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fonnesbeck/Epidemiology RW.ipynb

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Epidemiology RW.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"import seaborn as sb\n",
"import pymc as pm\n",
"sb.set_style(\"white\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/io/parsers.py:1170: DtypeWarning: Columns (140,142,144,146,148,181,206,212,213,263,282,283,284,298,299) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"data": {
"text/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>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>...</th>\n",
" <th>was_whole_blood_obtained_f</th>\n",
" <th>date</th>\n",
" <th>whole_blood_complete</th>\n",
" <th>age_months</th>\n",
" <th>length_of_stay</th>\n",
" <th>gest_age</th>\n",
" <th>death</th>\n",
" <th>hospitalized_vitamin_d</th>\n",
" <th>wheezing_ind</th>\n",
" <th>sex</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",
" <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>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>F</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>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>M</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>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>40</td>\n",
" <td>False</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>F</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>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>F</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>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>False</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 414 columns</p>\n",
"</div>"
],
"text/plain": [
" 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 ... was_whole_blood_obtained_f date \\\n",
"case_id ... \n",
"A0001 NaN ... NaN NaN \n",
"A0002 NaN ... NaN NaN \n",
"A0003 NaN ... NaN NaN \n",
"A0004 NaN ... NaN NaN \n",
"A0005 NaN ... NaN NaN \n",
"\n",
" whole_blood_complete age_months length_of_stay gest_age death \\\n",
"case_id \n",
"A0001 0 1 6 40 False \n",
"A0002 0 1 5 40 False \n",
"A0003 0 11 10 40 False \n",
"A0004 0 7 3 38 False \n",
"A0005 0 2 1 39 False \n",
"\n",
" hospitalized_vitamin_d wheezing_ind sex \n",
"case_id \n",
"A0001 3 0 F \n",
"A0002 4 1 M \n",
"A0003 35 0 F \n",
"A0004 2 1 F \n",
"A0005 6 0 F \n",
"\n",
"[5 rows x 414 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized = pd.read_csv('data/hospitalized.csv', index_col=0)\n",
"hospitalized.head()"
]
},
{
"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",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['no_hx',\n",
" 'diabetes_hx',\n",
" 'heart_hx',\n",
" 'kidney_hx',\n",
" 'downs_hx',\n",
" 'sickle_hx',\n",
" 'cancer_hx',\n",
" 'cf_hx',\n",
" 'genetic_hx',\n",
" 'cp_hx',\n",
" 'seizure_hx',\n",
" 'neuro_hx',\n",
" 'mr_hx',\n",
" 'asthma_hx',\n",
" 'immuno_hx',\n",
" 'liver_hx',\n",
" 'gerd_hx',\n",
" 'diarrhea_hx',\n",
" 'other_hx',\n",
" 'spec_hx',\n",
" 'no_asthma_hx',\n",
" 'no_allergy_hx',\n",
" 'no_eczema_hx']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[v for v in hospitalized.columns if v.endswith('hx')]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized['asthma'] = (hospitalized.child_asthma==1) | hospitalized.asthma_hx\n",
"hospitalized.asthma.sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 2848\n",
"0 320\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized.no_hx.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"55"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"((hospitalized.no_hx==1) & (hospitalized.asthma.astype(bool))).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Past medical history calculated as those who claim to have past medical history and those who claim to have no past medical history and reported asthma."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"375"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized['past medical history'] = (~hospitalized.no_hx.astype(bool) | \n",
" ((hospitalized.no_hx==1) & (hospitalized.asthma.astype(bool))))\n",
"hospitalized['past medical history'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 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'}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"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.pcr_result___17)\n",
"hospitalized['Adeno'] = hospitalized.pcr_result___18.astype(bool)\n",
"hospitalized['PIV'] = (hospitalized.pcr_result___6 | hospitalized.pcr_result___7 | \n",
" hospitalized.pcr_result___8)\n",
"hospitalized['No virus'] = hospitalized[list(pcr_lookup.keys())].sum(1) == 0\n",
