fonnesbeck/Non-Spatial Measles Model.ipynb

## 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "name": "stderr",
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     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "sb.set()\n",
    "import pdb\n",
    "\n",
    "from IPython.core.display import HTML\n",
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    "    styles = open(\"styles/custom.css\", \"r\").read()\n",
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    "css_styling()"
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "data_dir = \"data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import outbreak data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0)\n",
    "measles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\n",
    "measles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\n",
    "measles_data.ONSET = pd.to_datetime(measles_data.ONSET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sao Paulo population by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_names = sp_pop.index.values\n",
    "_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\n",
    "sp_pop.set_index(_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0 a 4 anos</th>\n",
       "      <th>5 a 9 anos</th>\n",
       "      <th>10 a 14 anos</th>\n",
       "      <th>15 a 19 anos</th>\n",
       "      <th>20 a 24 anos</th>\n",
       "      <th>25 a 29 anos</th>\n",
       "      <th>30 a 34 anos</th>\n",
       "      <th>35 a 39 anos</th>\n",
       "      <th>40 a 44 anos</th>\n",
       "      <th>45 a 49 anos</th>\n",
       "      <th>50 a 54 anos</th>\n",
       "      <th>55 a 59 anos</th>\n",
       "      <th>60 a 64 anos</th>\n",
       "      <th>65 a 69 anos</th>\n",
       "      <th>70 a 74 anos</th>\n",
       "      <th>75 anos e +</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGUA RASA</th>\n",
       "      <td>5411</td>\n",
       "      <td>5750</td>\n",
       "      <td>6450</td>\n",
       "      <td>7122</td>\n",
       "      <td>7621</td>\n",
       "      <td>7340</td>\n",
       "      <td>6999</td>\n",
       "      <td>6984</td>\n",
       "      <td>6346</td>\n",
       "      <td>5608</td>\n",
       "      <td>4987</td>\n",
       "      <td>4212</td>\n",
       "      <td>4152</td>\n",
       "      <td>3595</td>\n",
       "      <td>2937</td>\n",
       "      <td>3637</td>\n",
       "      <td>89151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTO DE PINHEIROS</th>\n",
       "      <td>2070</td>\n",
       "      <td>2369</td>\n",
       "      <td>2953</td>\n",
       "      <td>3661</td>\n",
       "      <td>4612</td>\n",
       "      <td>4190</td>\n",
       "      <td>3539</td>\n",
       "      <td>3633</td>\n",
       "      <td>3448</td>\n",
       "      <td>3289</td>\n",
       "      <td>3040</td>\n",
       "      <td>2533</td>\n",
       "      <td>2298</td>\n",
       "      <td>1732</td>\n",
       "      <td>1305</td>\n",
       "      <td>1823</td>\n",
       "      <td>46495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANHANGUERA</th>\n",
       "      <td>3068</td>\n",
       "      <td>3006</td>\n",
       "      <td>2755</td>\n",
       "      <td>2431</td>\n",
       "      <td>2426</td>\n",
       "      <td>2636</td>\n",
       "      <td>2695</td>\n",
       "      <td>2308</td>\n",
       "      <td>1653</td>\n",
       "      <td>1107</td>\n",
       "      <td>753</td>\n",
       "      <td>509</td>\n",
       "      <td>352</td>\n",
       "      <td>217</td>\n",
       "      <td>162</td>\n",
       "      <td>171</td>\n",
       "      <td>26249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARICANDUVA</th>\n",
       "      <td>7732</td>\n",
       "      <td>7730</td>\n",
       "      <td>8373</td>\n",
       "      <td>8956</td>\n",
       "      <td>9182</td>\n",
       "      <td>8531</td>\n",
       "      <td>7813</td>\n",
       "      <td>7365</td>\n",
       "      <td>6551</td>\n",
       "      <td>5554</td>\n",
       "      <td>4887</td>\n",
       "      <td>3858</td>\n",
       "      <td>3320</td>\n",
       "      <td>2449</td>\n",
       "      <td>1611</td>\n",
       "      <td>1723</td>\n",
       "      <td>95635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTUR ALVIM</th>\n",
       "      <td>9031</td>\n",
       "      <td>9078</td>\n",
       "      <td>10000</td>\n",
       "      <td>11058</td>\n",
       "      <td>11387</td>\n",
       "      <td>10347</td>\n",
       "      <td>9125</td>\n",
       "      <td>8658</td>\n",
       "      <td>7830</td>\n",
       "      <td>7055</td>\n",
       "      <td>5919</td>\n",
       "      <td>4612</td>\n",
       "      <td>3756</td>\n",
       "      <td>2633</td>\n",
       "      <td>1727</td>\n",
       "      <td>1724</td>\n",
       "      <td>113940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\n",
       "AGUA RASA                5411        5750          6450          7122   \n",
       "ALTO DE PINHEIROS        2070        2369          2953          3661   \n",
       "ANHANGUERA               3068        3006          2755          2431   \n",
       "ARICANDUVA               7732        7730          8373          8956   \n",
       "ARTUR ALVIM              9031        9078         10000         11058   \n",
       "\n",
       "                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\n",
       "AGUA RASA                  7621          7340          6999          6984   \n",
       "ALTO DE PINHEIROS          4612          4190          3539          3633   \n",
       "ANHANGUERA                 2426          2636          2695          2308   \n",
       "ARICANDUVA                 9182          8531          7813          7365   \n",
       "ARTUR ALVIM               11387         10347          9125          8658   \n",
       "\n",
       "                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\n",
       "AGUA RASA                  6346          5608          4987          4212   \n",
       "ALTO DE PINHEIROS          3448          3289          3040          2533   \n",
       "ANHANGUERA                 1653          1107           753           509   \n",
       "ARICANDUVA                 6551          5554          4887          3858   \n",
       "ARTUR ALVIM                7830          7055          5919          4612   \n",
       "\n",
       "                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\n",
       "AGUA RASA                  4152          3595          2937         3637   \n",
       "ALTO DE PINHEIROS          2298          1732          1305         1823   \n",
       "ANHANGUERA                  352           217           162          171   \n",
       "ARICANDUVA                 3320          2449          1611         1723   \n",
       "ARTUR ALVIM                3756          2633          1727         1724   \n",
       "\n",
       "                    Total  \n",
       "AGUA RASA           89151  \n",
       "ALTO DE PINHEIROS   46495  \n",
       "ANHANGUERA          26249  \n",
       "ARICANDUVA          95635  \n",
       "ARTUR ALVIM        113940  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of cumulative cases by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f465cc31358>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+DehE590+5nIpoxSvTSILLsTlbRx23rYNUh1PgeYgtuiTuP0tp/38ZCnF/97+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\nJ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vLGgVS2ECS9MLbzBvt+k2jdtLyZidQWM2O9fSM0CbTG8yx0PQpv+97hf//P5y4mtFCZ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\nAwSei7v2LggQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f465cc480f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\n",
    "measles_onset_dist.cumsum().plot(legend=False, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_district_cases = measles_onset_dist.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 5 districts by number of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DISTRICT\n",
