fonnesbeck/Non-Spatial Measles Model.ipynb

## Non-Spatial Measles Model.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport pandas as pd\nimport numpy as np\nimport numpy.ma as ma\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_context(\"talk\")\nimport engarde.checks as check\n\nnp.random.seed(20090425)",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_dir = \"data/\"",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Import outbreak data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0, encoding='latin-1')\nmeasles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\nmeasles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\nmeasles_data.ONSET = pd.to_datetime(measles_data.ONSET)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "measles_data = (measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})\n                    .drop('AGE', axis=1))",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Sao Paulo population by district"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "_names = sp_pop.index.values\n_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\nsp_pop.set_index(_names, inplace = True)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sp_pop.head(3)",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\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  </tbody>\n</table>\n</div>",
            "text/plain": "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\nAGUA RASA                5411        5750          6450          7122   \nALTO DE PINHEIROS        2070        2369          2953          3661   \nANHANGUERA               3068        3006          2755          2431   \n\n                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\nAGUA RASA                  7621          7340          6999          6984   \nALTO DE PINHEIROS          4612          4190          3539          3633   \nANHANGUERA                 2426          2636          2695          2308   \n\n                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\nAGUA RASA                  6346          5608          4987          4212   \nALTO DE PINHEIROS          3448          3289          3040          2533   \nANHANGUERA                 1653          1107           753           509   \n\n                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\nAGUA RASA                  4152          3595          2937         3637   \nALTO DE PINHEIROS          2298          1732          1305         1823   \nANHANGUERA                  352           217           162          171   \n\n                   Total  \nAGUA RASA          89151  \nALTO DE PINHEIROS  46495  \nANHANGUERA         26249  "
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Plot of cumulative cases by district"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\nmeasles_onset_dist.cumsum().plot(legend=False, grid=False)",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x11a01a908>"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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pcBswYOEQQiSEPo+8vBPbsKRQDGdWLN9L2Vv7sCDwWgRfvWb8kNy3or2C0uZSqp3VFDcX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NkBex8jU0hz0MvRh8srXh5UcYHWvMWhVIwBFwbzAcQLAi62lac6myacOepeEfELYoC8uGQkT9zL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\nwWAwTAhjOAwGg8EwIYzhMBgMBsOE+P8pBBBXQy4A0gAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x119fac9e8>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "measles_data[measles_data.ONSET<'1997-06-15'].shape",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "text/plain": "(3698, 14)"
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "measles_data[measles_data.ONSET<'1997-07-15'].shape",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "(11982, 14)"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Age distribution of cases, by confirmation status"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "by_conclusion = measles_data.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\ncounts_by_cause = by_conclusion.size().unstack().fillna(0)\nax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "image/png": 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SQhEREZHTrEePHiQmJjJ8+HCqVKkCQKVKlRgwYAAPPvhgsftLSkoiJSWFt99+26/6o0eP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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x1187c5780>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Vaccination Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\nvaccination_data.head()",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\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      <th>beta_alpha</th>\n      <th>beta_beta</th>\n    </tr>\n    <tr>\n      <th>YEAR</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980</th>\n      <td>3896442</td>\n      <td>0.57</td>\n      <td>121740438</td>\n      <td>0.0</td>\n      <td>55.3128</td>\n      <td>41.7272</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>3933136</td>\n      <td>0.73</td>\n      <td>124610790</td>\n      <td>0.0</td>\n      <td>56.8232</td>\n      <td>21.0168</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>3952137</td>\n      <td>0.66</td>\n      <td>127525420</td>\n      <td>0.0</td>\n      <td>58.5816</td>\n      <td>30.1784</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>3952735</td>\n      <td>0.68</td>\n      <td>130455659</td>\n      <td>0.0</td>\n      <td>58.5072</td>\n      <td>27.5328</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>3935224</td>\n      <td>0.73</td>\n      <td>133364277</td>\n      <td>0.0</td>\n      <td>56.8232</td>\n      <td>21.0168</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       BIRTHS   VAX        POP  SIA  beta_alpha  beta_beta\nYEAR                                                      \n1980  3896442  0.57  121740438  0.0     55.3128    41.7272\n1981  3933136  0.73  124610790  0.0     56.8232    21.0168\n1982  3952137  0.66  127525420  0.0     58.5816    30.1784\n1983  3952735  0.68  130455659  0.0     58.5072    27.5328\n1984  3935224  0.73  133364277  0.0     56.8232    21.0168"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Calculate residual susceptibility from routine vaccination"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]\nn = len(vax_97)\nFOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vacc_susc = (1 - vax_97*0.9)[::-1]\nvacc_susc[0] = 0.5",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Susceptiblity accounting for SIAs"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sia_susc = np.ones(len(vax_97))\nbirth_year = np.arange(1922, 1998)[::-1]\nby_mask = (birth_year > 1983) & (birth_year < 1992)\nsia_susc[by_mask] *= 0.2",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Compilation of cases into 2-week intervals by age class\n\nAge classes are defined in 5-year intervals. We will combine 40+ ages into a single class."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_classes = [0,5,10,15,20,25,30,35,40,100]\nmeasles_data.dropna(subset=['YEAR_AGE'], inplace=True)\nmeasles_data['YEAR_AGE'] = measles_data.YEAR_AGE.astype(int)\nmeasles_data['AGE_GROUP'] = pd.cut(measles_data.YEAR_AGE, age_classes, right=False)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Lab-checked observations are extracted for use in estimating lab confirmation probability."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "CONFIRMED = measles_data.CONCLUSION == 'CONFIRMED'\nCLINICAL = measles_data.CONCLUSION == 'CLINICAL'\nDISCARDED = measles_data.CONCLUSION == 'DISCARDED'",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Extract lab-confirmed and clinical-confirmed subsets, with no missing county information."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lab_subset = measles_data[(CONFIRMED | DISCARDED) & measles_data.COUNTY.notnull()].copy()",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "age = lab_subset.YEAR_AGE.values\nages = lab_subset.YEAR_AGE.unique()\ncounties = lab_subset.COUNTY.unique()\nconfirmed = (lab_subset.CONCLUSION=='CONFIRMED').values",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clinic_subset = measles_data[CLINICAL & measles_data.COUNTY.notnull()].copy()",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Histogram of lab subset, by outcome."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "_lab_subset = lab_subset.replace({\"CONCLUSION\": {\"CLINICAL\": \"UNCONFIRMED\"}})\nby_conclusion = _lab_subset.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\ncounts_by_cause = by_conclusion.size().unstack().fillna(0)\nax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5), grid=False)",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "image/png": 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gTAglSZIkqaBMCCVJkiSpoEwIJUmSJKmgTAglSZIkqaBMCCVJkiSpoEwIJUmS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\n6g9pn/hiJBqUNgAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x118a53d30>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lab_subset.shape",