"hospitalized['All'] = True"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"viruses = ['RSV', 'HMPV', 'Rhino', 'Influenza', 'Adeno', 'PIV', 'No virus', 'All']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'RSV'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_rsv_lookup = pcr_lookup.copy()\n",
"non_rsv_lookup.pop('pcr_result___1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Identify individuals with coinfection"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['coinfection'] = hospitalized[list(pcr_lookup.keys())].sum(1) > 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Virus frequency by coinfection status"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.2297979797979798"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized[(~hospitalized.coinfection) & (hospitalized.RSV)].shape[0]/hospitalized.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1135d17f0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113351fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"virus_by_coinf = hospitalized.groupby('coinfection')[viruses[:-2]].sum()/hospitalized.shape[0]\n",
"virus_by_coinf.T.plot(kind='bar', stacked=True, grid=False, \n",
" color=sb.color_palette('Paired', 2)[::-1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RSV 1397\n",
"HMPV 273\n",
"Rhino 1238\n",
"Influenza 119\n",
"Adeno 475\n",
"PIV 175\n",
"No virus 587\n",
"All 3168\n",
"dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized[viruses].sum(0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"other_virus_index = (hospitalized[list(non_rsv_lookup.keys())].sum(1) > 0).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['RSV'] = hospitalized['pcr_result___1']\n",
"hospitalized['non-RSV virus'] = (hospitalized['pcr_result___1']==0) & other_virus_index\n",
"hospitalized['no virus'] = (hospitalized['pcr_result___1']==0) & (other_virus_index==0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized[\"prev_cond\"] = (hospitalized[[c for c in hospitalized.columns if c.endswith('hx') \n",
" and not c.startswith('no_')]].sum(1) > 0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['male'] = (hospitalized.sex=='M').astype(int)\n",
"age_groups = pd.get_dummies(pd.cut(hospitalized.age_months, [0,1,11,23]))\n",
"age_groups.index = hospitalized.index\n",
"age_groups.columns = 'under 2 months', '2-11 months', '12-23 months'\n",
"hospitalized = hospitalized.join(age_groups)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nationality_lookup = {1: 'Jordanian', 2: 'Egyptian', 3: 'Palestinian', 4: 'Iraqi', 5: 'Syrian', \n",
" 6: 'Sudanese', 7: 'Russian', 8: 'Asian', 9: 'Other'}\n",
"hospitalized['nationality'] = hospitalized.mother_nationality.replace(nationality_lookup)\n",
"hospitalized['Jordanian'] = (hospitalized.nationality=='Jordanian').astype(int)\n",
"hospitalized['Palestinian'] = (hospitalized.nationality=='Palestinian').astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['vitamin D < 20'] = (hospitalized.hospitalized_vitamin_d < 20).astype(int)\n",
"hospitalized.loc[hospitalized.hospitalized_vitamin_d.isnull(), 'vitamin D < 20'] = np.nan\n",
"hospitalized['vitamin D < 11'] = (hospitalized.hospitalized_vitamin_d < 11).astype(int)\n",
"hospitalized.loc[hospitalized.hospitalized_vitamin_d.isnull(), 'vitamin D < 11'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['any_cigarette'] = (hospitalized.cigarette_smokers > 0).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hospitalized['any_smoke'] = (hospitalized.cigarette_smokers.astype(bool) | \n",
" hospitalized.nargila_smokers.astype(bool))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.76546717171717171"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized.any_smoke.mean()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['premature'] = (hospitalized.gest_age < 37).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11380f0b8>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1135d1710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hospitalized.length_of_stay.hist(bins=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Diagnosis"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['ros'] = hospitalized.adm_sepsis | hospitalized.adm_febrile"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['pertussis-like cough'] = hospitalized.adm_pertussis | hospitalized.adm_cough"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hospitalized['brochopneumonia'] = hospitalized.adm_bronchopneumo"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hospitalized['bronchiolitis'] = hospitalized.adm_bronchiolitis"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hospitalized['pneumonia'] = hospitalized.adm_pneumo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rate Estimation"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 3168\n",
