Epidemiology paper summaries

Epidemiology.ipynb

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     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized = pd.read_csv('data/hospitalized.csv', index_col=0)\n",
      "hospitalized.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/io/parsers.py:1071: DtypeWarning: Columns (140,142,144,146,148,181,206,207,212,213,261,280,281,282,296,297) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>greater_48hrs</th>\n",
        "      <th>fever_neutropenia</th>\n",
        "      <th>never_left</th>\n",
        "      <th>written_consent</th>\n",
        "      <th>child_name</th>\n",
        "      <th>mother_name</th>\n",
        "      <th>mother_birth_date</th>\n",
        "      <th>mother_record</th>\n",
        "      <th>mother_nationality</th>\n",
        "      <th>other_mother_nationality</th>\n",
        "      <th>father_nationality</th>\n",
        "      <th>other_father_nationality</th>\n",
        "      <th>child_record</th>\n",
        "      <th>newborn_id</th>\n",
        "      <th>newborn_id_number</th>\n",
        "      <th>adm_ari</th>\n",
        "      <th>adm_apnea</th>\n",
        "      <th>adm_asthma</th>\n",
        "      <th>adm_bronchiolitis</th>\n",
        "      <th>adm_bronchopneumo</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>case_id</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",
        "      <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",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A0001</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>           Remas Mahmoud Jbarah</td>\n",
        "      <td>                Huda Katalo</td>\n",
        "      <td> 1976-01-21</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2002048926</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0002</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>       Majed Abdel Kareem Majed</td>\n",
        "      <td>      Noor SHa'aban Mahmood</td>\n",
        "      <td> 1989-09-09</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2002043480</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0003</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>     Rayyan Jamal Muhyi Al.Deen</td>\n",
        "      <td> SAra Hussein Muhyi Al.Deen</td>\n",
        "      <td> 1965-01-01</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2001860539</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0004</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> Hanan Mohd Mustapha Abu Othman</td>\n",
        "      <td>           Kawla Abu Shanab</td>\n",
        "      <td> 1983-10-31</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2001953536</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0005</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>       Yara Mahmoud Azmi Ismael</td>\n",
        "      <td>            Suha Abdel Aziz</td>\n",
        "      <td> 1986-02-28</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2002040652</td>\n",
        "      <td> 3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 414 columns</p>\n",
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       "text": [
        "         greater_48hrs  fever_neutropenia  never_left  written_consent  \\\n",
        "case_id                                                                  \n",
        "A0001                0                  0           0                1   \n",
        "A0002                0                  0           0                1   \n",
        "A0003                0                  0           0                1   \n",
        "A0004                0                  0           0                1   \n",
        "A0005                0                  0           0                1   \n",
        "\n",
        "                             child_name                 mother_name  \\\n",
        "case_id                                                               \n",
        "A0001              Remas Mahmoud Jbarah                 Huda Katalo   \n",
        "A0002          Majed Abdel Kareem Majed       Noor SHa'aban Mahmood   \n",
        "A0003        Rayyan Jamal Muhyi Al.Deen  SAra Hussein Muhyi Al.Deen   \n",
        "A0004    Hanan Mohd Mustapha Abu Othman            Kawla Abu Shanab   \n",
        "A0005          Yara Mahmoud Azmi Ismael             Suha Abdel Aziz   \n",
        "\n",
        "        mother_birth_date mother_record  mother_nationality  \\\n",
        "case_id                                                       \n",
        "A0001          1976-01-21           NaN                   3   \n",
        "A0002          1989-09-09           NaN                   3   \n",
        "A0003          1965-01-01           NaN                   1   \n",
        "A0004          1983-10-31           NaN                   1   \n",
        "A0005          1986-02-28           NaN                   1   \n",
        "\n",
        "        other_mother_nationality  father_nationality other_father_nationality  \\\n",