      "execution_count": 22,
      "outputs": [
        {
          "data": {
            "text/plain": "(41547, 15)"
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Define age groups"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_group = pd.cut(age, age_classes, right=False)\nage_index = np.array([age_group.categories.tolist().index(i) for i in age_group])\nage_groups = age_group.categories\nage_groups",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "text/plain": "IntervalIndex([[0, 5), [5, 10), [10, 15), [15, 20), [20, 25), [25, 30), [30, 35), [35, 40), [40, 100)]\n              closed='left',\n              dtype='interval[int64]')"
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_slices = [slice(g.left, g.right) for g in age_groups]",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Get index from full cross-tabulation to use as index for each district"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dates_index = measles_data.groupby(['ONSET', 'AGE_GROUP']).size().unstack().index",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Cleanup of Sao Paulo population data\n\nMatch age groupings, exclude invalid districts."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "unique_districts = measles_data.DISTRICT.dropna().unique()",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "excludes = ['BOM RETIRO']\n\nN = sp_pop.drop(excludes).loc[unique_districts].sum().drop('Total')",
      "execution_count": 27,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "N_age = pd.Series(np.concatenate([N.iloc[:8], [N.iloc[8:].sum()]]), index=age_groups)\nN_age",
      "execution_count": 28,
      "outputs": [
        {
          "data": {
            "text/plain": "[0, 5)        844130.0\n[5, 10)       830880.0\n[10, 15)      858750.0\n[15, 20)      904972.0\n[20, 25)      945244.0\n[25, 30)      902086.0\n[30, 35)      835888.0\n[35, 40)      764605.0\n[40, 100)    2841133.0\ndtype: float64"
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "N_age.plot(kind='bar')",
      "execution_count": 29,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x118ed5320>"
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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gIh7JP58HfFHSYuC/gM8BtwPX5fqwMpjtKHpOunxEtrP2tMNGZDtm20vdc1PvAs6v/Lw5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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x1194c6780>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# All confirmed cases, by district\nconfirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\nconfirmed_counts = (confirmed_data.groupby(['ONSET', 'AGE_GROUP'])\n                    .size()\n                    .unstack()\n                    .reindex(dates_index)\n                    .fillna(0)\n                    .sum())\n\nall_confirmed_cases = (confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n                       .fillna(0).values.astype(int))",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "confirmed_data.shape",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/plain": "(22097, 15)"
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "confirmed_counts_2w = (confirmed_data\n                        .groupby(['ONSET', 'AGE_GROUP'])\n                        .size()\n                        .unstack()\n                        .reindex(dates_index)\n                        .fillna(0)\n                        .resample('2W')\n                        .sum()\n                        .pipe(check.is_shape, \n                              (28, len(age_groups)))) ",
      "execution_count": 32,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# All clinical cases, by district\nclinical_counts = (clinic_subset.groupby(['ONSET', 'AGE_GROUP'])\n                   .size()\n                   .unstack()\n                   .reindex(dates_index)\n                   .fillna(0)\n                   .sum())\n\nall_clinical_cases = (clinical_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n                      .fillna(0).values.astype(int))",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clinical_counts_2w = (clinic_subset\n                        .groupby(['ONSET', 'AGE_GROUP'])\n                        .size()\n                        .unstack()\n                        .reindex(dates_index)\n                        .fillna(0)\n                        .resample('2W')\n                        .sum()\n                        .pipe(check.is_shape, \n                              (28, len(age_groups)))) \n",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "confirmed_counts_2w.head()",
      "execution_count": 35,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>AGE_GROUP</th>\n      <th>[0, 5)</th>\n      <th>[5, 10)</th>\n      <th>[10, 15)</th>\n      <th>[15, 20)</th>\n      <th>[20, 25)</th>\n      <th>[25, 30)</th>\n      <th>[30, 35)</th>\n      <th>[35, 40)</th>\n      <th>[40, 100)</th>\n    </tr>\n    <tr>\n      <th>ONSET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1997-01-05</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-01-19</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-02</th>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-16</th>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-03-02</th>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\nONSET                                                                           \n1997-01-05     0.0      0.0       0.0       0.0       1.0       0.0       0.0   \n1997-01-19     0.0      1.0       0.0       0.0       3.0       4.0       0.0   \n1997-02-02     4.0      1.0       0.0       0.0       2.0       1.0       0.0   \n1997-02-16     4.0      0.0       0.0       0.0       2.0       1.0       1.0   \n1997-03-02     9.0      0.0       0.0       2.0       4.0       5.0       1.0   \n\nAGE_GROUP   [35, 40)  [40, 100)  \nONSET                            \n1997-01-05       0.0        0.0  \n1997-01-19       0.0        0.0  \n1997-02-02       0.0        0.0  \n1997-02-16       0.0        0.0  \n1997-03-02       0.0        1.0  "
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clinical_counts_2w.head()",