       "GRAJAU             1074\n",
       "JARDIM ANGELA       944\n",
       "CAPAO REDONDO       849\n",
       "JARDIM SAO LUIZ     778\n",
       "CAMPO LIMPO         692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = measles_onset_dist.sum()\n",
    "totals.sort(ascending=False)\n",
    "totals[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of cases, by confirmation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PcPvtbZk37wOaNGkGwOefFyz9f/fdPTh37izTpyexffuP+Pr6EBhYlZEjR/++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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f465d9f5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_conclusion = measles_data.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\n",
    "counts_by_cause = by_conclusion.size().unstack().fillna(0)\n",
    "ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIRTHS</th>\n",
       "      <th>VAX</th>\n",
       "      <th>POP</th>\n",
       "      <th>SIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3896442</td>\n",
       "      <td>0.57</td>\n",
       "      <td>121740438</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>3933136</td>\n",
       "      <td>0.73</td>\n",
       "      <td>124610790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3952137</td>\n",
       "      <td>0.66</td>\n",
       "      <td>127525420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>3952735</td>\n",
       "      <td>0.68</td>\n",
       "      <td>130455659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>3935224</td>\n",
       "      <td>0.73</td>\n",
       "      <td>133364277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BIRTHS   VAX        POP  SIA\n",
       "YEAR                               \n",
       "1980  3896442  0.57  121740438    0\n",
       "1981  3933136  0.73  124610790    0\n",
       "1982  3952137  0.66  127525420    0\n",
       "1983  3952735  0.68  130455659    0\n",
       "1984  3935224  0.73  133364277    0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\n",
    "vaccination_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]\n",
    "n = len(vax_97)\n",
    "FOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vacc_susc = (1 - vax_97*0.9)[::-1]\n",
    "vacc_susc[0] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sia_susc = np.ones(len(vax_97))\n",
    "birth_year = np.arange(1922, 1998)[::-1]\n",
    "by_mask = (birth_year > 1983) & (birth_year < 1992)\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": 16,
   "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": 17,
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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31azMWiaTSX/963y99tqrWr48Xi4ururUqbMiI59Qfn6+3nxzvkaMiFC9evXk6dlYr7zy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\nY4s36iIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f465ccb1940>"
      ]
     },
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39982, 16)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22097"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60257048468117846"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(measles_data[CONFIRMED].YEAR_AGE>20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5, 10, 15, 20, 25, 30, 35, 40, 100]"
      ]
     },
     "execution_count": 24,
     "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": [
    "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": [
    "#Extract cases by age and time.\n",
    "age_group = pd.cut(age, age_classes, right=False)\n",
    "age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_group.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excludes = ['BOM RETIRO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9727688.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = sp_pop.ix[unique_districts, 'Total'].dropna()\n",
    "N = N.drop(excludes).sum()\n",
    "N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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": 39,
   "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": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1442"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38502"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[0, 5)', '[10, 15)', '[15, 20)', '[20, 25)', '[25, 30)',\n",
       "       '[30, 35)', '[35, 40)', '[40, 100)', '[5, 10)'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups = np.sort(measles_data['AGE_GROUP'].unique())\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert 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": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.91700000e+03,   3.57900000e+03,   1.63300000e+03,\n",
       "          6.18000000e+02,   1.70000000e+02,   5.40000000e+01,\n",
       "          2.20000000e+01,   5.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00]),\n",
       " array([ 0.00014776,  0.0133144 ,  0.02648105,  0.03964769,  0.05281434,\n",
       "         0.06598098,  0.07914763,  0.09231428,  0.10548092,  0.11864757,\n",
       "         0.13181421]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N0tq1a2VZljIzM7V582alpqaqrq5Ou3btksPh0LJly1RUVKRwOKyKigq1t7fL4XCosrJS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\nAyvqR7cG1WIFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4657101588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc import rbeta\n",
    "plt.hist(rbeta(2, 100, 10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obs_date = '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": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.21114206,  0.20557103,  0.19164345,  0.16991643,  0.01559889,\n",
       "        0.06016713,  0.01281337,  0.06016713,  0.0729805 ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(I_obs_t, (0)) / float(I_obs_t.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1795"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs_t.sum((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   6,   56,   96,  137,  202,  332,  417,  504,  628,  842, 1189,\n",
       "       1795])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs_t.sum(1).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc import MCMC, Matplot, AdaptiveMetropolis\n",
    "from pymc import (Uniform, DiscreteUniform, Beta, Binomial, Normal, \n",
    "                  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, \n",
    "                  all_traces=False):\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*0.5).astype(int), n=I_obs_t, p=p_age):\n",
    "            # Binomial confirmation process\n",
    "            return np.sum([binomial_like(xi, ni, p) for xi,ni in zip(value,n)])\n",
    "\n",
    "        age_dist_init = np.sum(I.value, 0)/ float(I.value.sum())\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), \n",
    "                         value=age_dist_init[:-1]/age_dist_init.sum())\n",
    "    age_dist = CompletedDirichlet('age_dist', _age_dist)\n",
    "    @potential\n",
    "    def age_dist_like(p=age_dist, I=I):\n",
    "        return multinomial_like(I.sum(0), I.sum(), p)\n",
    "#     age_dist = age_dist_init\n",
    "\n",
    "    # Transmission parameter\n",
    "    beta = Uniform('beta', 1, 100, value=10)  \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",
    "    # Weakly-informative prior on proportion susceptible being \n",
    "    # between 0 and 0.07\n",
    "    p_susceptible = Beta('p_susceptible', 2, 100)\n",
    "    \n",
    "    # Estimated total initial susceptibles\n",
    "    S_0 = Binomial('S_0', n=int(N), p=p_susceptible)\n",
    "\n",
    "    S = Lambda('S', lambda I=I, S_0=S_0: S_0 - I.cumsum(axis=0))\n",
    "    \n",
    "    # Check shape\n",
    "    assert S.value.shape == (t_obs+1., n_age_groups)\n",
    "\n",
    "    S_t = Lambda('S_t', lambda S=S: S[-1])\n",