        "case_id                                                                         \n",
        "A0001                        NaN                   1                      NaN   \n",
        "A0002                        NaN                   1                      NaN   \n",
        "A0003                        NaN                   1                      NaN   \n",
        "A0004                        NaN                   1                      NaN   \n",
        "A0005                        NaN                   1                      NaN   \n",
        "\n",
        "        child_record  newborn_id newborn_id_number  adm_ari  adm_apnea  \\\n",
        "case_id                                                                  \n",
        "A0001     2002048926           3               NaN        0          0   \n",
        "A0002     2002043480           3               NaN        0          0   \n",
        "A0003     2001860539           3               NaN        0          0   \n",
        "A0004     2001953536           3               NaN        0          0   \n",
        "A0005     2002040652           3               NaN        0          0   \n",
        "\n",
        "         adm_asthma  adm_bronchiolitis  adm_bronchopneumo      \n",
        "case_id                                                        \n",
        "A0001             0                  0                  0 ...  \n",
        "A0002             0                  1                  0 ...  \n",
        "A0003             0                  0                  0 ...  \n",
        "A0004             0                  0                  1 ...  \n",
        "A0005             0                  0                  0 ...  \n",
        "\n",
        "[5 rows x 414 columns]"
       ]
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     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Breakdown by:\n",
      "\n",
      "- month of age (and greater than 1 year)\n",
      "- sex\n",
      "- birthweight\n",
      "- prematurity\n",
      "- feeding: % with any breastfeeding\n",
      "- maternal ed\n",
      "- number in household\n",
      "- daycare\n",
      "- smoking exposure: cigarette, maternal during pregnacy, nargalia\n",
      "- % with comorbidity: past medical history\n",
      "- nationality: jordanian, palestinean, syrian, egptian, other\n",
      "- diagnosis: pneumo, bronchiolitis, bronchopnewumonia\n",
      "\n",
      "Severity measures:\n",
      "\n",
      "- length of stay\n",
      "- ICU\n",
      "- O2\n",
      "- Mechanical vent.\n",
      "- days of symptoms before admission\n",
      "- % with antibiotics prior\n",
      "- % with antibiotics during\n",
      "\n",
      "Columns: \n",
      "\n",
      "- RSV, MPV, Rhino, Influenza A/B/C, Adeno, Parinfluenza, Hospitalzied pop, no virus"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Positive culture lookup\n",
      "pcr_lookup = {'pcr_result___1': 'RSV',\n",
      "'pcr_result___2': 'HMPV',\n",
      "'pcr_result___3': 'flu A',\n",
      "'pcr_result___4': 'flu B',\n",
      "'pcr_result___5': 'rhino',\n",
      "'pcr_result___6': 'PIV1',\n",
      "'pcr_result___7': 'PIV2',\n",
      "'pcr_result___8': 'PIV3',\n",
      "'pcr_result___13': 'H1N1',\n",
      "'pcr_result___14': 'H3N2',\n",
      "'pcr_result___15': 'Swine',\n",
      "'pcr_result___16': 'Swine H1',\n",
      "'pcr_result___17': 'flu C',\n",
      "'pcr_result___18': 'Adeno'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['RSV'] = hospitalized.pcr_result___1.astype(bool)\n",
      "hospitalized['HMPV'] = hospitalized.pcr_result___2.astype(bool)\n",
      "hospitalized['Rhino'] = hospitalized.pcr_result___5.astype(bool)\n",
      "hospitalized['Influenza'] = hospitalized.pcr_result___3 | hospitalized.pcr_result___4\n",
      "hospitalized['Adeno'] = hospitalized.pcr_result___18.astype(bool)\n",
      "hospitalized['PIV'] = hospitalized.pcr_result___6 | hospitalized.pcr_result___7 | hospitalized.pcr_result___8\n",
      "hospitalized['No virus'] = hospitalized[pcr_lookup.keys()].sum(1) == 0\n",
      "hospitalized['All'] = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "viruses = ['RSV', 'HMPV', 'Rhino', 'Influenza', 'Adeno', 'PIV', 'No virus', 'All']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
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     "input": [
      "from scipy.stats import beta\n",
      "from scipy.stats import norm\n",
      "\n",
      "def binomial_hpdr(x, pct=0.95, a=1, b=1, n_pbins=1e3):\n",
      "    \"\"\"\n",
      "    Function computes the posterior mode along with the upper and lower bounds of the\n",
      "    **Highest Posterior Density Region**.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    x: data (boolean)\n",
      "    pct: the size of the confidence interval (between 0 and 1)\n",
      "    a: the alpha hyper-parameter for the Beta distribution used as a prior (Default=1)\n",
      "    b: the beta hyper-parameter for the Beta distribution used as a prior (Default=1)\n",