      "execution_count": 36,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>AGE_GROUP</th>\n      <th>[0, 5)</th>\n      <th>[5, 10)</th>\n      <th>[10, 15)</th>\n      <th>[15, 20)</th>\n      <th>[20, 25)</th>\n      <th>[25, 30)</th>\n      <th>[30, 35)</th>\n      <th>[35, 40)</th>\n      <th>[40, 100)</th>\n    </tr>\n    <tr>\n      <th>ONSET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1997-01-05</th>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-01-19</th>\n      <td>30.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-02</th>\n      <td>22.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1997-02-16</th>\n      <td>21.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1997-03-02</th>\n      <td>24.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\nONSET                                                                           \n1997-01-05     3.0      1.0       1.0       0.0       0.0       0.0       0.0   \n1997-01-19    30.0      3.0       1.0       1.0       1.0       3.0       2.0   \n1997-02-02    22.0      4.0       0.0       2.0       1.0       1.0       1.0   \n1997-02-16    21.0      2.0       2.0       2.0       2.0       1.0       1.0   \n1997-03-02    24.0      5.0       2.0       5.0       2.0       2.0       2.0   \n\nAGE_GROUP   [35, 40)  [40, 100)  \nONSET                            \n1997-01-05       0.0        0.0  \n1997-01-19       1.0        0.0  \n1997-02-02       0.0        1.0  \n1997-02-16       0.0        2.0  \n1997-03-02       1.0        0.0  "
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Stochastic Disease Transmission Model\n\nWe will extend a simple SIR disease model, to account for confirmation status, which will be fit using MCMC.\n\nThis model fits the series of 2-week infection totals for each age group $a$ as a set of Poisson random variables:\n\n\\\\[Pr(I_{a}(t) | \\lambda_a(t)) = \\text{Poisson}(\\lambda_a(t)) \\\\]\n\nWhere the age-specific outbreak intensity at time $t$ is modeled as:\n\n\\\\[\\lambda_a(t) = S_a(t-1) \\frac{I(t-1)\\mathbf{B}}{N_a} \\\\]\n\nwhere $S_a(t-1)$ is the number of susceptibles in age group $a$ in the previous time period, $I(t-1)$ an age-specific vector of the number of infected individuals in the previous time period, $\\mathbf{B}$ a matrix of transmission coefficients (both within- and between-ages), and $N_a$ an estimate of the population of age-$a$ people in Sao Paulo.\n\nThe matrix $B$ was constructed from a scalar transmission parameter $\\beta$, which was given a vague half-Cauchy prior (scale=25). This was used to represent within-age-group transmission, and hence placed on the diagonal of a square transmission matrix of size $A$. Off-diagonal elements, representing transmission between age groups were scaled by a decay parameter $\\delta$ which was used to scale the transmission to adjacent groups according to:\n\n\\\\[\\beta \\delta^{|a-b|}\\\\]\n\nwhere a and b are indices of two age group. The resulting transmission matrix is parameterized as follows:\n\n$$\\begin{aligned}\n\\mathbf{B} = \\left[{\n\\begin{array}{c}\n  {\\beta} & {\\beta \\delta} & {\\beta \\delta^2}& \\ldots & {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-1}}  \\\\\n  {\\beta \\delta} & {\\beta} &  \\beta \\delta & \\ldots & {\\beta \\delta^{A-3}}  & {\\beta \\delta^{A-2}} \\\\\n  {\\beta \\delta^2} & \\beta \\delta & {\\beta} & \\ldots & {\\beta \\delta^{A-4}} & {\\beta \\delta^{A-3}} \\\\\n  \\vdots & \\vdots & \\vdots &  & \\vdots & \\vdots\\\\\n  {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-3}} & {\\beta \\delta^{A-4}} & \\ldots &  {\\beta} & \\beta \\delta  \\\\\n{\\beta \\delta^{A-1}} & {\\beta \\delta^{A-2}} & \\beta \\delta^{A-3} & \\ldots & \\beta \\delta &  {\\beta} \n\\end{array}\n}\\right]\n\\end{aligned}$$\n\nThe basic reproductive number $R_0$ was calculated as the largest real-valued eigenvalue of the matrix $\\mathbf{B}$. To impose a mild constraint on $R_0$, we applied a Gaussian prior distribution whose 1st and 99th quantiles are 8 and 24, respectively, a reasonable range for a measles outbreak:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from pymc import MCMC, Matplot, AdaptiveMetropolis, MAP, Slicer\nfrom pymc import (Uniform, DiscreteUniform, Beta, Binomial, Normal, \n                  CompletedDirichlet, Pareto,\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, uniform_like)\nfrom pymc import (Lambda, observed, invlogit, deterministic, potential, stochastic, logit)\n\nalphas, betas = vaccination_data[['beta_alpha', 'beta_beta']].values.T\n\ndef measles_model(obs_date, confirmation=True, migrant=False, constrain_R=True, vax_uncertainty=False):\n    \n    \n    '''\n    Truncate data at observation period\n    '''\n    obs_index = clinical_counts_2w.index <= obs_date\n    confirmed_obs_t = confirmed_counts_2w[obs_index].values.astype(int)\n    clinical_obs_t = clinical_counts_2w[obs_index].values.astype(int)\n                        \n    n_periods, n_age_groups = confirmed_obs_t.shape\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    lab_index = (lab_subset.NOTIFICATION > obs_date).values\n    confirmed_t = confirmed[lab_index]\n    age_index_t = age_index[lab_index]\n    \n    '''\n    Confirmation sub-model\n    '''\n    \n    if confirmation:\n\n        # Specify priors on age-specific means\n        age_classes = np.unique(age_index)\n\n        μ = Normal(\"μ\", mu=0, tau=0.0001, value=[0]*len(age_classes))\n        σ = HalfCauchy('σ', 0, 25, value=1)\n        var = σ**2\n        ρ = Uniform('ρ', -1, 1, value=0)\n\n        # Build variance-covariance matrix with first-order correlation \n        # among age classes\n        @deterministic\n        def Σ(var=var, cor=ρ):\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        β_age = MvNormalCov(\"β_age\", mu=μ, C=Σ, value=[1]*len(age_classes))\n        p_age = Lambda('p_age', lambda b=β_age: invlogit(b))\n\n        @deterministic(trace=False)\n        def p_confirm(b=β_age):\n            return invlogit(b[age_index_t])\n\n\n        # Confirmation likelihood\n        lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, value=confirmed_t, \n                                observed=True)\n\n    if confirmation:\n        \n        @stochastic(dtype=int)\n        def clinical_cases(value=(clinical_obs_t*0.5).astype(int), \n                            n=clinical_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        I = Lambda('I', lambda clinical=clinical_cases: \n                           clinical + confirmed_obs_t.astype(int))\n\n        assert I.value.shape == (t_obs +1, n_age_groups)\n        \n        age_dist_init = np.sum(I.value, 0)/ float(I.value.sum())\n        \n    else:\n        \n        I = confirmed_obs_t + clinical_obs_t\n\n        assert I.shape == (t_obs +1, n_age_groups)\n        \n        age_dist_init = np.sum(I, 0) / float(I.sum())\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    \n    @potential\n    def age_dist_like(p=age_dist, I=I):\n        return multinomial_like(I.sum(0), I.sum(), p)\n\n    \n    '''\n    Disease transmission model\n    '''\n\n    # Transmission parameter\n    β = HalfCauchy('β', 0, 25, value=5)#[5]*n_age_groups) \n    decay = Beta('decay', 1, 5, value=0.8)\n\n    @deterministic\n    def B(b=β, d=decay):\n        b = np.ones(n_age_groups)*b\n        B = b*np.eye(n_age_groups)\n        for i in range(1, n_age_groups):\n            B += np.diag(np.ones(n_age_groups-i)*b[i:]*d, k=-i) \n            B += np.diag(np.ones(n_age_groups-i)*b[:-i]*d, k=i)\n        return B\n\n    # Downsample annual series to observed age groups\n    downsample = lambda x: np.array([x[s].mean() for s in age_slices])\n\n    @deterministic\n    def R0(B=B):\n        evs = np.linalg.eigvals(B)\n        return max(evs[np.isreal(evs)])\n    \n    if constrain_R:\n        @potential\n        def constrain_R0(R0=R0):\n            # Weakly-informative prior to constrain R0 to be within the \n            # typical measles range\n            return normal_like(R0, 16, 3.4**-2)\n\n\n    A = Lambda('A', lambda R0=R0: 75./(R0 - 1))\n    \n    if vax_uncertainty:\n        \n        vax = Beta('vax', alphas[:17], betas[:17], value=vaccination_data.VAX[:17])\n\n        vax_97 = Lambda('vax_97', lambda vax=vax: np.r_[[0]*(1979-1921+1), vax])\n\n        n = len(vax_97.value)\n\n        FOI_mat = Lambda('FOI_mat', lambda vax_97=vax_97: np.resize((1 - vax_97*0.9), (n,n)).T)\n\n        @deterministic\n        def vacc_susc(vax_97=vax_97):\n            v = (1 - vax_97*0.9)[::-1]\n            v[0] = 0.5\n            return v\n\n        coverage_residual = Uniform('coverage_residual', 0.2, 0.325)\n\n        @deterministic\n        def sia_susc(r=coverage_residual):\n            s = np.ones(n)\n            birth_year = np.arange(1922, 1998)[::-1]\n            by_mask = (birth_year > 1983) & (birth_year < 1992)\n            s[by_mask] *= r\n            return s\n\n        lt_sum = Lambda('lt_sum', lambda FOI_mat=FOI_mat: downsample(np.tril(FOI_mat).sum(0)[::-1]))\n        natural_susc = Lambda('natural_susc', lambda A=A, lt_sum=lt_sum: np.exp((-1/A) * lt_sum))\n    \n    else:\n        lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])\n        natural_susc = Lambda('natural_susc', lambda A=A: np.exp((-1/A) * lt_sum))\n\n    @deterministic\n    def p_μ(v=vacc_susc, n=natural_susc, s=sia_susc): \n        return downsample(s) * downsample(v) * n\n    \n    # Following Stan manual chapter 16.2\n    λ_p = Pareto('λ_p', 1.5, 0.1, value=0.5)\n\n    a = Lambda('a', lambda mu=p_μ, lam=λ_p: mu*lam, trace=False)\n    b = Lambda('b', lambda mu=p_μ, lam=λ_p: (1-mu)*lam, trace=False)\n\n    p_susceptible = Beta('p_susceptible', a, b, value=p_μ.value)\n\n    # Estimated total initial susceptibles\n    if not migrant:\n        S_0_init = (N_age.values*p_μ.value).astype(int) + 30\n        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible, value=S_0_init)\n    else:\n        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible)\n    \n    '''\n    Model of migrant influx of susceptibles\n    '''\n    if migrant:\n    \n        # Data augmentation for migrant susceptibles\n        imaginary_migrants = 1000000\n        N_migrant = DiscreteUniform('N_migrant', 0, imaginary_migrants, value=100000)\n        μ_age = Uniform('μ_age', 15, 35, value=25)\n        σ_age = Uniform('σ_age', 1, 10, value=5)\n        M_age = Normal('M_age', μ_age, σ_age**-2, \n                       size=imaginary_migrants, trace=False)\n\n        @deterministic\n        def M_0(M=M_age, N=N_migrant):\n            # Take first N augmented susceptibles\n            M_real = M[:N]\n            # Drop into age groups\n            M_group = pd.cut(M_real, \n                             [0, 5, 10, 15, 20, 25, 30, 35, 40, 100], \n                             right=False)\n            return M_group.value_counts().values\n\n        p_migrant = Lambda('p_migrant', lambda M_0=M_0, S_0=S_0: M_0/(M_0 + S_0))\n\n        I_migrant = [Binomial('I_migrant_%i' % i, I[i], p_migrant) \n                             for i in range(t_obs + 1)]\n\n        I_local = Lambda('I_local', \n                lambda I=I, I_m=I_migrant: \n                         np.array([Ii - Imi for Ii,Imi in zip(I,I_m)]))\n        S = Lambda('S', lambda I=I, S_0=S_0, M_0=M_0: S_0 + M_0 - I.cumsum(0))\n        S_local = Lambda('S_local', lambda I=I_local, S_0=S_0: S_0 - I.cumsum(0))\n\n\n    else:\n\n        # Remaining susceptibles at each 2-week period\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    # Susceptibles at time t, by age\n    S_age = Lambda('S_age', lambda S=S: S[-1].astype(int))\n\n    # Force of infection\n    @deterministic\n    def λ(B=B, I=I, S=S): \n        return S * (I.dot(B) / N_age.values)\n\n    # Check shape\n    assert λ.value.shape == (t_obs+1, n_age_groups)\n    \n    \n    # FOI in observation period\n    λ_t = Lambda('λ_t', lambda lam=λ: lam[-1])\n    \n    # Effective reproductive number\n    R_t = Lambda('R_t', lambda S=S, R0=R0: S.sum(1) * R0 / N_age.sum())\n    \n    if migrant:\n        R_t_local = Lambda('R_t_local', lambda S=S_local, R0=R0: S.sum(1) * R0 / N_age.sum())\n    \n    # Poisson likelihood for observed cases\n    @potential\n    def new_cases(I=I, lam=λ):\n        return poisson_like(I[1:], lam[:-1])\n    \n    '''\n    Vaccination targets\n    '''\n    \n    # Probability of vaccine efficacy\n    p_efficacy = 0.95\n    # Reach of campaign\n    p_reach = 0.9\n    \n    @deterministic\n    def vacc_5(S=S_age):\n        # Vaccination of 5 and under\n        p = [p_reach] + [0]*(n_age_groups - 1)\n        return rbinomial(rbinomial(S, p), p_efficacy)\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 = [p_reach]*3 + [0]*(n_age_groups - 3)\n        return rbinomial(rbinomial(S, p), p_efficacy)\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 = [p_reach]*6 + [0]*(n_age_groups - 6)\n        return rbinomial(rbinomial(S, p), p_efficacy)\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 = [p_reach, 0, 0, 0, p_reach, p_reach] + [0]*(n_age_groups - 6)\n        return rbinomial(rbinomial(S, p), p_efficacy)\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()",
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model execution\n\nRun models for June 15 and July 15 observation points, both with and without clinical confirmation."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "n_iterations = 20000\nn_burn = 10000\nmigrant = True",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use `backgroundjobs` to run the models each in their own thread:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.lib import backgroundjobs as bg\n\njobs = bg.BackgroundJobManager()",
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Instantiate models"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june = MCMC(measles_model('1997-06-15', confirmation=True, migrant=migrant, vax_uncertainty=True))\nmodel_july = MCMC(measles_model('1997-07-15', confirmation=True, migrant=migrant, vax_uncertainty=True))\nmodel_june_noconf = MCMC(measles_model('1997-06-15', confirmation=False, migrant=migrant, vax_uncertainty=True))\nmodel_july_noconf = MCMC(measles_model('1997-07-15', confirmation=False, migrant=migrant, vax_uncertainty=True))",
      "execution_count": 52,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Run models"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for model in model_june, model_july, model_june_noconf, model_july_noconf:\n    jobs.new(model.sample, n_iterations, n_burn, kw=dict(progress_bar=False))",
      "execution_count": 53,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Starting job # 0 in a separate thread.\nStarting job # 2 in a separate thread.\nStarting job # 3 in a separate thread.\nStarting job # 4 in a separate thread.\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/fonnescj/Repos/pymc/pymc/distributions.py:854: ComplexWarning: Casting complex values to real discards the imaginary part\n  return flib.beta_like(x, alpha, beta)\n/Users/fonnescj/Repos/pymc/pymc/distributions.py:2169: ComplexWarning: Casting complex values to real discards the imaginary part\n  return flib.normal(x, mu, tau)\n/Users/fonnescj/anaconda3/envs/dev/lib/python3.6/site-packages/ipykernel_launcher.py:213: RuntimeWarning: invalid value encountered in true_divide\n"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "jobs.status()",
      "execution_count": 55,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Completed jobs:\n0 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x11ae55fd0>>\n2 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x11874dd68>>\n3 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x11ac17630>>\n4 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x118e63748>>\n\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Summary of model output\n\nEstimate of R0 for june (with confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "if model_june.R0.value.shape:\n    Matplot.summary_plot(model_june.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_june.R0)",
      "execution_count": 56,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Plotting R0\n"
        },
        {
          "data": {
            "image/png": 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QkJBgdgjCYqROGBPUuyaVUpcDu7TWq41McqqUagW0AhgwYIBPzzF7/AzUfWyV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\nzMwsERdiZmZmZom4EDMzMzNL5P8D3fCmS/vCXX8AAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x1188a08d0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for june (no confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "if model_june.R0.value.shape:\n    Matplot.summary_plot(model_june_noconf.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_june_noconf.R0)",