    "    \n",
    "    # Susceptibles at time t, by age\n",
    "    S_age = Multinomial('S_age', S_t, age_dist)       \n",
    "        \n",
    "    # Force of infection\n",
    "    @deterministic\n",
    "    def lam(beta=beta, I=I, S=S, N=N): \n",
    "        return beta * I.sum(axis=1) * S.sum(axis=1) / N\n",
    "    \n",
    "    # Check shape\n",
    "    assert lam.value.shape == (t_obs+1,) # n_age_groups)\n",
    "    \n",
    "    # FOI in observation period\n",
    "    lam_t = Lambda('lam_t', lambda lam=lam: lam[-1])\n",
    "    \n",
    "    # Poisson likelihood for observed cases\n",
    "    @potential\n",
    "    def new_cases(I=I, lam=lam):\n",
    "#         return negative_binomial_like(I[1:].sum(1), lam[:-1], I[:-1].sum(1))\n",
    "        return poisson_like(I.sum(axis=1), lam)\n",
    "    \n",
    "    '''\n",
    "    Vaccination targets\n",
    "    '''\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_5(S=S_age):\n",
    "        # Vaccination of 5 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: 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: 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: 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: 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": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = 'ram'\n",
    "n_iterations = 100000\n",
    "n_burn = 50000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = measles_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june = MCMC(model('1997-06-15'), db=db, dbname='model_june')\n",
    "# model_june.use_step_method(AdaptiveMetropolis, model_june._age_dist)\n",
    "# model_june.use_step_method(AdaptiveMetropolis, [model_june.beta, \n",
    "#                                                 model_june.p_susceptible])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 425.0 sec"
     ]
    }
   ],
   "source": [
    "model_june.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "July 15, with lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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.age_dist)\n",
    "# model_july.use_step_method(AdaptiveMetropolis, [model_july.beta, model_july.p_susceptible])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 428.3 sec"
     ]
    }
   ],
   "source": [
    "model_july.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June 15, no lab confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june_noconf = MCMC(model('1997-06-15', \n",
    "                                       confirmation=False), \n",
    "                         db=db, dbname='model_june_noconf')\n",
    "# model_june_noconf.use_step_method(AdaptiveMetropolis, [model_june_noconf.β, model_june_noconf.p_susceptible])\n",
    "# model_june_noconf.use_step_method(AdaptiveMetropolis, model_june_noconf.age_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 76.3 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": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_july_noconf = MCMC(model('1997-07-15', \n",
    "                                       confirmation=False), \n",
    "                         db=db, dbname='model_july_noconf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 100000 of 100000 complete in 81.0 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": 160,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting p_susceptible\n"
     ]
    },
    {
     "data": {
      "image/png": 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H8BdfLObTT5eSm3uZuXNnERAQQHr6eUA/+aNFi5a0bh3MG2/MATC6T3ss+ufvJJ/N5cW/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\nj4iIiMhK/T8ABRSPnqxesgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b284a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.p_susceptible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting beta\n"
     ]
    },
    {
     "data": {
      "image/png": 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REhGRTUl1nbuPl0bC0bme6Nf99PNhDHr8MfMXyIak2kZSxgCLiOqE+Ph4TJo0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b243b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.beta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab confirmation rates, June model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f64b1c63278>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F+TabTfZoTp87XEf7/wgAAAAEg99QdPzxxys/P1+7d+/We++9p3feeUf9+vULRW0BVb8/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\nDwAAQKD4vXnryJE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Jrl27dODAAe3YsaPVY7a5vkqM2eZ626i1Y/aRRx5R7969ZZqmKioq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\nKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b2565358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_age = pd.DataFrame(model_june.p_age.trace(), columns=age_groups)\n",
    "\n",
    "f, axes = plt.subplots(figsize=(14,6))\n",
    "sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n",
    "          color=sb.color_palette(\"coolwarm\", 5)[0],\n",
    "          order=age_group.categories)\n",
    "axes.set_ylabel('Confirmation rate')\n",
    "axes.set_xlabel('Age group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting beta\n"
     ]
    },
    {
     "data": {
      "image/png": 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iaWklQlqK1rnjP2ojbmg9TcOZM2ewbNkypW3u7u7IzX00g3ZOTg7c3d0NVzpC\nWqEj5zO4LgIhBkP5PfxHbcQNrXqwpFIpbG1tIRIpx2Pt2rXDgwcPkJWVBZlMhmPHjmHIkCFGKSgh\nhBBCiLnQqgcrNzdXKfdq//79sLe3x8iRI7F8+XLMmVMfHY8ZMwbt2+s2OSQhhBBCSGujVYDVs2dP\nbNu2TfH7uHHjFD8PGDAAu3fvNnzJCCGE8B7NscR/1Ebc4HyxZ0IIIeaLbtr8R23EDZMvlUMIIYQQ\n0tpRgEUIIYQQYmAUYBFCCNEbzbHEf9RG3KAcLEIIIXqj/B7+ozbiBvVgEUIIIYQYGAVYhBBCCCEG\nRkOEhBCzVF5ejgULFqC4uBg1NTWYNWsWnnjiCcybNw8sy8LNzQ1r166FhYUF4uPjERsbC6FQiAkT\nJiA0NBQymQwRERHIysqCUChEZGQkvLy8uK6W2aE5lviP2ogbFGARQszS/v370alTJ7z//vuQSqWY\nOnUq+vbti0mTJiEkJATR0dGIi4vD2LFjERMTg7i4OIhEIoSGhiI4OBhHjx6Fo6MjPv30UyQmJmLd\nunWIjo7mulpmh27a/EdtxA0aIiSEmCVnZ2cUFhYCAIqLi+Hs7IyzZ89ixIgRAICgoCAkJSUhNTUV\nfn5+sLW1hVgsRr9+/XD+/HkkJydj5MiRAIDBgwcjJSWFs7oQQlofCrAIIWZp1KhRyM7ORnBwMKZM\nmYIFCxagoqICFhYWAAAXFxdIpVLk5+fD2dlZ8TpnZ2fk5uYiLy9PsZ1hGAgEAshkMk7qQghpfWiI\nkBBiluLj49G2bVts27YNf//9NxYvXqy0n2VZla9Tt72urs7gZXwcUH4P/1EbcYMCLEKIWUpJScHQ\noUMBAL6+vsjJyYG1tTWqq6thaWmJnJwcSCQSuLu7Izc3V/G6nJwc+Pv7w93dHXl5efD19VX0XIlE\n2n0kurnZG75CRmCKci5fvrzF5zB2OcvLhRAIGKNeAwDEVha8+dtoWA5DtJGx8OX9MgYKsAghZql9\n+/a4ePEinn32WWRmZsLGxgYDBw5EQkICXnjhBRw6dAhDhw6Fn58flixZgrKyMjAMgwsXLmDx4sUo\nLS1FQkICAgMDcfToUQwcOFDra+fmlhqxZobh5mZP5XyovLwcdXWqey4NqaqyhhfvObW9YekbBFKA\nRQgxSy+//DIWLVqEyZMno7a2Fh999BE6duyIBQsWYM+ePfD09MS4ceMgFAoxd+5cTJs2DQKBAOHh\n4bCzs8PXkGO+AAAgAElEQVTo0aORmJiIiRMnQiwWIyoqiusqEUJaEQqwCCFmycbGBhs2bGiyfceO\nHU22BQcHIzg4WGmbQCBAZGSk0cr3uKD8Hv6jNuKGVgFWfHw8tm/fDpFIhNmzZ2P48OGKfd9//z1+\n+eUXCIVC9OrVCwsXLjRaYQkhhPAL3bT5j9qIGxqnaSgqKsKWLVuwe/dubN26FUeOHFHsKysrw/bt\n2/HDDz/g+++/x40bN3Dp0iWjFpgQQgghhO809mAlJSUhMDAQ1tbWsLa2xsqVKxX7LC0tIRaLUVZW\nBmtra1RWVsLR0dGoBSaEEEII4TuNAVZmZiYqKiowY8YMlJaWYtasWQgICABQH2CFh4dj5MiRsLKy\nwgsvvID27dsbvdCEEEL4gYv8npWfxiCvTPtpF9g6FkJbiRFLxG+Ug8UNjQEWy7IoKipCTEwMMjIy\nMGXKFPz5558A6ocIY2JicPjwYdja2mLq1Kn4559/0LVrV6MXnBBCCPe4uGlXyCxRZtVJp9dYGKks\n5oACK25ozMFydXWFv78/GIaBt7c3bG1tUVBQAAC4desWvL294ejoCJFIhP79++PKlStGLzQhhBBC\nCJ9pDLACAwNx+vRpsCyLwsJClJeXK9bvateuHW7duoXq6moAwJUrV+Dj42PcEhNCCCGE8JzGIUKJ\nRIKQkBCEhYWBYRgsXboU+/fvh729PUaOHInp06dj8uTJEIlE8Pf3x4ABA0xRbkIIITxA+T38R23E\nDa3mwQoLC0NYWJjO+wghhLRudNPmP2ojbmgcIiSEEEIIIbqhAIsQQgghxMAowCKEEKK3mJj1ihwf\nwk/URtygxZ4JIYTojfJ7+I/aiBvUg0UIIYQQYmAUYBFCCCGEGBgFWIQQQvRG+T38R23EDcrBIoQQ\nojfK7+E/aiNuUA8WIYQQQoiBUYBFCCGEEGJgFGARQgjRG+X38B+1ETcoB4sQQojeKL+H/6iNuEE9\nWIQQQgghBkYBFiGEEEKIgVGARQghRG+U38N/1EbcoBwsQggheqP8Hv6jNuIG9WARQgghhBiYVgFW\nfHw8xo4di5deegnHjx9X2pednY2JEyciLCwMK1asMEYZCSGEEELMisYAq6ioCFu2bMHu3buxdetW\nHDlyRGl/VFQUpk+fjj179kAoFCI7O9tohSWEEMIvlN/Df9RG3NCYg5WUlITAwEBYW1vD2toaK1eu\nVOxjWRbnz59HdHQ0AGDp0qXGKykhhBDeofwe/qM24obGHqzMzExUVFRgxowZmDRpEpKTkxX7CgoK\nYGNjg1WrVmHixIlYv54iZEIIIYQQjQEWy7IoKipCTEwMIiMjsWjRIqV9UqkUr732Gr777jtcu3at\nSY4WIYQQQsjjRmOA5erqCn9/fzAMA29vb9ja2qKgoAAA0KZNG7Rr1w5eXl4QCAQICAjAjRs3jF5o\nQggh/ED5PfxHbcQNjQFWYGAgTp8+DZZlUVhYiPLycjg7OwMAhEIhvLy8kJ6eDgC4evUqOnbsaNwS\nE0II4Y2ZM+dQjg/PURtxQ2OSu0QiQUhICMLCwsAwDJYuXYr9+/fD3t4eI0eOxKJFixAREQGWZdG1\na1eMGDHCFOUmhBBCCOEtrWZyDwsLQ1hYmMp9Pj4+2LVrl0ELRQghhBBizmgmd0IIIXqj/B7+ozbi\nBq1FaEDt29rjbnYp18UghBCTodwe/qM24gb1YBkSa7pLMQAG9ZSY7oJmoHcnF66LQAghhABoxQHW\n4F5tjXp+T1dbo55fk4hJ/dClnSOnZeCbd8f34roIrdqG8CFcF4EQQsxGqw2wpj/XHYwRzuvlZgcA\nmBLia4Sz19v03lDFz2+/0FPlMV28nIx2fS4IBc231lA/D7wQ2KHZYyxEQgOWiDRmLdb9/e37hKsR\nSkL4hPJ7+I/aiButNgeLYRi4OVlDWlRh0POunP6U2n0Durnhbo5hc7AcbCwMer6Wen10N3z9f2kA\ngNdGdUNecQUu3chHurSsReeNfGsQ5n+RrHb/gG7u6OTpgPjEOy26DjEt9zbWXBeBGBnl9/AftRE3\nWm0PFgA809/LoOezs24+2Onq3bRXyc3JqkXXFAr520TD+nhi/LDOGNzbo8XncnVq/kZsjN7Ihgb2\neJTP1rNDGyNfzXT+85SPAc9m7FYghJDWg793bwOw0mNIo6Gp//FF+Eu9Fb8vmdJfp9fPGtcbi6cM\naFEZnvBqJs+KUX3Da/Y1LSRpY9N0I2vC7H4jmf5cd8XPk//TjcOSGBZrwCcvBK3604KQlqutlaGs\nrFSn/2pra7kuNjGSVjtEaAhD/TwhEDDYNu9pCBgGgmbyhGzETd/K/r5uel234bkEDIPP5wzHe5tO\nolpWp3ygmsCmdycX3Mgo1uvamqjqpWsNRA16Ct019KY9roR6RFitIPYmGshze2gYCvinwApvrfhW\np9c8P8gD/w0dZ6QS1aM24obJA6zpL/TC9vgrJrmWrdWjIT1XRyvkFVc2e/wQPw+EPOWDpV+dBgBF\nQCVSM0z3wrBOuJtZjIE9JPD1UT+stOrNgVj85Wm1+7v5OCEtvehhmUWPgrqH1xdbCtG7kwvO/5Pb\nbPnlJAbMexnQzR3n0qRK294Y013pZqvNPTSwd1vczCxBdkF5k32vPtu1pcVs1lvP98C2X64Z9Rq6\n6Ohhj9v3TTNfGsPxsJ4he9AIP9FN+xGxvSdg76nTa9i6IiOV5hFqI26YPMAKGdQeF9JykKJlsNAS\nfbsoP8HU1csR/zTTs/PsAG+002H6hTfH9kZuruYbpYdL8+d8pr+XIsDq4OEAQH1QB9QnlzdnQDd3\nzGQYxPysPpC1s7ZAWUVNs+eRH9fY4F7KOVeaeil2RNSvT1lcVoUrtwuw/bfrGq/bBNM0WPh05mDc\nyS7F5p8uN/vS5t5LU+rm44ShfTzh6+2ED2KSTHJNzgMciq8IIY8pk995rMUivP1CD43HBRpgHisB\nwygFTE92N/7EnL4ahtBUJb03DFDEFprzxtweDmE91UOicj4uAcNgQDd3jecxNUc7MQK1SIiXOKvI\n8wJgY6X8fcDZwQp+nV3wZDd3zHulr9rzMSpy1dq3tddYDl091V39e96zQxvM+68/Anq2VepZDX+p\nN8LH98a00d3hZGep9vWfzhyMWeN6q92vjqGH6Hzc7Qx7QkIIaaU4+Wqv6kN/zst9MKJfO8XvDScK\nHdRDAhcH/Z7G0+VGKr8N//eZLnhnrOr5pzR5LqB9s/s19WapU6fiTbO1ssDHbwzU63yq2Fq1vENz\nzOD2+O/ILorfZ76oefLP7u3rh1flAYavtyPmvtJXKd9L3nv11YIgDPHzwPthfQDU907NeLEXundw\nVnt+Valz/5vQR3NldPTOWPV1tRAJFYGepcWjf3YONpbw7+qGIX4esHw4j9egnhJ8tSBI6fXODlbo\n7+uGp/3bgUs+Ogam1IHV+tEcS/xHbcQNfoydAOjV0UV98MEAa2cE6B306OrZJ73xlIF7u958vgc+\naKaXRRNVvTDamjWu6Y3/2QFNp7AY3rfpzVukYQJQQHkYavywznh2gLfid2160hr3wrEs0LODs1J7\ny6svYBhMG91dt2VxVFTB0VZ9b5EuBA8LJp/aYc07AZqL07At1by9AjXt/XRf5fyOF4d2VASo2nJx\nEGt1nKSNdZOeZAuR6o+MhvPDBfRs8BqKsFq9mTPnUI4Pz1EbcYOTAEskEsDO2gJD/dQPFzX8XGZQ\nf1N6qrsEOyJGYHKIL8YO6aj2tRGv9lN9Tk3jJUbMBw7o2RY9OjjjeQ2zkasrwivPPKH4uZ2b6kA0\n+ElvTHi6s1ZnfT6wIwb2kKidyHTGi73g38UVYzSUVx+LJvXXmEcGKJfa10f/pxdbEpxqIhQy2PrB\n05jzcn3w7OZkjS3vD8PIRgGsuiI0fGJUm3wpH4k9tjfo3fLv4tbsslCNA7UdESPwycxAjdcBACux\nCNPHKA/njw1U/nc3vK8nPvvfUMUKB0D9lwk5I771hBDCa5xM0yBgGGycPQQMw6CwtArX7xYCeDTr\ns6ZhvaCHwySFpVU4kZrVZL+6qQRM8WVa0zU6ezrC0c4SxWXVOp3X1dEa2xcEQVZbp3ZJmFee6aJy\nuzryZXg+/Pos7uaUwrFBDpCXmy3CX/LT6jwDu0sQd+wWpo7SbvmgJ7wc8YSXI745mKZyv6qbsj5T\nBMhp0QnXIo17dazFoia5dI2DvMi3BuFGZrHKXltNxW18rsbfG2ytRHh7bE/8mZKJPk+4IOFMuoYz\nas+hUc+ftVikyCl79dmuTYaye3VywR/nMwx2fUIIMRdaBVjx8fHYvn07RCIRZs+ejeHDhzc5Zt26\ndbh48SK+/Va7OUDkN4n3w/oogpJeHZ0RPr43ung7abXkzGujusFCKMCRFPUf4L06OSPpSjYG9ZRo\njH64+rKtNGLUTDTAMIye6+01X/HZoX64crcQAd3d8cMf/9a/QsVL1E1g6uxg1SRnyNHOEvbW+g3D\nKa5toO6PlvZgTfmPL85cy0FaepHi6UsBw6COZbVOIm9cAomzjdpkfl0waNrztem9oWAYBr06uuBG\npnHmQ1NF1coJQiF1YbV2NMcS/1EbcUNjgFVUVIQtW7bg559/xoMHD7Bp06YmAdbNmzdx7tw5WFjo\nvm4ewzx6+J5hGPh31W1yzrARnSEtqkDJg2rczSltsmjwwO4SdPRwgJuTNY6cM/436ZY+tRUWpGqI\nz7ja2IsxPqiLxiknFkz01/qc62YF6hywNg6E5MNnXVo4M33j+Gr+f7WvBwA83bcderRvg69+vY4p\nIb7wcrfD3mM3cPCUDj1DesQZA3u2xemr2XixmeHwxiY83VnpfdS0iLY2pj/XXamXLvLtQVi49ZR2\nL6YcrFaPbtr8R23EDY3jLklJSQgMDIS1tTVcXV2xcuXKJsesWbMGc+fONWzJtPxgthAJ8X5YHwQ/\nVZ9Y3aPR02QMw0DSxgYChlHMrD45RM1QlqkSRpqpm6uj7pOEalojsaV9c84OYkjaWOs0TCdgmBb3\nHFmIBPh87nAsUJNTp62G5RjWxwPdHiaFvxeq3RAoALi3scGiyf3h1WSaAtWN2cnTQbkMWlyji1f9\n0Lb8Gv97xR+vjeqG0c09mcooF2HUIOVj27e11zkJXk4+YW1gbw+lhz4kbWxgqSbZnRBCSD2NPViZ\nmZmoqKjAjBkzUFpailmzZiEg4NGTUvv370dAQAA8PFq+4G9LDOwhgZWlEN2amVHd2cEK2xcEgWEY\n/JZ8BwUlVUr7DRNeaY4MDf2lfv27gaitM15XwdoZgzkbPtVmXjBNGoYCdg2GLRv37rwf1gcONtoN\na2qaIb3vE65YOnUAvjhwBblFlVoF75ODfdH3CVfFBLl2NpYY1kfzrNDNtbyAYTB1VDdEfJGscv+C\nif5Ys+uC4vfO7RxwM7MEwKP51vS+OHgw0SkhhHBE49dQlmVRVFSEmJgYREZGYtGiRYp9xcXFOHDg\nAKZOnQqWZTU/pacD5Zuf5puTgGHg38UN1irWBGxI3puxePIAvDmmh8EDBy7WXhMJBRoCkZYVyhC9\nUaa2+X9DFT0wzo5WEKnIBWr8rvTu5KL1vGlt7OunOmj49FxDDMOgo4eDotdPm3dPbCnEgG7uOs08\nzwBo+zCXq6OHg8pjmvt32bhdF08eAP+HAZ66uumE4qtWj+ZY4j9qI25o7MFydXWFv78/GIaBt7c3\nbG1tUVBQAGdnZ5w6dQr5+fmYOHEiqqqqcO/ePURFRSEiIqLZc7q5ab6JObvYAT/Uf7O2shJp9Rpd\nuLnZo2snV3TyaYOFMYn113S21fk6jY93zH2gdp9c40WjHRwe9RQYup6Nz9/cdRpuk7jbw03PSVF1\nJb+u/H2xsrLQ+30YO6wzUv6WwserDTbMDUJWbhm6NujVtLGxVJzbMf/RuoiNr/fJ7KGYt+mkyn0A\nEPqsL2xsLDG0bzu0aWYSXHmSt751UvcaC5EANbI6eLdzgoujNVY5WKFzOyfYqhgurmkQ3jU+n5OT\ncqK9m5s9Fr0+ENfvFKBvVzf1gfXD7Q3fT1XauqsO+kjrQfk9/EdtxA2NAVZgYCAWLVqEN998E0VF\nRSgvL4ezc32eU0hICEJCQgDUDyUuXLhQY3AFQKv1+4BHCzRXVsq0fo2uJA0mXSwsfAArHVJL3Nzs\nm5SrtloGoL4HTl2Z6xoN55WUVCh+NkY9S0oqMfeVvki7W4jfku+qvI68LkunDsC/GcUQ1tUZ7T2X\nWzSpP6zEQsV15O9LRWWN3td+Y2wv5OaWIi+vDADQxlqE3NxSRc9iRUW14tzFxerfd5cG84OpK0tA\nd3fIqmqQm6t+TcdBPSSIO34LPXycdK6Tqr8vuai3A5BTUI666vp/Gx6OVigvq0R5WdMFzfMbLLDd\n+HxFRY/2rXj9ScV+L2drxXuo0sM3tLy8utl6udiK8NLwTurPQwghrZTGAEsikSAkJARhYWFgGAZL\nly7F/v37YW9vj5EjR5qijGY1WWEnTwdM/Y+v3onFxtKzgzN6dnBWBFjqdPRwUDvUZGjqpn1oTUYP\nao/BvTwUQ4qG0sZerPU5rS2bDh+HPt0ZB0/dRXuJPVa8/iRKK2rgI9Ghh03Lf5MMw+C5gA7an1cP\njaeR8fX1xbx588CyLNzc3LB27VpYWFggPj4esbGxEAqFmDBhAkJDQyGTyRAREYGsrCwIhUJERkbC\ny6vpdBOEEKIrrebBCgsLQ1hYWLPHtGvXDrGxsQYplDljGEblkjMNebnZ4toD3SYaJeaJYRiDB1e6\ncrQTY05YH7R1eTQcOHpQe4x++MShToEVz6iaRiYhIQGTJ09GcHAwoqOjERcXh7FjxyImJgZxcXEQ\niUQIDQ1FcHAwjh49CkdHR3z66adITEzEunXrEB0dzXW1zArNscR/1Ebc4PWz1vIkW62eZjIAUyVy\nTxvdXZFIbEzyJ9A6ejy6gYYFPYGwoCfUvYRT8nI9rSFA1YeqPG9NDyS4OIibXYbGnPTq5KLXFCDq\nDH643mAXb9W9kC8EdsB/dVxZQB+qppE5c+YMgoLqJ74NCgpCUlISUlNT4efnB1tbW4jFYvTr1w/n\nz59HcnKyoid+8ODBSElJMXqZWxta547/qI24wclSOdqa9lx3nL6Wg2F9uJ0CwtCcHawQ/pIfpkUd\nVWwLftIbTnaG7emY+h9f/HdkF6UnDP8z0Meg1zCkgT0keKq7u1EDXV1Ore2afY+jV4O74pkB3vB0\nUT0b/YtDTZN3pWoamcrKSsWkxy4uLpBKpcjPz1fkjgKAs7MzcnNzkZeXp9jOMAwEAgFkMhlEIl5/\nNBJCzACvP0XsrC1ULr9hLFymeum6jqA2GIYxyDxSpmSs4IrmYzIsoUCAdq6mecq0OfJpZLZs2YLM\nzExMmTJFaVoKdVNUqNteV1dnlHISQh4/vA6wHh9mlMVv9hq+1xR0mTv5NDICgUAxjYxIJEJ1dTUs\nLS2Rk5MDiUQCd3d35ObmKl6Xk5MDf39/uLu7Iy8vD76+vpDJ6p8A1qb3yhjTqRiDKcr54YcfAgCW\nL1+u9zl0LafYUgi0kjRWO3sro7RTw3Maoo2MxVz+LemDAqyGKM4hxKyomkZmyJAhSEhIwAsvvIBD\nhw5h6NCh8PPzw5IlS1BWVgaGYXDhwgUsXrwYpaWlSEhIQGBgII4ePYqBAwdqdV1jT2FiCM1N82FI\n8twefa+lTzmrqmv1uhYflZVWGrydGr+nLW0jYzHV32hL6RsEUoBFHgtzXu6LH4/cwDP9HiXQczHr\nPjGsxtPILFu2DL169cL8+fOxZ88eeHp6Yty4cRAKhZg7dy6mTZsGgUCA8PBw2NnZYfTo0UhMTMTE\niRMhFosRFRXFdZUIIa0EBVgNUAdW69WzgzNWTn+K62IQI1A1jcyOHTuaHBccHIzg4GClbQKBAJGR\nkUYtHyHk8cTraRpMZdRAH9haieDczJInxiRUsU4eIYSYA1rnjv+ojbhBPVgAJgQ9gQkczA21YKI/\nTl66j96dnDUfTAxOPpP8uGG0lAsh+qL5lfiP2ogbFGBxyNenDXx9+LWkzuPE1soC2xcEmWyCWUII\nIY8PGiIkjzUKrgghhBgDBViEEEL0Rvk9/EdtxA0aIiSEEKK3lub33MvIxJ/Hz+r0mpKSIsChRZd9\nrFAOFjcowCKEEMKZuF+O4NA1S51eI7DtTcMvhPcowCKEEMIZgUAAkaU118UgxODoSwAhhBC9UX4P\n/1EbcUOrHqz4+Hhs374dIpEIs2fPxvDhwxX7Tp06hejoaAiFQnTs2BGrVq0yWmEJIYTwC+X38B+1\nETc09mAVFRVhy5Yt2L17N7Zu3YojR44o7V++fDk2bdqEXbt2oaysDCdOnDBaYQkhhBBCzIHGHqyk\npCQEBgbC2toa1tbWWLlypdL+uLg42NnZAQCcnZ1RVFRknJISQgghhJgJjT1YmZmZqKiowIwZMzBp\n0iQkJycr7ZcHV1KpFElJSUrDh4QQQlo3yu/hP2ojbmjswWJZFkVFRYiJiUFGRgamTJmCP//8U+mY\n/Px8zJgxAytWrICjo6PRCksIIYRfKL+H/6iNuKExwHJ1dYW/vz8YhoG3tzdsbW1RUFAAZ+f6BYrL\nysrw5ptvYu7cuQgICNDqom5u9i0rNY9QXfintdQDaF11IYSQx4nGIcLAwECcPn0aLMuisLAQ5eXl\niuAKAKKiovD6668jMDDQqAUlhLQeCxcuxI4dO5CRkcF1UQghxCg09mBJJBKEhIQgLCwMDMNg6dKl\n2L9/P+zt7TFkyBDEx8cjPT0de/bsAcMweP755zFhwgRTlJ0QYqYiIyORmZmJo0ePIjExEf3790dY\nWBilGJgheW4PDUPxF7URN7SaByssLAxhYWEq9126dMmgBSKEtH6HDh1CcnIyGIZBaGgofH19sWrV\nKqxdu5brohEd0U2b/6iNuEFL5RBCTK62thbz5s1DVVUV6urq4OrqitmzZ3NdLEIIMRhaKocQYnLH\njh0Dy7IQCAT45JNPAABeXl4cl4oQQgyHAixCiMlZWlqCZVnFz8R80RxL/EdtxA2TDhFGRkYiNTUV\nDMNg0aJF6N27tykvr1FaWhrCw8Px2muv4dVXX0V2djbmzZsHlmXh5uaGtWvXwsLCAvHx8YiNjYVQ\nKMSECRMQGhoKmUyGiIgIZGVlQSgUIjIyEl5eXkhLS8OKFSsgEAjg6+uL5cuXG70ea9euRUpKCmpr\na/HWW2+hd+/eZlmPyspKREREID8/H9XV1ZgxYwa6detmlnUBgKqqKowZMwazZs3CoEGDzLIeZ86c\nwXvvvYcuXbqAZVn4+vrijTfe0LkuN27cwHPPPYchQ4bgtdde46xNSMtRfg//URtxw2Q9WGfPnsXd\nu3exe/dufPzxx7xbFLqiogJr1qxRmm5i48aNmDx5Mr777jv4+PggLi4OFRUViImJwc6dOxEbG4ud\nO3eipKQEv/76KxwdHbFr1y688847WLduHQBg9erVWLp0KXbt2oWSkhKcPHnSqPU4ffo0bty4gd27\nd+PLL7/E6tWrsXHjRkyaNMms6gEAR48eRe/evfHtt98iOjoakZGRZlsXAIiJiYGTkxMA8/zbknvq\nqacQGxuLb7/9FkuWLNGrLq+//jq6du2KU6dO4euvv+asLoQQYiwmC7CSk5MxcuRIAEDnzp1RUlKC\nBw8emOryGonFYmzduhWurq6KbWfOnEFQUBAAICgoCElJSUhNTYWfnx9sbW0hFovRr18/nD9/Xql+\ngwcPxoULF1BTU4OMjAz07NkTADBixAgkJSUZtR5PPvkkNm7cCABwcHBAeXk5zp49ixEjRphVPQBg\n9OjRmD59OgAgKysLHh4eZluXW7du4fbt2xg+fDhYlsXZs2fN7m9LTj60J6fPv5Nbt27h888/R21t\nLZYvX85ZXQghxFhMFmDl5eUpTVDapk0b5OXlmeryGgkEgia5IBUVFbCwsAAAuLi4QCqVIj8/X6ke\nzs7OyM3NVaofwzBgGAZ5eXmKHouGxxq7HtbW1gCAffv24emnnzbLejT0yiuvYP78+Vi4cKHZ1mXt\n2rWIiIhQ/G6u9QCAmzdvYubMmXj11VeRlJSEyspKnetSUVGBpKQk1NTU4MiRI5z+fZGWofwe/qM2\n4gZn0zQ0/hbMd+rK29x2hmE4q+cff/yBuLg4bN++HcHBwUrlUoWv9QCA3bt3Iy0tDR988IFSOcyl\nLj///DOefPJJeHp6qi2Xrtu5apP27dvj3XffxahRo3Dv3j1MmTIFMplMqWyqNN7evXt3FBYWQiaT\nITc31+w+D8gjlN/Df9RG3DBZD5a7u7tSj5VUKoWbm5upLq8XW1tbVFdXAwBycnIgkUjg7u6u9O26\n4XZ5/WQymSLht6ioSOlYd3d3o5f75MmT2LZtG7766ivY2dmZbT2uXLmC+/fvAwC6deuGuro6s6zL\n8ePHkZCQgJdffhn79u1DTEwMbGxszK4eQP3KDqNGjQIAeHt7w9XVFSUlJTrXxdbWFrdv34alpSU8\nPT05qQshhBiTyQKswMBAHDp0CABw9epVSCQS2NjYmOryegkICFCU+dChQxg6dCj8/Pxw5coVlJWV\n4cGDB7hw4QL69++PwMBAJCQkAKhPzh44cCCEQiE6deqElJQUAMDhw4cxdOhQo5a5rKwMn3zyCb74\n4gvY29ubbT0A4Ny5c/j6668B1A8xl5eXIyAgQFE+c6lLdHQ09u7dix9//BGhoaGYNWuWWdYDAH75\n5Rds3rwZAJCfn4/8/HyMHz9e57qkpqYiPT0dgwcPxunTpzmpCyGEGBPDmrBvfv369Thz5gyEQiGW\nLVsGX19fU11ao9TUVCxZsgQFBQUQCoVwdHTE9u3bERERgerqanh6eiIyMhJCoRCHDx/GV199BYFA\ngMmTJ+O5555DXV0dFi9ejLt370IsFiMqKgoSiQQ3b97EsmXLwLIs+vTpgwULFhi1Hnv27MHmzZvR\noRGwn5wAACAASURBVEMHxVDSmjVrsHjxYrOqB1A/rcGiRYuQnZ2NqqoqhIeHo2fPnpg/f77Z1UVu\n8+bN8PLywpAhQ8yyHg8ePMDcuXNRXFwMlmUxa9YsdOvWDQsWLNCpLomJiYq1TX///Xe89dZbnLWJ\nPnJzS7kugkZubvYmKWdL17n74acD+P0fe0MWSW+/rn8RADBmzs8mu+bITkWYGDbeoOds3PZ8XYvQ\nVH+jLeXmpt/fp0kDLEIIAYDc3FwcPHgQDMNg1KhRSk/vmgNzuSmYQzkpwDJ+gMVX5lROfdBahIQQ\nk5Mv6lxdXY2UlBRER0dzXCJCCDEsCrAIISYnX38QAGJjYzksCSGEGAcFWIQQk9uwYQMYhkFdXR3S\n09MxZcoUrotE9MTX/B7yCLURNyjAIoSY3IQJE8AwDAQCAe+nayHNo5s2/1EbcYMCLEKIyS1atAg2\nNjaora1FVVUV3N3dwTCMIjeLEELMHQVYhBCTGzx4MN5++20A9Qtfv/feexyXiBBCDIsCLEKIyd28\neVOR3H737l2OS0NagvJ7+I/aiBsUYBFCTC4yMhJpaWlgWRavvvoq18UhLUA3bf6jNuKGyZbKIYQQ\nuU8//RQ7d+5E27ZtsWbNGq6LQwghBkc9WIQQk5MvQu7q6gqxWMx1cQghxOAowCKEmJxYLMbRo0ch\nlUrh5OTEdXFIC1B+D/9RG3GDAixCiMn16tULr7zyCoD63ixivuimzX/URtygAIsQYlIsy+KHH35A\n//79YWNjAwAIDQ3luFSEEGJYlOROCDGpqKgovPrqq0hISICPjw98fHy4LhIhhBgc9WARQkzuqaee\nQq9evfDUU09xXRTSQpTfw3/URtwweYAlk9WisLDc1Jc1ijZtbKguPNNa6gG0rrq4udkrfr5//z6S\nk5ORnZ2N5ORkAEBAQABXRSMtRDdt/qM24obJAyyRSGjqSxoN1YV/Wks9gNZVl4aCgoKQnZ2t+D/D\nMFwXiRBCDI6GCAkhJjVu3Diui0AIIUZHSe6EEEL0FhOzXpHjQ/iJ2ogb1INFCCFEb5Tfw3/URtyg\nHixCCCGEEAOjAIsQQgghxMC0CrDS0tLw7LPP4vvvv2+yLykpCRMmTMArr7yCmJgYgxeQEEIIf1F+\nD/9RG3FDYw5WRUUF1qxZg8DAQJX7V61ahR07dsDd3R2TJk1CSEgIOnfubPCCEkII4R/K7+E/aiNu\naOzBEovF2Lp1K1xdXZvsu3fvHpycnCCRSMAwDIYPH45Tp04ZpaCEEEIIIeZCY4AlEAhgaWmpcl9e\nXh6cnZ0Vvzs7O0MqlRqudIQQQgghZsigSe4syxrydIQQQniO8nv4j9qIGy2aB8vd3R25ubmK33Ny\ncuDu7t7sazp06IA7d+605LK80nCNNXPXWurSWuoBtK66kNaJ8nv4j9qIGy0KsNq1a4cHDx4gKysL\n7u7uOHbsGNatW6fxdbm5pS25LG+4udlTXXimtdQDaH11MZaqqiqMGTMGs2bNwqBBgzBv3jywLAs3\nNzesXbsWFhYWiI+PR2xsLIRCISZMmIDQ0FDIZDJEREQgKysLQqEQkZGR8PLyMlo5CSGPF40BVmpq\nKpYsWYKCggIIhULs3r0bL730Ery8vDBy5EgsX74cc+bUR8djxoxB+/btjV5oQgiRi4mJgZOTEwBg\n48aNmDx5MoKDgxEdHY24uDiMHTsWMTExiIuLg0gkQmhoKIKDg3H06FE4Ojri008/RWJiItatW4fo\n6GiOa0MIaS00Blh9+vTBL7/8onb/gAEDsHv3boMWihBCtHHr1i3cvn0bw4cPB8uyOHv2LFauXAkA\nCAoKwo4dO9ChQwf4+fnB1tYWANCvXz+cP38eycnJePHFFwEAgwcPxqJFizirhzmT5/bQMBR/URtx\ng1drER48+Ctqa2sxZsxYrotCCDEDa9euxbJly/DTTz8BqJ+3z8LCAgDg4uICqVSK/Pz8Jk875+bm\nKj0FzTAMBAIBZDIZRCJefSzyHt20+Y/aiBtmuVTO6tUfcl0EQgjHfv75Zzz55JPw9PRUuV/dU83q\nttfV1RmsbIQQwruvaomJJ3H5ciqKi4uwatUn+OefNMTGfg0bG2sMH/4MRCIRTp9OxtGjf8DJyQk/\n/vg9WBZ47rnnMXz4CMV5UlLONdj3AoYPD8Lnn3+GrKxMMAyDpUtXIinpJH7/PQEMI8C4caHw9GyH\njz9eDolEgjFjXsR33+1Ehw4dER7+PofvCCFElePHjyMjIwOHDx9GTk4OLCwsYGNjg+rqalhaWiIn\nJwcSiUTl087+/v5wd3dHXl4efH19IZPJAEDr3itzebrTXMr5OLOztzJKO5lL25tLOfXBuwDL29sb\nM2e+hy+//BwXL6bgxx+/x9KlH8He3h7vvz8L0dFb4OXljREjRiI5ORGLFi2HpaUYS5cuUAqwqqur\nlfZ17eqL/Pw8fPRRFM6fP4vCwgLs2fMDtmz5EpWVlVi4cC4WLFgCAFi69CNcuHAebdq0oeCKEJ5q\nmJC+efNmeHl5ISUlBQkJCXjhhRdw6NAhDB06FH5+fliyZAnKysrAMAwuXLiAxYsXo7S0FAkJCQgM\nDMTRo0cxcOBAra9tDk93muopVMrvaZmy0kqDt1PjtudrG5nLk9L6BoG8C7A8Pesfk3Zzc0NhYQHy\n8nLx2Wf1fxy1tbVgWVbRxW9hYYGtW7fAysoKNTUypfOIRCKlffn5+XBzq5+jq3//J5WOrT+mBgAD\niUSi2C6RtDVWNQkhRjB79mzMnz8fe/bsgaenJ8aNGwehUIi5c+di2rRpEAgECA8Ph52dHUaPHo3E\nxERMnDgRYrEYUVFRXBffLPHtpk2aojbiBu8CLKk0BwCQm5uL9u07QiLxwNy5ERCLxcjIuAeGYRQB\n1vbtX+Czz7ahqKgQK1YsVjpPw30ffrgE7u7uyMrKBAAkJ/8FLy8fMAwDACgvfwCx2AqAcm6GfD8h\nhN/effddxc87duxosj84OBjBwcFK2wQCASIjI41eNkLI44l3Ada9e+lYv34N8vPzMH3627CwsMDK\nlUsgElnA17cbJk6cAoDF/v370KNHb6xe/SF8fNqDYRhcuXIZvXr1BgClfQAglUrh6dkOy5YtBMuy\nWLbsI7z88kQsX74QtbV1mDp12sMSUFBFCCHENK6npeHwkaNaHy8UCDHi6WHUAWAGGNbECwh26NAB\nZ89eNuUljcZcxo+10Vrq0lrqAbS+urQm5tAu5pKD9cNPB/D7P/z4+/h1ff28aGPm/Gyya8qqK3Q6\nvir3CnZvmt9sgEU5WIbVanKwCCGEmA++3bTNjcjSWqfjay3EOl+D2ogbZjkPFiGEkP9v7/6Dorrv\nfoG/zy5CZFmR1WUjoNaHPjKjwDyI3oZg/JHyYJvRtnYUGRqt0asd48V6sakEInqtFDRRbjKEaX2K\nk2tMshoZKj6TkYxJY1OBSDWSQn9kFIsaCsuigAv+CPC9fxBWUGCX9SznnPX9+sfds7/eX77nsB/P\n+XAOEakZCywiIiIimbHAIiIijxUVHXD2+JA6cY6UwR4sIiLyGPt71I9zpAzuwSIiIiKSGQssIiIi\nIpmxwCIiIo+xv0f9OEfKYA8WERF5jP096sc5UoZbBVZeXh5qamogSRKysrIQExPjfOydd97ByZMn\nodfrER0djZdfftlrYYmIiIi0wGWBVV1djYaGBlitVly+fBnZ2dmwWq0AAIfDgeLiYnz00UeQJAnr\n16/HF198gdjYWK8HJyIiIlIrlz1YlZWVSEpKAgBERkaio6MDnZ2dAAB/f38EBATA4XCgu7sbd+7c\nQXBwsHcTExGRarC/R/04R8pwuQfLbrcjOjraeT8kJAR2ux0GgwH+/v5IT09HUlISnnjiCfzgBz/A\n9OnTvRqYiIjUg/096sc5Usao/4pQCOG87XA4UFRUhA8//BAfffQRLly4gC+//FLWgERERERa43IP\nVmhoKOx2u/O+zWaD2WwGANTX12Pq1KnOw4Lx8fGora3FzJkzR3xPs9n4KJlVhWNRH18ZB+BbYyEi\nepy4LLASExNRWFiIlJQU1NXVwWKxIDAwEAAQHh6O+vp63Lt3D/7+/qitrcWCBQtcfmhLy61HT64C\nZrORY1EZXxkH4HtjId/U39vDw1DqxTlShssCKy4uDrNnz0Zqair0ej1ycnJQWloKo9GIpKQkrF+/\nHqtXr4afnx/i4uIwd+7cschNREQqwC9t9eMcKcOt82BlZAyenKioKOftlJQUpKSkyJuKiIiISMN4\nqRwiIiIimbHAIiIij/EcS+rHOVIGr0VIREQeY3+P+nGOlME9WEREREQyY4FFREREJDMWWERE5DH2\n96gf50gZivVgxcf3Xd/w/PlapSIQEdEjYn+P+nGOlME9WEREREQyY4FFREREJDMWWERE5DH296gf\n50gZPA8WERF5jP096sc5Ugb3YBERERHJjAUWERERkcx4iJCIiDzW39vz4osZuHGjFVt3vgHDRIvb\nr+/u6QUmzPJWPMLgOaKxwwKLiIg8NvBL++uvu3HviQj4GSIVTEQPYmGlDB4iJCIiIpKZW3uw8vLy\nUFNTA0mSkJWVhZiYGOdjTU1NyMjIQHd3N2bNmoVdu3Z5KysRERGRJrjcg1VdXY2GhgZYrVbs2bMH\nubm5gx7Pz8/H+vXrcezYMej1ejQ1NXktLBERqQvPsaR+nCNluNyDVVlZiaSkJABAZGQkOjo60NnZ\nCYPBACEEzp8/j4KCAgDAjh07vJuWiIhUhf096sc5UobLPVh2ux0mk8l5PyQkBHa7HQBw48YNBAYG\nIjc3F2lpaThwgBUyERER0aib3IUQg27bbDasXbsWR44cwV//+lecOXNG1oBEREREWuPyEGFoaKhz\njxUA2Gw2mM1mAH17s8LDwxEREQEASEhIwKVLl7Bw4cIR39NsNkKnk5y3tUzr+QfylbH4yjgA3xoL\n+SaeY0n9OEfKcFlgJSYmorCwECkpKairq4PFYkFgYCAAQK/XIyIiAlevXsW0adNQV1eHpUuXuvzQ\nlpZb6O0VzttaZTYbNZ1/IF8Zi6+MA/C9sZBv4pe2+nGOlOGywIqLi8Ps2bORmpoKvV6PnJwclJaW\nwmg0IikpCVlZWcjMzIQQAjNnzsSzzz47FrmJiIiIVMut82BlZAyufqOiopy3p02bhnfffVfeVERE\nREQaxjO5ExGRx3iOJfXjHCmD1yIkIiKPsb9H/ThHyuAeLCIiIiKZscAiIiIikhkLLCIi8hj7e9SP\nc6QM9mAREZHH2N+jfpwjZXAPFhEREZHMFC+w4uOjER8frXQMIiIiItkoXmAREZF2sb9H/ThHymAP\nFhEReYz9PerHOVIG92ARERERyYwFFhEREZHMWGAREZHH2N+jfpwjZbAHi4g0bd++fbhw4QJ6enqw\nceNGxMTE4KWXXoIQAmazGfv27cO4ceNQVlaGw4cPQ6/XY+XKlVixYgW6u7uRmZmJxsZG6PV65OXl\nISIiQukhaQr7e8ZeR0c7JEka9nF//150dNxy3n/hhU0YP378WESjAVhgEZFmffbZZ7h06RKsViva\n2tqwfPlyPPXUU3j++eexZMkSFBQUoKSkBD/84Q9RVFSEkpIS+Pn5YcWKFUhOTsbHH3+M4OBgvPba\nazh79iz279+PgoICpYdFNCx98L9h0+73RnyOpJMgeoXzflhQJ177P7/wdjR6AAssItKsefPmITY2\nFgAwYcIEdHV1obq6Grt37wYALF68GIcOHcK3vvUtxMbGwmAwAADmzJmD8+fPo7KyEj/60Y8AAE8/\n/TSysrKUGQiRm/wCguBnjhrVa8b7XfFSGhoJe7CISLN0Op3z0Mfx48exaNEi3L59G+PGjQMATJo0\nCTabDa2trTCZTM7XmUwmtLS0wG63O5dLkgSdTofu7u6xH4iGsb9H/aYEtnOOFODWHqy8vDzU1NRA\nkiRkZWUhJibmoefs378fFy9exNtvvy17SCKikZw+fRolJSUoLi5GcnKyc7kQYsjnD7e8t7fXK/l8\nGXuw1O9fXcH41S/WKx3jseOywKqurkZDQwOsVisuX76M7OxsWK3WQc+5fPky/vznPzv/10hENFY+\n/fRTHDx4EMXFxQgKCoLBYMC9e/fg7++P5uZmWCwWhIaGoqWlxfma5uZmxMXFITQ0FHa7HVFRUc49\nV35+rv/faTYbvTYeOY11zp6eTkgYvvmalBHgP06166xac8nB5W+SyspKJCUlAQAiIyPR0dGBzs5O\nZy8DAOzduxfbtm3DG2+84b2kREQPcDgcePXVV/HWW2/BaOz7RZ2QkIDy8nIsW7YM5eXleOaZZxAb\nG4tXXnkFDocDkiTh888/R3Z2Nm7duoVTp04hMTERH3/8Mb7zne+49bktLbdcP0lhZrNxzHPa7Q4I\nDL13kJRz997XqlxnlVhHPeFpEeiywLLb7YiOvn8x5pCQENjtdmeBVVpaioSEBEyZMsWjAEREnvrg\ngw/Q1taGrVu3QggBSZKwd+9eZGdn4+jRowgLC8Py5cuh1+uxbds2rFu3DjqdDunp6QgKCsJzzz2H\ns2fPIi0tDQEBAcjPz1d6SJrT39vDQ4Xq1d+DxTkaW6P+K8KBvQvt7e04ceIEDh06hMbGxmH7Gh5k\nNhuh00kPLdMireYeiq+MxVfGAfjWWLwhJSUFKSkpDy0/dOjQQ8uSk5MH9WcBfU3yeXl5Xsv3OOCX\ntvqxB0sZLgus/h6FfjabDWazGQBQVVWF1tZWpKWl4e7du7h27Rry8/ORmZk54nu2tNxCb694aJnW\naGX3pjt8ZSy+Mg7A98ZCRPQ4cXmahsTERJSXlwMA6urqYLFYEBgYCABYsmQJTp48CavVisLCQsya\nNctlcUVERETk61wWWHFxcZg9ezZSU1Px61//Gjk5OSgtLcXp06fHIh8REakYz4OlfjwPljLc6sHK\nyBh8jD0q6uGzyIaHh+Pw4cPypCIiIk1gD5b6sQdLGao6k3t8fDTi46NdP5GIiIhIxVRVYBERERH5\nAhZYRETkMfZgqR97sJQx6vNgERER9WMPlvqxB0sZ3INFREREJDMWWEREREQyY4FFREQeYw+W+rEH\nSxnswSIiIo+xB0v92IOlDO7BIiIiIpIZCywiIiIimbHAIiIij7EHS/3Yg6UM9mAREZHH2IOlfuzB\nUgb3YBERERHJTJUFFi/6TERERFqmygKLiIi0gT1Y6sceLGWwB4uIiDzGHiz1Yw+WMtwqsPLy8lBT\nUwNJkpCVlYWYmBjnY1VVVSgoKIBer8eMGTOQm5vrtbBEREREWuDyEGF1dTUaGhpgtVqxZ8+ehwqo\nnTt34o033sC7774Lh8OBP/7xj14LS0RERKQFLgusyspKJCUlAQAiIyPR0dGBzs5O5+MlJSWwWCwA\nAJPJhLa2Ni9FJSIitWEPlvqxB0sZLg8R2u12REff/4u+kJAQ2O12GAwGAEBQUBAAwGazoaKiAlu3\nbvVSVCIiGkuF/3UE7V3dLp5lAgDkvv4W7t69A71/sPeD0aiwB0sZo25yF0I8tKy1tRWbNm3Crl27\nEBzMjYuIyBdcae7ETb9/H9VrAoxeCkOkMS4LrNDQUNjtdud9m80Gs9nsvO9wOLBhwwZs27YNCQkJ\nbn2o2WyETie5tUzttJDRXb4yFl8ZB+BbYyEiepy4LLASExNRWFiIlJQU1NXVwWKxIDAw0Pl4fn4+\nXnjhBSQmJrr9oS0tt9DbK9xapmZms1H1Gd3lK2PxlXEAvjcW8k1zJ1wEAPy54z8UTkLD6e/B4ik1\nxpbLAisuLg6zZ89Gamoq9Ho9cnJyUFpaCqPRiPnz56OsrAxXr17FsWPHIEkSli1bhpUrV8oWsP+M\n7ufP18r2nkREJA8WVurHHixluNWDlZExuOqNiopy3v7iiy/kTURERESkcbxUDhEREZHMWGAREZHH\n5k646OzDInXiebCUwWsREhGRx9iDpX7swVIG92ARERERyYwFFhEREZHMNHOIkKdrICJSH54HS/36\ne7AMIdPdfk3CvDjM/Pa/eTGV79NMgUVEROrDwkr9nHPUMYoXic9ZYD0iHiIkIiIikhkLLCIiIiKZ\nscAiIiKP8TxY6sc5UoYme7DY8E5EpA7swVI/zpEyuAeLiIiISGYssIiIiIhkpukCKz4+2nm4kIiI\nxh77e9SPc6QMTfZgERGROrC/R/04R8rQ9B6sgbg3i4iIiNTCrT1YeXl5qKmpgSRJyMrKQkxMjPOx\niooKFBQUQK/XY8GCBXjxxRe9FtZd/CtDIiIiUpLLPVjV1dVoaGiA1WrFnj17kJubO+jx3NxcFBYW\n4r333sPZs2dx+fJlr4UlIiJ1YX+P+nGOlOFyD1ZlZSWSkpIAAJGRkejo6EBnZycMBgOuXbuGiRMn\nwmKxAAAWLlyIqqoqREZGeje1mwbuyeJeLSIi+bG/R/04R8pwWWDZ7XZER9/vbQoJCYHdbofBYIDd\nbofJZHI+ZjKZcO3aNe8klclQhRaLLyLydYX/dRif/f3GqF4jjTfDL8hLgYh83Kj/ilAI4dFj/a5f\n/xPi4w1obPzToOVyLRvda7765t6fhljWJywsfIRl7QDEQ88b+TWDl6mFTgf09hqUjuG2gT/rgdQ4\njqHWC3fmfqSxjG49ffR17VE/7+rVR/p4koGk98c4c6zSMYgeGy4LrNDQUNjtdud9m80Gs9nsfKyl\npcX5WHNzM0JDQ0d8v4iIiG/+nTrEY/Is8+Z7y51BDtevX//m/SOGvH3/8+VfNta57t8e/mep091v\nLfTmz8bV+IfKOtp1YOBYhnvP0S7zxFh/HmlHf28PD0OpF+dIGS4LrMTERBQWFiIlJQV1dXWwWCwI\nDAwEAISHh6OzsxONjY0IDQ3FJ598gv3794/4fv/8J9DSckuW8Eozm40qGUvwN//eGnQ7Pj5x0LOq\nq2udy+4fDu17zeCxDHwfuFg2+lzuv/doP2+oORl9hv5DxtXVtS5yeXfu1bN+ycGodADyEn5pqx/n\nSBkuC6y4uDjMnj0bqamp0Ov1yMnJQWlpKYxGI5KSkrBz505kZGQAAJYuXYrp06d7PTS5Z6ieMvaZ\nucafERERPSq3erD6C6h+UVFRzttz586F1WqVNxURERGRhvFSOURE5DH296ifJ3PU3n4T16+7f1YA\nSZIQFhYOSZJGnc9XscAiIiKPsbBSP0/mqOKf/jj7+mm3n3+37Z/4f6/9bwQHTxz1Z/kqFlhEREQ0\nSGCIe38l3U8v7ngpiXb5zMWeiYiIiNSCe7CI6LE20sXs1erKlXq8lP8WDMZJwz5Hp9eht6fXeb9X\n8se4SSOfp9AT7MFSP86RMlhgEdFja+DF7C9fvozs7GxN/FV0T08PdBP/HfqQkc/Qrx/mtpz4pa1+\nnCNlsMAiosfWSBezJyL36fwn4P/+7jj0fuPcfs20J41Ys+rHXkylLBZYRPTYGuli9mOps7MTvb09\no3i+w4tpiEbPf8IUNHwN4Gv3X9PVUI9btzpG9TlPPDEe48a5X8QpiQUWEdE33LlgvTf8z/+1DY47\nva6fOMDEJ2eit/XGsI/r/XTo6R7de3rif8zoKwzPXfHsIORY5RyN3ta/PLRMjTmH82DWR50jb7nS\naUfaxl+M6jVL//MZ/Gzdai8lkpciBZbZ7DvXJeNY1MdXxgH41ljUaKSL2Y9E7nk5/d/vyPp+5KG3\nXlE6AfkQnqaBiB5biYmJKC8vB4CHLmZPRPQoeIiQiB5bQ13MnohIDpJQqumAiIiIyEfxECERERGR\nzFhgEREREcmMBRYRERGRzMa0yV2L1/waaN++fbhw4QJ6enqwceNGxMTE4KWXXoIQAmazGfv27dPM\nCdDu3r2LpUuXYvPmzXjqqac0O46ysjIUFxfDz88PW7ZsQVRUlCbH0tXVhe3bt6O9vR1ff/01Nm/e\njG9/+9uaGsvf//53pKenY+3atfjJT36CpqamIfOXlZXh8OHD0Ov1WLlyJVasWKF09CE9uG4tXLjQ\n+VhVVRUKCgqg1+sxY8YM5ObmqjJnv/379+PixYt4++23FUh430hZm5qakJGRge7ubsyaNQu7du1S\nZc533nkHJ0+ehF6vR3R0NF5++WVFMh4/fhwnTpyAJEkQQqCurg4XLlxwPl5RUeFcRxcsWIAXX3xR\nlTnVtC25ytrP7e1JjJFz586Jn/3sZ0IIIS5duiRWrVo1Vh8ti6qqKrFhwwYhhBA3b94UixYtEpmZ\nmeLUqVNCCCEOHDgg3nvvPSUjjsqBAwfEihUrRGlpqcjMzBTl5eXO5VoZx82bN0VycrLo6uoSLS0t\nYseOHZody5EjR8SBAweEEEI0NzeL733ve5pav7q6usTatWvFzp07xZEjR4QQYsi56OrqEkuWLBEO\nh0PcuXNHLF26VLS3tysZfUhDrVsDJScni6amJiGEEFu2bBFnzpxRIqbLnEL0/b5NTU0Vq1evViDh\nfa6y/vznPxenT58WQgixe/du8a9//UuJmCPmvHXrlli8eLHo7e0VQgixbt06UVNTo0jOgc6dOyd2\n7949aNlzzz0nmpqaRG9vr0hLSxOXLl1SKN19Q+VUy7b0oKGyCjG67WnMDhEOd80vrZg3bx5ef/11\nAMCECRPQ1dWF6upqPPvsswCAxYsXo6KiQsmIbquvr8eVK1ewcOFCCCFQXV2NxYsXA9DWOCoqKpCY\nmIjx48dj8uTJ2L17N86dO6fJsZhMJty8eRMA0N7eDpPJpKn1KyAgAL/97W8xefJk57Kh5qKmpgax\nsbEwGAwICAjAnDlzhvwfotKGWrcGKikpgcViAdA3d21tbUrEdJkTAPbu3Ytt27YpkG6wkbIKIXD+\n/Hnn+r5jxw48+eSTqsvp7++PgIAAOBwOdHd3486dOwgODlYk50BvvvnmoD1U165dw8SJE2GxWCBJ\nEhYuXIiqqioFE/Z5MCegnm3pQUNlBUa3PY1ZgWW322EymZz3+6/5pRU6nQ7jx48H0LcbcdGitCKw\nFAAABS5JREFURbh9+7bzkM2kSZPQ0tKiZES37du3D5mZmc77Wh3HV199hdu3b2PTpk14/vnnUVlZ\niTt37mhyLN///vfR1NSE5ORkrFmzBtu3b9fUvOh0Ovj7+w9a9mB+m82G1tbWQb8HTCaTKsc11Lo1\nUFBQEIC+M79XVFQMeVhuLLjKWVpaioSEBEyZMkWRfAONlPXGjRsIDAxEbm4u0tLScODAAVXm9Pf3\nR3p6OpKSkvDd734Xc+bMwfTp0xXLCgB/+ctfMGXKFEyaNMm57MHvW5PJBJvNpkQ8p6FyAurZlgYa\nLutotyfFTjQqNHr6rdOnT6OkpATFxcVITk52LtfKeH7/+99j3rx5CAsLG/JxrYwD6Mva1taGN998\nE1999RXWrFkzKL+WxlJWVoYnn3wSBw8exD/+8Q9kZ2cPelxLYxnKcPnVOq7+dauoqAjXr1/HmjVr\n8Ic//GHQc1pbW7Fp0ybs2rVLsb0YI+Vsb2/HiRMncOjQITQ2Nir+sx4pqxACNpsNa9euRVhYGDZu\n3IgzZ84o8mU7Uk6Hw4GioiJ8+OGHMBgM+OlPf4ovv/wSM2fOHPOc/d5//338+Mc/HvE5Ss89MHJO\nNWxLAw2V1ZPtacz2YHl6zS81+fTTT3Hw4EH87ne/Q1BQEAwGA+7duwcAaG5uRmhoqMIJXTtz5gxO\nnTqFVatW4fjx4ygqKkJgYKDmxgEAkydPRlxcHHQ6HaZOnQqDwaDJOQGACxcu4JlnngEAREVFobm5\nGePHj9fkWPo9OBcWiwWhoaGD9lipdVz965YkSc5168aN+xdWdjgc2LBhAzIyMpCQkKDKnFVVVWht\nbUVaWhrS09Pxt7/9Dfn5+arMGhISgvDwcERERECn0yEhIQGXLl1SXc76+npMnToVwcHB8PPzQ3x8\nPGpraxXJ2e/cuXOIi4sbtEyN29lQOQH1bEsDDZXVk+1pzAosrV/zy+Fw4NVXX8VvfvMbGI19F3pN\nSEhwjqm8vNz5BalmBQUFeP/993H06FGsWLECmzdvRkJCAk6dOgVAO+MA+tapzz77DEII3Lx5E11d\nXZody/Tp03Hx4kUAfYcoAgMD8fTTT2tyLP2G2j5iY2NRW1sLh8OBzs5OfP7554iPj1c46cOGWrcG\nHnLJz8/HCy+8gMTERAVTjpxzyZIlOHnyJKxWKwoLCzFr1qxBrQFqyqrX6xEREYGrV68C6PuOmDFj\nhupyhoeHo76+3vkfh9raWkybNk2RnEDfjgqDwQA/v8EHo8LDw9HZ2YnGxkZ0d3fjk08+wfz58xVK\nOXxOQD3bUr/hsnqyPY3ZIUKtX/Prgw8+QFtbG7Zu3QohBCRJwt69e5GdnY2jR48iLCwMy5cvVzqm\nR7Zs2YJf/vKXOHbsmKbGYbFYsGTJEqSkpECSJOTk5CA6OlqTY1m1ahWysrKwevVq9PT04Fe/+hVm\nzJiB7du3a2IsNTU1eOWVV3Djxg3o9XpYrVYUFxcjMzNz0Pah1+uxbds2rFu3DjqdDunp6c4eDDV5\ncN3asWMHSktLYTQaMX/+fJSVleHq1as4duwYJEnCsmXLsHLlSlXl7P+jIrVwlTUrKwuZmZkQQmDm\nzJnOhne15Vy/fj1Wr14NPz8/xMXFYe7cuYrkBICWlpZBfUIDc+7cuRMZGRkAgKVLlyraKzZcTjVt\nS66yerI98VqERERERDLjmdyJiIiIZMYCi4iIiEhmLLCIiIiIZMYCi4iIiEhmLLCIiIiIZMYCi4iI\niEhmLLCIiIiIZMYCi4iIiEhm/x8miq7PAs6LfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b2558198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_july.beta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of population susceptible, June model with no confirmation correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting p_susceptible\n"
     ]
    },
    {
     "data": {
      "image/png": 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p+/S/h0n47SQ/7zVdSq0mluKqjlgi5+oNPlufRHqJCX4tOZ2WzbJNx5i66M8K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b1e37e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june_noconf.p_susceptible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity estimates at June and July, per district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting lam_t\n"
     ]
    },
    {
     "data": {