      "    n_pbins: the number of bins to segment the p_range into (Default=1e3)\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    A tuple that contains the mode as well as the lower and upper bounds of the interval\n",
      "    (mode, lower, upper)\n",
      "\n",
      "    \"\"\"\n",
      "    n, N = sum(x), len(x)\n",
      "    # fixed random variable object for posterior Beta distribution\n",
      "    rv = beta(n+a, N-n+b)\n",
      "    # determine the mode and standard deviation of the posterior\n",
      "    stdev = rv.stats('v')**0.5\n",
      "    mode = (n+a-1.)/(N+a+b-2.)\n",
      "    # compute the number of sigma that corresponds to this confidence\n",
      "    # this is used to set the rough range of possible success probabilities\n",
      "    n_sigma = np.ceil(norm.ppf( (1+pct)/2. ))+1\n",
      "    # set the min and max values for success probability \n",
      "    max_p = mode + n_sigma * stdev\n",
      "    if max_p > 1:\n",
      "        max_p = 1.\n",
      "    min_p = mode - n_sigma * stdev\n",
      "    if min_p > 1:\n",
      "        min_p = 1.\n",
      "    # make the range of success probabilities\n",
      "    p_range = np.linspace(min_p, max_p, n_pbins+1)\n",
      "    # construct the probability mass function over the given range\n",
      "    if mode > 0.5:\n",
      "        sf = rv.sf(p_range)\n",
      "        pmf = sf[:-1] - sf[1:]\n",
      "    else:\n",
      "        cdf = rv.cdf(p_range)\n",
      "        pmf = cdf[1:] - cdf[:-1]\n",
      "    # find the upper and lower bounds of the interval \n",
      "    sorted_idxs = np.argsort( pmf )[::-1]\n",
      "    cumsum = np.cumsum( np.sort(pmf)[::-1] )\n",
      "    j = np.argmin( np.abs(cumsum - pct) )\n",
      "    upper = p_range[ (sorted_idxs[:j+1]).max()+1 ]\n",
      "    lower = p_range[ (sorted_idxs[:j+1]).min() ]    \n",
      "\n",
      "    return mode, lower, upper"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Month of age"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_by_var(var, cond=True, title=None):\n",
      "    fig, axes = plt.subplots(2, 4, sharex=True, sharey=True, figsize=(16,8))\n",
      "    for i,ax in enumerate(axes.ravel()):\n",
      "        v = viruses[i]\n",
      "        hospitalized[hospitalized[v] & cond][var].hist(ax=ax, grid=False)\n",
      "        ax.set_xlabel(v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_by_var('age_months', (hospitalized.age_months>0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xu96V3/7t386ECRPysY99LOvWrUt/f3+qq6uTjHx58Le+9a1ceumlQ6sb/s2/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\nHvmPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ecaaed0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def describe_by_var(var, round_to=1):\n",
      "    return(pd.concat({v:hospitalized[hospitalized[v]][var].describe().round(round_to) for v in viruses}, axis=1)[viruses].T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe_by_var('age_months')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>min</th>\n",
        "      <th>25%</th>\n",
        "      <th>50%</th>\n",
        "      <th>75%</th>\n",
        "      <th>max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>RSV</th>\n",
        "      <td> 1397</td>\n",
        "      <td> 4.8</td>\n",
        "      <td> 4.8</td>\n",
        "      <td>-11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  7</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HMPV</th>\n",
        "      <td>  273</td>\n",
        "      <td> 6.6</td>\n",
        "      <td> 5.7</td>\n",
        "      <td> -6</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td>  9</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rhino</th>\n",
        "      <td> 1238</td>\n",
        "      <td> 5.2</td>\n",
        "      <td> 5.4</td>\n",
        "      <td> -7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  7</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Influenza</th>\n",
        "      <td>  104</td>\n",
        "      <td> 7.0</td>\n",
        "      <td> 6.4</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 12</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adeno</th>\n",
        "      <td>  475</td>\n",
        "      <td> 6.8</td>\n",
        "      <td> 6.0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIV</th>\n",
        "      <td>  175</td>\n",
        "      <td> 6.1</td>\n",
        "      <td> 5.7</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 10</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>No virus</th>\n",
        "      <td>  588</td>\n",
        "      <td> 4.6</td>\n",
        "      <td> 5.8</td>\n",
        "      <td> -2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  7</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <td> 3169</td>\n",
        "      <td> 5.3</td>\n",
        "      <td> 5.5</td>\n",