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Plotting R0\n"
        },
        {
          "data": {
            "image/png": 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fu3U3kcpXOTfGD0v2z9PW2Nqv+vxdk9MXf96+73nE/L+GX8xbw7INO8kvmu/3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\njWLFkR4qviEmIiJ9t2XLltARJIDBR+0OHaHiqSEmIiIFTZ0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6TF+Y2Viim5h/UvvSP1TK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\nAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11a084780>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for july (with confirmation submodel)"
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "if model_july.R0.value.shape:\n    Matplot.summary_plot(model_july.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_july.R0)",
      "execution_count": 58,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Plotting R0\n"
        },
        {
          "data": {
            "image/png": 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MGjez1jHZXWo/XP01/QkS1pD7Vs0aMiSzHT+vLWPh+u0ADAuQKPl6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\nVhHdAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11aed1860>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimate of R0 for july (no confirmation submodel)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "if model_july_noconf.β.shape:\n    Matplot.summary_plot(model_july_noconf.R0, custom_labels=age_groups)\nelse:\n    Matplot.plot(model_july_noconf.R0)",
      "execution_count": 59,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Plotting R0\n"
        },
        {
          "data": {
            "image/png": 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O7RFmCJ4EL1Dz0q+e+JSXbziR4d3913v7YnXt61aCpyN4bZ75eA2DurQK2Q9t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\nP15sAKYmXbcHx3dmvVhHRKQoMzvTzM7qfA58BngZWAN0nmC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Nk3x20oNuPrv/Dix39/fj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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11ae20278>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Lab confirmation rates, June model"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n\n    p_age = pd.DataFrame(model_june.p_age.trace(), columns=age_groups)\n\n    f, axes = plt.subplots(figsize=(14,6))\n    sns.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n              color=sns.color_palette(\"coolwarm\", 5)[0])\n    axes.set_ylabel('Proportion lab positive')\n    axes.set_xlabel('Age interval (years)')",
      "execution_count": 64,
      "outputs": [
        {
          "data": {
            "image/png": 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5ts3IyPBNpQAQAG63W06nM9BlSJLy8vICXYIXh8Mhu93Ud3QAANQKpsKVw+FQ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\ngg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x241cfb710>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Proportion of **local** population susceptible, June model."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_group_labels = [str(v) for v in age_groups.values]",
      "execution_count": 66,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.p_susceptible, custom_labels=age_group_labels)",
      "execution_count": 67,
      "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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kctuuxv33v7S/eTPMm9fI1s3M+o6GBDpJB5MC0Jm55MeAtwHjgTcBDwG/lrRb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\nmZm1NAc6MzNraQ50ZmbW0hz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            "text/plain": "<matplotlib.figure.Figure at 0x1b2c8a048>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Proportion of **local** population susceptible, June model with no confirmation correction"
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june_noconf.p_susceptible, custom_labels=age_group_labels)",
      "execution_count": 68,
      "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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Ec+Y0snYzs96jIYFO0sGkAHRWLvkJ4B3AWOAtwCPAryXtVknZEXFRRAwijdS+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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x118e63f28>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Epidemic intensity estimates at June or July observation time, by age group."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.λ_t, custom_labels=age_group_labels)",
      "execution_count": 70,
      "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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CH8AeFap3lxex/TZvHjSkY/+GhmS/OI9Hd2bWVdVt6jIisiswn5M0GXiRZGS1pAJVtKSP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\nQzXa0paaBDpJR5I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mlmsOdGZmlmsOdGZmlmsOdHUgqYekqyS9JqlF0gxJnf2pn12SpJsl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\nK26WPwAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x128748cc0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_july.λ_t, custom_labels=age_group_labels)",
      "execution_count": 71,
      "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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3RFwSEYOAscC3gKdz2YOAVUWHrAQGF6WtBnYupV0zs3qI6HybNw+asvm6pqa0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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x127a47b70>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Time series of epidemic intensities for lab- versus clinical-confirmation models, for each age group."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june.λ_t.trace().std()",
      "execution_count": 72,
      "outputs": [
        {
          "data": {
            "text/plain": "85.128330509261218"
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lam_june = model_june.λ_t.stats()",
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lam_june = model_june.λ.stats()\n\nfig, axes = plt.subplots(2, 1, sharey=True)\n\naxes[0].plot(lam_june['quantiles'][50], 'b-', alpha=0.4)\naxes[0].set_ylabel('Epidemic intensity')\naxes[0].set_xlabel('time (2-week periods)')\naxes[0].set_title('Lab confirmation')\n\nlam_june_noconf = model_june_noconf.λ.stats()\n\naxes[1].plot(lam_june_noconf['quantiles'][50], 'b-', alpha=0.4)\naxes[1].set_ylabel('Epidemic intensity')\naxes[1].set_xlabel('time (2-week periods)')\naxes[1].set_title('Clinical confirmation')\n\nplt.tight_layout()",
      "execution_count": 74,
      "outputs": [
        {
          "data": {
            "image/png": 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1bwbs82Xtw6nBnAY+Cgi8FbzzZuDTqpohcLnjOI6zPZNJ4byV2Vo4B2Eto7l9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\ngg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x127a78518>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n                          model_june.R_t_local.trace()[:, -1]],\n                        columns=['June, total', 'June, local'])\n\nax = Rt_values.boxplot(return_type='axes');\nax.set_ylabel('R(t)')",
      "execution_count": 75,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x11ae191d0>"
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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9wP2eB1wMMDY2dtP4+PgBbk4zMzNMTk6ybds2RkZGlrscrVFTU1MLLp+ZmeHZ77+aq/7w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\nAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x127a95a90>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n    \n    Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n                              model_june_noconf.R_t.trace()[:, -1],\n                             model_july.R_t.trace()[:, -1],\n                             model_july_noconf.R_t.trace()[:, -1]],\n                            columns=['June\\nage confirmation', 'June\\nclinical only',\n                                    'July\\nage confirmation', 'July\\nclinical only'])\n\n    ax = Rt_values.boxplot(return_type='axes', figsize=(14,6), grid=False);\n    ax.set_ylabel('R(t)')",
      "execution_count": 76,
      "outputs": [
        {
          "data": {
            "image/png": 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PPjoefvjheM973tPcOQEAAFq0RpWr3/72tzFnzpwoLy+P4cOHx5AhQ+Kss86KT37yk82d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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11ad87048>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "S_age_june = pd.DataFrame(model_june.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june.columns = 'Age', 'Iteration', 'S'\nS_age_june['Confirmation'] = 'Lab'\nS_age_june['Month'] = 'June'\n\nS_age_june_noconf = pd.DataFrame(model_june_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_june_noconf['Confirmation'] = 'Clinical'\nS_age_june_noconf['Month'] = 'June'\n\nS_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)\nS_age_june.to_csv('output/S_age_june.csv')",
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "S_age_june_local = pd.DataFrame(model_june.S_local.trace()[:,-1,:].squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_local.columns = 'Age', 'Iteration', 'S'\nS_age_june_local['Confirmation'] = 'Lab'\nS_age_june_local['Month'] = 'June'\n\nS_age_june_local_noconf = pd.DataFrame(model_june_noconf.S_local.trace()[:,-1,:].squeeze(), columns=age_groups).unstack().reset_index()\nS_age_june_local_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_june_local_noconf['Confirmation'] = 'Clinical'\nS_age_june_local_noconf['Month'] = 'June'\n\nS_age_june_local = pd.concat([S_age_june_local, S_age_june_local_noconf], ignore_index=True)\nS_age_june_local.to_csv('output/S_age_june_local.csv')",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "S_age_july = pd.DataFrame(model_july.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_july.columns = 'Age', 'Iteration', 'S'\nS_age_july['Confirmation'] = 'Lab'\nS_age_july['Month'] = 'July'\n\nS_age_july_noconf = pd.DataFrame(model_july_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\nS_age_july_noconf.columns = 'Age', 'Iteration', 'S'\nS_age_july_noconf['Confirmation'] = 'Clinical'\nS_age_july_noconf['Month'] = 'July'\n\nS_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)\nS_age_july.to_csv('output/S_age_july.csv')",
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "S_age = pd.concat([S_age_june.assign(susceptibles='Migrant + Local'), \n                   S_age_june_local.assign(susceptibles='Local only')], ignore_index=True)\nS_age.to_csv('output/S_age.csv')",
      "execution_count": 80,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Numbers of suscepibles in each age group, under lab vs clinical confirmation"
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.despine()\n\nwith sns.axes_style(\"ticks\"):\n    \n    g = sns.factorplot(\"Age\", \"S1000\", \"Confirmation\", data=S_age.assign(S1000=S_age.S/1000), \n                      kind=\"box\", palette=\"hls\", row='susceptibles', size=6, aspect=2, linewidth=0.3, fliersize=0, \n                      order=age_group.categories, legend=False, facet_kws={'ylim':(0, 250)})\n    g.despine(offset=10, trim=True)\n    g.set_axis_labels(\"Age Interval (years)\", \"Susceptibles (1000's)\")\n    plt.tight_layout();",
      "execution_count": 81,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x245b8eef0>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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1+OOPO4QlSfL399fo0aO1d+9etxQHAAAAAJ7kEJhq1Kih48ePGx5w/Pjxy4Yp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\niIiIiIiIJLBgIiIiIiIikvD/AG+btIZG3TS5AAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x2495bca90>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june.summary(['β', 'ρ'])",
      "execution_count": 98,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\nβ:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t4.169            4.61            4.875          5.129         5.57\n\t\n\nρ:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t-0.055           0.311            0.019            [-0.523  0.453]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t-0.509           -0.333          -0.077         0.215         0.49\n\t\n"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june.summary(['B'])",
      "execution_count": 99,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\nB:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t1.036            0.092            0.005            [ 0.876  1.223]\n\t4.872            0.36             0.027            [ 4.16   5.544]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t0.87             0.971           1.031          1.1           1.22\n\t4.169            4.61            4.875          5.129         5.57\n\t\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Vaccination coverage by strategy"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 82,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.18             0.007            0.0              [ 0.165  0.192]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.165            0.173           0.181          0.182         0.192\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.26             0.01             0.0              [ 0.241  0.276]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.24             0.251           0.261          0.263         0.276\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.764            0.002            0.0              [ 0.761  0.767]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.76             0.762           0.764          0.765         0.767\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.579            0.0              0.0              [ 0.578  0.579]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.578            0.578           0.579          0.579         0.579\n\t\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "june_coverage = pd.DataFrame({name: model_june.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njune_coverage['Month'] = 'June'\njune_coverage['Confirmation'] = 'Lab'",
      "execution_count": 83,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "june_noconf_coverage = pd.DataFrame({name: model_june_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njune_noconf_coverage['Month'] = 'June'\njune_noconf_coverage['Confirmation'] = 'Clinical'\n\njuly_coverage = pd.DataFrame({name: model_july.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njuly_coverage['Month'] = 'July'\njuly_coverage['Confirmation'] = 'Lab'\n\njuly_noconf_coverage = pd.DataFrame({name: model_july_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\njuly_noconf_coverage['Month'] = 'July'\njuly_noconf_coverage['Confirmation'] = 'Clinical'",
      "execution_count": 84,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], \n                     ignore_index=True)\n\ncoverage.to_csv('output/coverage.csv')",
      "execution_count": 85,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.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)",
      "execution_count": 86,
      "outputs": [
        {
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x24963a320>"
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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kjdcTn3wYt0/dObbtb/7Gzex7wIFj2fZ+E48ey3bVcIRXWqDjjz+ejRs3jmXbW7duZe3a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\nAxb+t1ngXOlCAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x24963a6d8>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "axes = sns.boxplot(data=june_coverage, order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n                  color=sns.color_palette(\"coolwarm\", 5)[0])\naxes.set_xticklabels(['Under 5', 'Under 15', 'Under 30', 'Under 5 + 20-30'])\naxes.set_ylabel('% susceptibles vaccinated')\nsns.despine(offset=10, trim=True)",