      "image/png": 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cMtpzmVKeluk7CCGEGE7vpX0SEhKQmZmJuXPnqlwKr+2yeEu9XL6+uikcTl7O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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f464472c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_june.lam_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting lam_t\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tx6726+DMi889YbWYRMMxmpQplUpcXPTXlEhPT+fEiRPMmDFDl5RlZ2fj4+Oj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\nIiIiIhn4f4GXwIcDnQqGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b217ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(model_july.lam_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity for lab- versus clinical-confirmation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sHD5sperHY0CA4dJLXXTp4iIy0tC1axud8t3MPAacb775pinqEBERaXFcLsjL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\nNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b28e5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lam_june = model_june.lam.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.lam.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",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27135, 25373, 23611, 25416,  2677, 11117,  2516,  8950, 10866]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_june.S_age.trace()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(model_june.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(model_june.S_age.trace().squeeze(), columns=age_groups).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().squeeze(), columns=age_groups).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": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_july = pd.DataFrame(model_july.S_age.trace().squeeze(), columns=age_groups).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().squeeze(), columns=age_groups).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": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lly9Xa2urDMNQVVWVioqK1NLSol27dik7O1vl5eWaMGGCXC6XRo0apYMHDyorK0t79uxR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\nERGPoAZVREREREREPML/AQ/OVunCcq3fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f649f49fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.factorplot(\"Age\", \"S\", \"Confirmation\", S_age_june, kind=\"box\",\n",
    "                   palette=\"hls\", size=6, aspect=2, linewidth=0.3, fliersize=0, \n",
    "                  order=age_group.categories)\n",
    "g.despine(offset=10, trim=True)\n",
    "g.set_axis_labels(\"Age Group\", \"Susceptibles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_lam = pd.DataFrame(model_june.lam_t.trace()).unstack().reset_index()\n",
    "june_lam.columns = ('district', 'iteration', 'λ')\n",
    "june_lam['month'] = 'June'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_lam_noconf = pd.DataFrame(model_june_noconf.lam_t.trace()).unstack().reset_index()\n",
    "june_lam_noconf.columns = ('district', 'iteration', 'λ')\n",
    "june_lam_noconf['month'] = 'June'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "july_lam = pd.DataFrame(model_july.lam_t.trace()).unstack().reset_index()\n",
    "july_lam.columns = ('district', 'iteration', 'λ')\n",
    "july_lam['month'] = 'July'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "july_lam_noconf = pd.DataFrame(model_july_noconf.lam_t.trace()).unstack().reset_index()\n",
    "july_lam_noconf.columns = ('district', 'iteration', 'λ')\n",
    "july_lam_noconf['month'] = 'July'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "confirmed_lam = june_lam.append(july_lam, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-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"
     ]
    }
   ],
   "source": [
    "june_means = june_lam.groupby('district')['λ'].mean()\n",
    "june_means.sort(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-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"
     ]
    }
   ],
   "source": [
    "july_means = july_lam.groupby('district')['λ'].mean()\n",
    "july_means.sort(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted_districts = june_means.index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity by district in June and July (with lab confirmation), sorted by June means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sb.set_context(\"talk\", font_scale=0.8)\n",
    "\n",
    "f, (ax_1, ax_2) = plt.subplots(2, 1, figsize=(12,6), sharey=True, sharex=True)\n",
    "\n",
    "sb.boxplot('district', 'λ', data=june_lam, ax=ax_1, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=sorted_districts)\n",
    "# ax_1.hlines(1, xmin=0, xmax=93, linestyles='dashed', linewidth=0.2)\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_xlabel('')\n",
    "ax_1.set_ylabel('June')\n",
    "ax_1.set_title(r'Epidemic intensity (λ) estimates, ordered by June means')\n",
    "\n",
    "sb.boxplot('district', 'λ', data=july_lam, ax=ax_2, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=sorted_districts)\n",
    "# ax_2.hlines(1, xmin=0, xmax=93, linestyles='dashed', linewidth=0.2)\n",
    "ax_2.set_xticks([])\n",
    "ax_2.set_ylabel('July')\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity by district in June for lab-confirmed and clinical-confirmed, sorted by lab-confirmed means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f, (ax_1, ax_2) = plt.subplots(2, 1, figsize=(12,6), sharey=True, sharex=True)\n",
    "\n",
    "sb.boxplot('district', 'λ', data=june_lam, ax=ax_1, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=june_means.index.values)\n",
    "# ax_1.hlines(1, xmin=0, xmax=93, linestyles='dotted', linewidth=0.75)\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_xlabel('')\n",
    "ax_1.set_ylabel('Lab')\n",
    "ax_1.set_title(r'June epidemic intensity (λ) estimates, ordered by lab-confirmed means')\n",
    "\n",
    "sb.boxplot('district', 'λ', data=june_lam_noconf, ax=ax_2, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=june_means.index.values)\n",
    "# ax_2.hlines(1, xmin=0, xmax=93, linestyles='dotted', linewidth=0.75)\n",
    "ax_2.set_xticks([])\n",
    "ax_2.set_ylabel('Clinical')\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity by district in July for lab-confirmed and clinical-confirmed, sorted by lab-confirmed means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "july_means = july_lam.groupby('district')['λ'].mean()\n",
    "july_means.sort(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f, (ax_1, ax_2) = plt.subplots(2, 1, figsize=(12,6), sharey=True, sharex=True)\n",
    "\n",
    "sb.boxplot('district', 'λ', data=july_lam, ax=ax_1, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=july_means.index.values)\n",
    "# ax_1.hlines(1, xmin=0, xmax=93, linestyles='dotted', linewidth=0.75)\n",
    "ax_1.set_xticks([])\n",
    "ax_1.set_xlabel('')\n",
    "ax_1.set_ylabel('Lab')\n",
    "# ax_1.set_yticks(np.arange(13, step=2))\n",
    "ax_1.set_title(r'July epidemic intensity (λ) estimates, ordered by lab-confirmed means')\n",
    "\n",
    "sb.boxplot('district', 'λ', data=july_lam_noconf, ax=ax_2, linewidth=0.5, \n",
    "           fliersize=0, color='r', order=sorted_districts)\n",
    "# ax_2.hlines(1, xmin=0, xmax=93, linestyles='dotted', linewidth=0.75)\n",
    "ax_2.set_xticks([])\n",
    "ax_2.set_ylabel('Clinical')\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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",
      "\t0.207            0.013            0.001            [ 0.185  0.232]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.184            0.197           0.207          0.217         0.231\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.546            0.02             0.002            [ 0.513  0.584]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.509            0.53            0.544          0.563         0.581\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.793            0.014            0.001            [ 0.768  0.818]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.764            0.782           0.793          0.804         0.816\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.305            0.012            0.001            [ 0.281  0.325]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.282            0.295           0.306          0.314         0.326\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": 191,