        "      <td>-11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  8</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "           count  mean  std  min  25%  50%  75%  max\n",
        "RSV         1397   4.8  4.8  -11    1    3    7   23\n",
        "HMPV         273   6.6  5.7   -6    2    5    9   23\n",
        "Rhino       1238   5.2  5.4   -7    1    3    7   23\n",
        "Influenza    104   7.0  6.4    0    1    5   12   23\n",
        "Adeno        475   6.8  6.0    0    2    5   11   23\n",
        "PIV          175   6.1  5.7    0    2    3   10   22\n",
        "No virus     588   4.6  5.8   -2    1    2    7   23\n",
        "All         3169   5.3  5.5  -11    1    3    8   23\n",
        "\n",
        "[8 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Birth weight"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_by_var('birth_wt_child')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Vw5nrcMuMXrtb6Zttf7SlSXPnzs3999+fj370o2XXB2PV66+XkyZNOuYlgUfq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e21fc50>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe_by_var('birth_wt_child', round_to=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>min</th>\n",
        "      <th>25%</th>\n",
        "      <th>50%</th>\n",
        "      <th>75%</th>\n",
        "      <th>max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>RSV</th>\n",
        "      <td> 1396</td>\n",
        "      <td> 2.99</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 0.7</td>\n",
        "      <td> 2.6</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 4.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HMPV</th>\n",
        "      <td>  273</td>\n",
        "      <td> 2.98</td>\n",
        "      <td> 0.69</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 2.6</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rhino</th>\n",
        "      <td> 1238</td>\n",
        "      <td> 2.96</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 0.7</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Influenza</th>\n",
        "      <td>  104</td>\n",
        "      <td> 3.00</td>\n",
        "      <td> 0.62</td>\n",
        "      <td> 1.1</td>\n",
        "      <td> 2.7</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 4.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adeno</th>\n",
        "      <td>  475</td>\n",
        "      <td> 2.96</td>\n",
        "      <td> 0.68</td>\n",
        "      <td> 0.9</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 4.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIV</th>\n",
        "      <td>  175</td>\n",
        "      <td> 2.99</td>\n",
        "      <td> 0.69</td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 2.6</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 4.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>No virus</th>\n",
        "      <td>  587</td>\n",
        "      <td> 2.96</td>\n",
        "      <td> 0.64</td>\n",
        "      <td> 1.1</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.4</td>\n",
        "      <td> 5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <td> 3167</td>\n",
        "      <td> 2.97</td>\n",
        "      <td> 0.66</td>\n",
        "      <td> 0.7</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 5.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "           count  mean   std  min  25%  50%  75%  max\n",
        "RSV         1396  2.99  0.65  0.7  2.6    3  3.5  4.9\n",
        "HMPV         273  2.98  0.69  0.8  2.6    3  3.5  5.0\n",
        "Rhino       1238  2.96  0.67  0.7  2.5    3  3.5  5.0\n",
        "Influenza    104  3.00  0.62  1.1  2.7    3  3.5  4.2\n",
        "Adeno        475  2.96  0.68  0.9  2.5    3  3.5  4.6\n",
        "PIV          175  2.99  0.69  1.0  2.6    3  3.5  4.6\n",
        "No virus     587  2.96  0.64  1.1  2.5    3  3.4  5.0\n",
        "All         3167  2.97  0.66  0.7  2.5    3  3.5  5.0\n",
        "\n",
        "[8 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Sex"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['male'] = hospitalized.sex=='M'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def binomial_estimates(var):\n",
      "    estimates = pd.DataFrame({v: binomial_hpdr(hospitalized[hospitalized[v]][var]) for v in viruses}).T\n",
      "    estimates.columns = 'Mode', 'Lower', 'Upper'\n",
      "    return(estimates)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex_estimates = binomial_estimates('male')\n",
      "sex_estimates"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Mode</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Adeno</th>\n",
        "      <td> 0.627368</td>\n",
        "      <td> 0.583303</td>\n",
        "      <td> 0.669974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <td> 0.603660</td>\n",
        "      <td> 0.586568</td>\n",
        "      <td> 0.620596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HMPV</th>\n",
        "      <td> 0.575092</td>\n",
        "      <td> 0.516167</td>\n",
        "      <td> 0.632767</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Influenza</th>\n",
