=sb.color_palette(\"coolwarm\", 5)[0],\n",
    "          order=age_group.categories)\n",
    "axes.set_ylabel('Confirmation rate')\n",
    "axes.set_xlabel('Age group')\n",
    "axes.set_title('June confirmation estimates')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting β\n"
     ]
    },
    {
     "data": {
      "image/png": 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4W1wuF2Vl3dqc85qfjGFX9Z6W6/3ip2N5f+53LPm24zSLrgXhg5uJYwfyiV9+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\nIiIZUFElIiIikgEVVSIiIiIZ+D9WKzdwycsljAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c52a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.β)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model with no confirmation correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "plot() missing 1 required positional argument: 'name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-f73097280a32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_june\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp_susceptible\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/fonnescj/GitHub/pymc/pymc/Matplot.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(pymc_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0;31m# If others fail, assume that raw data is passed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m         \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpymc_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: plot() missing 1 required positional argument: 'name'"
     ]
    }
   ],
   "source": [
    "Matplot.plot(model_june.p_susceptible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity estimates at June and July, per district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting λ_t\n"
     ]
    },
    {
     "data": {
      "image/png": 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/s4Rr/if8vI7H3oy803bKtKX1Xns8cM1Dn1FWUc3TNx1HWhMOi89etIm+3dvT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\nbLeIiIhIixAuZ+s64DqXXeNcjp0CTGm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crTOBbGvtaGPMSOBB37Za9wFDgFJgiTHmRWvt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\nFllsSZIktej/AcjrWFPdZCNaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c698358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.λ_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity for lab- versus clinical-confirmation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12d4c7e48>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W04Ob2bnAOHe/AtgCZIFHzGyGu98HnA7MIyT9OWZWR0j+BwKNXR27qamlp+EMSA0N9Sqr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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ccbc9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lam_june = model_june.λ.stats()\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharey=True)\n",
    "\n",
    "axes[0].plot(lam_june['quantiles'][50].T, 'b-', alpha=0.4)\n",
    "axes[0].set_ylabel('Epidemic intensity')\n",
    "axes[0].set_xlabel('time (2-week periods)')\n",
    "axes[0].set_title('Lab confirmation')\n",
    "\n",
    "lam_june_noconf = model_june_noconf.λ.stats()\n",
    "\n",
    "axes[1].plot(lam_june_noconf['quantiles'][50].T, 'b-', alpha=0.4)\n",
    "axes[1].set_ylabel('Epidemic intensity')\n",
    "axes[1].set_xlabel('time (2-week periods)')\n",
    "axes[1].set_title('Clinical confirmation')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(model_june.S_age.trace(), columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(model_june.S_age.trace(), \n",
    "                          columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_june_noconf = pd.DataFrame(model_june_noconf.S_age.trace(), \n",
    "                                 columns=age_group.categories).unstack().reset_index()\n",
    "S_age_june_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_july = pd.DataFrame(model_july.S_age.trace(), \n",
    "                          columns=age_group.categories).unstack().reset_index()\n",
    "S_age_july.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_july_noconf = pd.DataFrame(model_july_noconf.S_age.trace(), \n",
    "                                 columns=age_group.categories).unstack().reset_index()\n",
    "S_age_july_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numbers of suscepibles in each age group, under lab vs clinical confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wr5m5PSIuohzJetsyz/954EBmfrDftlXPOTUzf2WlbXuF03aU3ZZHUy7CemNV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4d0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "sb.set_context(\"talk\", font_scale=0.8)\n",
    "sb.set_style(\"white\")\n",
    "\n",
    "g = sb.factorplot(\"Age\", \"S\", \"Confirmation\", S_age_june, kind=\"box\",\n",
    "                   palette=\"hls\", size=6, aspect=2, linewidth=0.3, fliersize=0, \n",
    "                  order=age_group.categories)\n",
    "g.despine(offset=10, trim=True)\n",
    "g.set_axis_labels(\"Age Group\", \"Susceptibles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_lam = pd.DataFrame(model_june.λ_t.trace()).unstack().reset_index()\n",
    "june_lam.columns = ('district', 'iteration', 'λ')\n",
    "june_lam['month'] = 'June'\n",
    "june_lam['confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_lam_noconf = pd.DataFrame(model_june_noconf.λ_t.trace()).unstack().reset_index()\n",
    "june_lam_noconf.columns = ('district', 'iteration', 'λ')\n",
    "june_lam_noconf['month'] = 'June'\n",
    "june_lam_noconf['confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "july_lam = pd.DataFrame(model_july.λ_t.trace()).unstack().reset_index()\n",
    "july_lam.columns = ('district', 'iteration', 'λ')\n",
    "july_lam['month'] = 'July'\n",
    "july_lam['confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "july_lam_noconf = pd.DataFrame(model_july_noconf.λ_t.trace()).unstack().reset_index()\n",
    "july_lam_noconf.columns = ('district', 'iteration', 'λ')\n",
    "july_lam_noconf['month'] = 'July'\n",
    "july_lam_noconf['confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "confirmed_lam = june_lam.append(july_lam, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity in June and July (with lab confirmation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yNDS03MNhhdm7d+9yD2FRmReZjnmR2cw0N84nCI/1vv5Oko+VUrYnOZrkjb3l1yTZ0Tu1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161eb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confirmed_lam.groupby('month').boxplot(column='λ', return_type='dict');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity in June for lab-confirmed and clinical-confirmed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "june_lam = june_lam.append(june_lam_noconf, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uTHIgySNJbqi1HljA2gEAOEaDZv6uSTJda31rkrcmuSnJbye5r9Z6WZI7ktzW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\nAGjI/wdFjKmX4OpY4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a34e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "june_lam.groupby('confirmation').boxplot(column='λ', return_type='dict');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pct_5:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.239            0.021            0.001            [ 0.199  0.281]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.2              0.225           0.239          0.253         0.283\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.576            0.028            0.002            [ 0.516  0.626]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.518            0.555           0.578          0.595         0.63\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.803            0.018            0.001            [ 0.767  0.837]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.766            0.793           0.804          0.815         0.837\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.366            0.027            0.002            [ 0.313  0.416]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.315            0.348           0.365          0.383         0.418\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "june_coverage = pd.DataFrame({name: model_june.trace(name)[:] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "june_coverage['Month'] = 'June'\n",
    "june_coverage['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_noconf_coverage = pd.DataFrame({name: model_june_noconf.trace(name)[:] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "june_noconf_coverage['Month'] = 'June'\n",
    "june_noconf_coverage['Confirmation'] = 'Clinical'\n",
    "\n",
    "july_coverage = pd.DataFrame({name: model_july.trace(name)[:] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "july_coverage['Month'] = 'July'\n",
    "july_coverage['Confirmation'] = 'Lab'\n",
    "\n",
    "july_noconf_coverage = pd.DataFrame({name: model_july_noconf.trace(name)[:] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "july_noconf_coverage['Month'] = 'July'\n",
    "july_noconf_coverage['Confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], \n",
    "                     ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x119bce240>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SSnb22WefCnwvM+9fqJ19qyQtzoH613YBeX7oHQZmisf3R8RhdUiev20hjz77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\nMzMzs4r/A6EES0lFz5iyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8b0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(row=\"Month\", col=\"Confirmation\", data=coverage, kind='box',\n",
    "              row_order=['June', 'July'],\n",
    "              order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'],\n",
    "               palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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sRIcPL8L0crB3b3LkyPQUMG9lgjTJzyZ5QVXdmuSuSV7U3b89PBPsWP4SvXxsbCQHDy5u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120b97f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june.p_age)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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