      "execution_count": 87,
      "outputs": [
        {
          "data": {
            "image/png": 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XnJltjNytZ2ZmLcfJqc3MnTu36raZ2cbEycnMzFqOB0RU54tiZlYbDyU3M7ON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\ntRwnJzMzazn/D5wLIMR/BzBtAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11977ae48>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "with sns.axes_style(\"ticks\"):\n    pc_values = np.maximum(0, 1- 1/Rt_values)\n    ax = pc_values.boxplot(return_type='axes', figsize=(14,6), grid=False)\n    ax.set_ylabel('$p_c$')\n    ax.semilogy();",
      "execution_count": 88,
      "outputs": [
        {
          "data": {
            "image/png": 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vfjH/2CzL4t57742rr746Kioqory8PMaPHx+HH354PPHEE7t8vvvuuy8uueSS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\nAAAgMUUMAAAgsf8HMTSwdRNToXwAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11977a240>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "qcoverage = coverage.groupby(['Month','Confirmation'], sort=False).quantile([0.025, 0.975])\nqcoverage",
      "execution_count": 89,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>pct_15</th>\n      <th>pct_30</th>\n      <th>pct_5</th>\n      <th>pct_adult</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th>Confirmation</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">June</th>\n      <th rowspan=\"2\" valign=\"top\">Lab</th>\n      <th>0.025</th>\n      <td>0.239764</td>\n      <td>0.760393</td>\n      <td>0.164731</td>\n      <td>0.577821</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.275871</td>\n      <td>0.766956</td>\n      <td>0.191777</td>\n      <td>0.579266</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Clinical</th>\n      <th>0.025</th>\n      <td>0.271059</td>\n      <td>0.765641</td>\n      <td>0.191371</td>\n      <td>0.579133</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.309842</td>\n      <td>0.772969</td>\n      <td>0.220901</td>\n      <td>0.580673</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">July</th>\n      <th rowspan=\"2\" valign=\"top\">Lab</th>\n      <th>0.025</th>\n      <td>0.229077</td>\n      <td>0.758223</td>\n      <td>0.156459</td>\n      <td>0.577649</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.249639</td>\n      <td>0.762031</td>\n      <td>0.171787</td>\n      <td>0.579115</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Clinical</th>\n      <th>0.025</th>\n      <td>0.242706</td>\n      <td>0.760656</td>\n      <td>0.170275</td>\n      <td>0.581104</td>\n    </tr>\n    <tr>\n      <th>0.975</th>\n      <td>0.260357</td>\n      <td>0.764155</td>\n      <td>0.183763</td>\n      <td>0.582420</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                            pct_15    pct_30     pct_5  pct_adult\nMonth Confirmation                                               \nJune  Lab          0.025  0.239764  0.760393  0.164731   0.577821\n                   0.975  0.275871  0.766956  0.191777   0.579266\n      Clinical     0.025  0.271059  0.765641  0.191371   0.579133\n                   0.975  0.309842  0.772969  0.220901   0.580673\nJuly  Lab          0.025  0.229077  0.758223  0.156459   0.577649\n                   0.975  0.249639  0.762031  0.171787   0.579115\n      Clinical     0.025  0.242706  0.760656  0.170275   0.581104\n                   0.975  0.260357  0.764155  0.183763   0.582420"
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "mean_coverage = coverage.groupby(['Month','Confirmation'], sort=False).mean()\nmean_coverage",
      "execution_count": 90,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>pct_15</th>\n      <th>pct_30</th>\n      <th>pct_5</th>\n      <th>pct_adult</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th>Confirmation</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">June</th>\n      <th>Lab</th>\n      <td>0.259751</td>\n      <td>0.763881</td>\n      <td>0.179740</td>\n      <td>0.578561</td>\n    </tr>\n    <tr>\n      <th>Clinical</th>\n      <td>0.289680</td>\n      <td>0.769166</td>\n      <td>0.205544</td>\n      <td>0.579899</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">July</th>\n      <th>Lab</th>\n      <td>0.239134</td>\n      <td>0.760080</td>\n      <td>0.163955</td>\n      <td>0.578391</td>\n    </tr>\n    <tr>\n      <th>Clinical</th>\n      <td>0.251491</td>\n      <td>0.762370</td>\n      <td>0.176992</td>\n      <td>0.581742</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                      pct_15    pct_30     pct_5  pct_adult\nMonth Confirmation                                         \nJune  Lab           0.259751  0.763881  0.179740   0.578561\n      Clinical      0.289680  0.769166  0.205544   0.579899\nJuly  Lab           0.239134  0.760080  0.163955   0.578391\n      Clinical      0.251491  0.762370  0.176992   0.581742"
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "label_lookup = dict(zip(['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n    ['Under 5', 'Under 15', 'Under 30', 'Adults & young kids']))",
      "execution_count": 91,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from matplotlib.patches import Rectangle\n\nplot_interval = True\n\nwith sns.axes_style(\"ticks\"):\n    \n    colors = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\"]\n    \n    fig = plt.figure(figsize=(14, 6))\n    ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.8])\n    ax1.semilogy()\n    ax2 = fig.add_axes([0.5, 0.1, 0.4, 0.8])\n    ax2.semilogy()\n    \n    q = pc_values.quantile([0.025, 0.975]).values + 1e-8\n    m = pc_values.mean().values\n    \n    months = ['June', 'July']\n    \n    for i,ax in enumerate([ax1, ax2]):\n        ax.set_yticks([0.01, 0.1, 0.2, 0.4, 0.7])\n        ax.set_yticklabels([0.01, 0.1, 0.2, 0.4, 0.7])\n        ax.set_ybound(0.0001, .999)\n        \n        ax.set_xticks([0.08, 0.27, 0.4])\n        ax.set_xticklabels(['lab confirmation', 'clinical only'])\n        \n        ax.tick_params(length=0)\n        \n        ax.set_title(months[i])\n                \n        ax.add_patch(Rectangle((0.0, q[0, 2*i]), 0.15, q[1, 2*i]-q[0, 2*i], alpha=0.1))\n        ax.add_patch(Rectangle((0.2, q[0, 1+2*i]), 0.15, q[1, 1+2*i]-q[0, 1+2*i], alpha=0.1))\n        \n        # Add estimates\n        conf = ('Lab', 'Clinical')\n        handles = []\n        for j, pct in enumerate(['pct_5', 'pct_15', 'pct_30', 'pct_adult']):\n            \n            interval = qcoverage.loc[('June', conf[i])]\n            ax.add_patch(Rectangle((0.04*j, interval.loc[0.025, pct]), \n                                   0.01, interval.loc[0.975, pct]-interval.loc[0.025, pct], \n                                   lw=2, \n                                   color=colors[j]))\n\n            interval = qcoverage.loc[('July', conf[i])]\n            rect = Rectangle((0.04*j + 0.2, interval.loc[0.025, pct]), \n                                   0.01, interval.loc[0.975, pct]-interval.loc[0.025, pct],\n                                   lw=2, label=label_lookup[pct],\n                                   color=colors[j])\n            ax.add_patch(rect)\n            handles.append(rect)\n        \n        \n        ax.axhline(m[2*i], 0.13, 0.45, color='black', lw=0.7)\n        ax.axhline(m[1+2*i], 0.57, 0.88, color='black', lw=0.7)\n\n    ax2.set_yticks([])\n    ax1.set_ylabel('Estimated vaccination threshold required')\n    \n    ax2.legend(handles=handles, loc=4)\n    \n    sns.despine(bottom=True)",
      "execution_count": 100,
      "outputs": [
        {
          "data": {