        "      <td> 0.557692</td>\n",
        "      <td> 0.462313</td>\n",
        "      <td> 0.650191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>No virus</th>\n",
        "      <td> 0.608844</td>\n",
        "      <td> 0.569090</td>\n",
        "      <td> 0.647754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIV</th>\n",
        "      <td> 0.588571</td>\n",
        "      <td> 0.515072</td>\n",
        "      <td> 0.659414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RSV</th>\n",
        "      <td> 0.596994</td>\n",
        "      <td> 0.571115</td>\n",
        "      <td> 0.622479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rhino</th>\n",
        "      <td> 0.609855</td>\n",
        "      <td> 0.582519</td>\n",
        "      <td> 0.636774</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "               Mode     Lower     Upper\n",
        "Adeno      0.627368  0.583303  0.669974\n",
        "All        0.603660  0.586568  0.620596\n",
        "HMPV       0.575092  0.516167  0.632767\n",
        "Influenza  0.557692  0.462313  0.650191\n",
        "No virus   0.608844  0.569090  0.647754\n",
        "PIV        0.588571  0.515072  0.659414\n",
        "RSV        0.596994  0.571115  0.622479\n",
        "Rhino      0.609855  0.582519  0.636774\n",
        "\n",
        "[8 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_prop_est(estimates, intervals, var_name=''):\n",
      "    n = len(estimates)\n",
      "    x = range(n)\n",
      "    err = np.abs(intervals.T.values - estimates.values)\n",
      "    plt.errorbar(x=x, y=estimates, yerr=err, fmt='o')\n",
      "    axes = plt.gca()\n",
      "    axes.set_xticks(x)\n",
      "    axes.set_xticklabels(sex_estimates.index, rotation=45)\n",
      "    axes.set_xlim(-1, n)\n",
      "    axes.set_ylabel('Proportion {}'.format(var_name))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_prop_est(sex_estimates.Mode, sex_estimates[['Lower', 'Upper']], 'male')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GNOOrV6+GTnejX7tAIICzszNsbW1NXyEREVmUwaDw9PSERqOBp6cnXF1dYWNjY666iIjI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\nCiIiMohBQUREBv0/XUR/SdO7vasAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112954710>"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Past medical history\n",
      "\n",
      "Using the completment of `no_hx`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['past_medical_history'] = hospitalized.no_hx.astype(bool)-True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "past_med_estimates = binomial_estimates('past_medical_history')\n",
      "past_med_estimates"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Mode</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Adeno</th>\n",
        "      <td> 0.113684</td>\n",
        "      <td> 0.087214</td>\n",
        "      <td> 0.144274</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <td> 0.101294</td>\n",
        "      <td> 0.091125</td>\n",
        "      <td> 0.112138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HMPV</th>\n",
        "      <td> 0.091575</td>\n",
        "      <td> 0.061248</td>\n",
        "      <td> 0.129616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Influenza</th>\n",
        "      <td> 0.153846</td>\n",
        "      <td> 0.093610</td>\n",
        "      <td> 0.230898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>No virus</th>\n",
        "      <td> 0.130952</td>\n",
        "      <td> 0.105372</td>\n",
        "      <td> 0.159793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIV</th>\n",
        "      <td> 0.120000</td>\n",
        "      <td> 0.077566</td>\n",
        "      <td> 0.173561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RSV</th>\n",
        "      <td> 0.064424</td>\n",
        "      <td> 0.052362</td>\n",
        "      <td> 0.078106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rhino</th>\n",
        "      <td> 0.103393</td>\n",
        "      <td> 0.087273</td>\n",
        "      <td> 0.121176</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "               Mode     Lower     Upper\n",
        "Adeno      0.113684  0.087214  0.144274\n",
        "All        0.101294  0.091125  0.112138\n",
        "HMPV       0.091575  0.061248  0.129616\n",
        "Influenza  0.153846  0.093610  0.230898\n",
        "No virus   0.130952  0.105372  0.159793\n",
        "PIV        0.120000  0.077566  0.173561\n",
        "RSV        0.064424  0.052362  0.078106\n",
        "Rhino      0.103393  0.087273  0.121176\n",
        "\n",
        "[8 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_prop_est(past_med_estimates.Mode, past_med_estimates[['Lower', 'Upper']], 'with past medical history')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NmzdTX1/PmDFjuHLlCrm5uRQVFTFs2DCTtsgW4mYkKESH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117bddf10>"
       ]
      }
     ],
     "prompt_number": 134
    }
   ],
   "metadata": {}
  }
 ]
}