   "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": 192,
   "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": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f464480c2e8>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAWYaEuAAAAwF/5+ctUVva1X2NUVFSod+/efo0RH99HGRnT/BoDrYPNGGNacofbtu1u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\nAADAgJAMAAAAGPw/eEqbwup/epAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f464480c240>"
      ]
     },
     "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": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f64990997b8>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAWYaEuAAAAwF/5+ctUVva1X2NUVFSod+/efo0RH99HGRnT/BoDrYPNGGNacofbtu1u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\nAADAgJAMAAAAGPw/eEqbwup/epAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64b21843c8>"
      ]
     },
     "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": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AgQPVv39/f5YEAKin8PBwXXC15NK9geyFyxQe7pu+uKFBHxsbW6sHX1JSopiY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\nDwCAiRH0AACY2P8BfsUYA3InTqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6499002748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sb.boxplot(data=june_coverage, order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n",
    "                  color=sb.color_palette(\"coolwarm\", 5)[0])\n",
    "axes.set_xticklabels(['Under 5', 'Under 15', 'Under 30', 'Under 5 + 20-30'])\n",
    "axes.set_ylabel('% susceptibles vaccinated')\n",
    "sb.despine(offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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",
      "\t0.199            0.01             0.001            [ 0.181  0.221]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.179            0.194           0.199          0.205         0.22\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.573            0.01             0.001            [ 0.554  0.593]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.553            0.565           0.573          0.581         0.592\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.807            0.009            0.001            [ 0.79   0.824]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.789            0.801           0.807          0.813         0.823\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.275            0.013            0.001            [ 0.249  0.301]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.247            0.266           0.274          0.284         0.3\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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",
      "\t0.255            0.007            0.001            [ 0.243  0.27 ]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.24             0.251           0.255          0.26          0.269\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.584            0.009            0.001            [ 0.568  0.6  ]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.567            0.578           0.584          0.59          0.599\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.815            0.006            0.001            [ 0.803  0.827]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.803            0.811           0.816          0.819         0.827\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.365            0.01             0.001            [ 0.346  0.385]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.343            0.358           0.365          0.372         0.384\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_july.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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",
      "\t0.252            0.006            0.001            [ 0.239  0.264]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.239            0.248           0.252          0.256         0.264\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.613            0.007            0.001            [ 0.602  0.626]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.602            0.608           0.612          0.617         0.627\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.827            0.004            0.0              [ 0.821  0.835]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.821            0.825           0.827          0.83          0.835\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.342            0.006            0.001            [ 0.331  0.355]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.33             0.338           0.342          0.346         0.354\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_july_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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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psrsMwPEaDBQX+u///m9t27YteD/E1/dVZGdnq3v37jX2tyxLP/rRj3TppZeqc+fOSk9P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\nUAAAAGMECgAAYIxAAQAAjBEoAACAsf8FSOC2riMvGkEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4616ce66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june.p_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Mapping spatial effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "import geopandas as gpd\n",
    "\n",
    "lllat=-24\n",
    "urlat=-23.3\n",
    "lllon=-47\n",
    "urlon=-46.3\n",
    "\n",
    "SP_base = Basemap(ax=None, lon_0=(urlon + lllon) / 2, lat_0=(urlat + lllat) / 2,\n",
    "        llcrnrlat=lllat, urcrnrlat=urlat, llcrnrlon=lllon, urcrnrlon=urlon, \n",
    "                  resolution='i',\n",
    "                 epsg='4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist = gpd.GeoDataFrame.from_file('Sao Paulo/Brazil_full/BRA_adm3.shp').to_crs({'proj': 'longlat', \n",
    "                                                                               'ellps': 'WGS84', \n",
    "                                                                               'datum': 'WGS84'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist['DIST_NAME'] = [trans.trans(_).upper() for _ in SP_dist.NAME_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "λ_june = pd.Series(model_june.λ_t.stats()['mean'], index=sp_districts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "λ_june"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SP_dist_merged = SP_dist.merge(pd.DataFrame(λ_june, columns=['λ']), left_on='DIST_NAME', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_onset_conf = measles_data[CONFIRMED].groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_onset_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_rates = measles_onset_conf/sp_pop.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SP_dist_conf = SP_dist.merge(pd.DataFrame(_rates, columns=['rate']), left_on='DIST_NAME', right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated expected value for infecteds, by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import cm\n",
    "\n",
    "map_fig = plt.figure(figsize=(16,12))\n",
    "map_ax = plt.gca()\n",
    "SP_base.drawcoastlines()\n",
    "SP_base.drawrivers()\n",
    "\n",
    "SP_dist_merged.plot(column='λ', colormap=cm.Reds, axes=map_ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observed confirmed cases, by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map_fig = plt.figure(figsize=(16,12))\n",
    "map_ax = plt.gca()\n",
    "SP_base.drawcoastlines()\n",
    "SP_base.drawrivers()\n",
    "\n",
    "SP_dist_conf.plot(column='rate', colormap=cm.Reds, axes=map_ax)"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}