            "image/png": 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8uNlZAAAAAAAlKPGxybS0NPvrsWPHKiYmRuPGjVNQUJAqV67scC6bdAMAAACA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\nC/B/SjPJMPqJWJ0AAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x25104d048>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 93,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.206            0.008            0.0              [ 0.191  0.22 ]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.191            0.199           0.206          0.212         0.221\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.29             0.01             0.0              [ 0.271  0.31 ]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.271            0.281           0.29           0.298         0.31\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.769            0.002            0.0              [ 0.766  0.773]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.766            0.768           0.769          0.771         0.773\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.58             0.0              0.0              [ 0.579  0.581]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.579            0.58            0.58           0.58          0.581\n\t\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_july.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 94,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.164            0.004            0.0              [ 0.157  0.172]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.156            0.161           0.165          0.167         0.172\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.239            0.005            0.0                [ 0.23  0.25]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.229            0.235           0.24           0.243         0.25\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.76             0.001            0.0              [ 0.758  0.762]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.758            0.759           0.76           0.761         0.762\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.578            0.0              0.0              [ 0.578  0.579]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.578            0.578           0.578          0.579         0.579\n\t\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_july_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])",
      "execution_count": 95,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\npct_5:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.177            0.003            0.0              [ 0.17   0.184]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.17             0.175           0.178          0.179         0.184\n\t\n\npct_15:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.251            0.004            0.0              [ 0.243  0.26 ]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.243            0.248           0.253          0.254         0.26\n\t\n\npct_30:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.762            0.001            0.0              [ 0.761  0.764]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.761            0.762           0.762          0.763         0.764\n\t\n\npct_adult:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t0.582            0.0              0.0              [ 0.581  0.582]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t0.581            0.582           0.582          0.582         0.582\n\t\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Initial migrant susceptibles (June model, with confirmation)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june.summary(['N_migrant'])",
      "execution_count": 96,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\nN_migrant:\n \n\tMean             SD               MC Error        95% HPD interval\n\t------------------------------------------------------------------\n\t543025.202       31889.552        1371.904     [ 489638.  610703.]\n\t\n\t\n\tPosterior quantiles:\n\t\n\t2.5             25              50              75             97.5\n\t |---------------|===============|===============|---------------|\n\t491409.0         531518.0        536706.0       572897.0      614757.0\n\t\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "By age group:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june.M_0, custom_labels=age_group.categories)",
      "execution_count": 97,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
        },
        {
          "ename": "TypeError",
          "evalue": "object of type 'pandas._libs.interval.Interval' has no len()",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-580aedbd5528>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_june\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM_0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mage_group\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m~/Repos/pymc/pymc/Matplot.py\u001b[0m in \u001b[0;36msummary_plot\u001b[0;34m(pymc_obj, name, format, suffix, path, alpha, chain, quartiles, hpd, rhat, main, xlab, x_range, custom_labels, chain_spacing, vline_pos)\u001b[0m\n\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[0;31m# Update margins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1344\u001b[0;31m     \u001b[0mleft_margin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.015\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_margin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/Repos/pymc/pymc/Matplot.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[0;31m# Update margins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1344\u001b[0;31m     \u001b[0mleft_margin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.015\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_margin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'pandas._libs.interval.Interval' has no len()"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x11977fe80>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "june_r = pd.DataFrame({'local': model_june.trace('R_t_local')[:, -1],\n                             'total': model_june.trace('R_t')[:, -1]})\njune_r['Month'] = 'June'\njune_r['Confirmation'] = 'Lab'\n\njune_noconf_r = pd.DataFrame({'local': model_june_noconf.trace('R_t_local')[:, -1],\n                             'total': model_june_noconf.trace('R_t')[:, -1]})\njune_noconf_r['Month'] = 'June'\njune_noconf_r['Confirmation'] = 'Clinical'\n\njuly_r = pd.DataFrame({'local': model_july.trace('R_t_local')[:, -1],\n                             'total': model_july.trace('R_t')[:, -1]})\njuly_r['Month'] = 'July'\njuly_r['Confirmation'] = 'Lab'\n\njuly_noconf_r = pd.DataFrame({'local': model_july_noconf.trace('R_t_local')[:, -1],\n                             'total': model_july_noconf.trace('R_t')[:, -1]})\njuly_noconf_r['Month'] = 'July'\njuly_noconf_r['Confirmation'] = 'Clinical'",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "r_estimates = pd.concat([june_r, june_noconf_r, july_r, july_noconf_r], \n                     ignore_index=True)\n\nr_estimates.to_csv('output/r_estimates.csv')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "g = sns.factorplot(row=\"Month\", col=\"Confirmation\", data=r_estimates, kind='box',\n              row_order=['June', 'July'],\n              order=['local', 'total'], margin_titles=True,\n            palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)\ng.set_ylabels(r\"$R_t$\")\nfor ax in g.axes.ravel():\n    ax.hlines(1, -1, 2, linestyles='dashed')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Removing augmentation of migrant population"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june_nomigrant = MCMC(measles_model('1997-06-15', migrant=False))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_june_nomigrant.sample(n_iterations, n_burn)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.plot(model_june_nomigrant.R0)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "Matplot.summary_plot(model_june_nomigrant.R_t)",
      "execution_count": null,
      "outputs": []
    }
  ],
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