"unique 794\n",
"top 2011-02-07 00:00:00\n",
"freq 17\n",
"first 2010-03-15 00:00:00\n",
"last 2013-03-30 00:00:00\n",
"Name: admission_date, dtype: object"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized.admission_date = pd.to_datetime(hospitalized.admission_date)\n",
"hospitalized.admission_date.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Age groups:\n",
"\n",
"- Under 2 months\n",
"- 2-5 mo.\n",
"- 6-11 mo.\n",
"- Over 11 mo."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"age_groups = pd.get_dummies(pd.cut(hospitalized.age_months, [0,1,5,11,24], include_lowest=True))\n",
"age_groups.index = hospitalized.index\n",
"age_groups.columns = 'age_under_2', 'age_2_5', 'age_6_11', 'age_over_11'\n",
"hospitalized = hospitalized.join(age_groups)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"age_group_lookup = {'age_under_2': '<2', \n",
" 'age_2_5': '2-5', \n",
" 'age_6_11': '6-11', \n",
" 'age_over_11': '>11'}"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"customcmap = sb.color_palette(\"coolwarm\", 6)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x114241a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2,2, figsize=(10,6), sharey=True)\n",
"for i,ax in enumerate(np.ravel(axes)):\n",
" age_group = age_groups.columns[i]\n",
"\n",
" age_subset = hospitalized[hospitalized[age_group].astype(bool)]\n",
" age_virus = age_subset[viruses[:-2]].mean()\n",
" age_virus.T.plot(kind='bar', grid=False, ax=ax, \n",
" color=customcmap)\n",
" ax.set_title(age_group_lookup[age_group])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Population rate estimation\n",
"\n",
"Recode year to virus season:\n",
"\n",
"- 2011: March 2010 - March 2011\n",
"- 2012: Apr 2011 - Mar 2012\n",
"- 2013: April 2012 - Mar 2013"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2012 1191\n",
"2013 1179\n",
"2011 798\n",
"dtype: int64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized['virus_year'] = 2011\n",
"hospitalized.loc[(hospitalized.admission_date >= '2011-03-31') \n",
" & (hospitalized.admission_date <= '2012-03-31'), 'virus_year'] = 2012\n",
"hospitalized.loc[hospitalized.admission_date > '2012-03-31', 'virus_year'] = 2013\n",
"\n",
"hospitalized.virus_year.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode zones"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"zone_string = \"1, Amman Zone 1 | 2, Abdoun Zone 1 | 3, Abu Alanda Zone 3 | 4, Abu Nusair Zone 2 | 5, Airport street Zone 4 | 6, Al.Ashrafeyeh Zone 5 | 7, Al.Badya Zone 31 | 8, Al.Baqa'a Zone 22 | 9, Al.Ghour Zone 32 | 10, Al.Hashmi Zone 6 | 11, Al.Hezam Zone 29 | 12, Al.Hussein Camping Zone 1 | 13, Al.Istiklal Zone 1 | 14, Al.Jeeza Zone 8 | 15, Al.Joufeh Zone 5 | 16, Al.Karak Zone 35 | 17, Al.Lubban Zone 16 | 18, Al.Madenah Al.Reyadeyeh Zone 1 | 19, Al.Mahatta Zone 6 | 20, Al.Manarah Zone 5 | 21, Al.Mareikh Zone 5 | 22, Al.Marqab Zone 6 | 23, Al.Muhajreen Zone 5 | 24, Al.Musdar Zone 5 | 25, Al.Musherfeh Zone 15 | 26, Al.Naser Zone 6 | 27, Al.Natheef Zone 5 | 28, Al.Nuzha Zone 1 | 29, Al.Qastal Zone 10 | 30, Al.Qwesmeh Zone 5 | 31, Al.Shouneh Zone 32 | 32, Al.Taj Zone 5 | 33, Al.Taybeh Zone 7 | 34, Aqaba Zone 30 | 35, Arjan Zone 1 | 36, Bayader Wadi AL.Seer Zone 20 | 37, Bnayyat Zone 12 | 38, D.Al.Ameer Ali Zone 13 | 39, D.Al.Aqsa Zone 9 | 40, D.Haj Hasan Zone 9 | 41, Daheyet Al.Rasheed Zone 17 | 42, Daheyet al.Yasmeen Zone 1 | 43, Dead Sea Zone 32 | 44, Deer.Al.Ghbar Zone 1 | 45, Down Town (Al.Balad) Zone 1 | 46, Dra'a al.Qarbi Zone 1 | 47, Ein Al.Basha Zone 22 | 48, Ein Ghazal Zone 6 | 49, Eskan Al.Ameer Hashem Zone 29 | 50, Eskan Al.Ameer Talal Zone 29 | 51, Etha'a wal Telvesion Zone 9 | 52, Hay Al.Dabaybeh Zone 5 | 53, Hay Al.Tafayleh Zone 5 | 54, Hay Nazzal Zone 1 | 55, Huttein (shneler ) Refugee camping Zone 6 | 56, Iraq Al.Ameer Zone 23 | 57, Jabal AL.Akhdar Zone 1 | 58, Jabal Al.Ameer Faisal Zone 29 | 59, Jabal AL.Hadeed Zone 5 | 60, Jabal Al.Hussein Zone 1 | 61, Jabal Al.Qosoor Zone 1 | 62, Jabal Amman Zone 1 | 63, Jarash Zone 36 | 64, Jawa Zone 7 | 65, Juwaydeh Zone 14 | 66, Khalda Zone 18 | 67, Khreibt Al.Souk Zone 7 | 68, Ma'an Zone 31 | 69, Madaba Zone 34 | 70, Mafraq Zone 33 | 71, Marj Al.Hamam Zone 19 | 72, Marka Zone 6 | 73, Muqableen Zone 9 | 74, Muwaqqar Zone 28 | 75, Nadi Al.Sebaq Zone 6 | 76, Naur Zone 19 | 77, Petra Zone 30 | 78, Qatranah Zone 30 | 79, Raghadan Zone 1 | 80, Ras Al.Ein Zone 1 | 81, Rusayfah Zone 29 | 82, Saffout Zone 22 | 83, Sahab Zone 11 | 84, Salheyet Al.Abed Zone 6 | 85, Shafa Badran Zone 25 | 86, Sharq Al.Awsat Zone 11 | 87, Shemasani Zone 1 | 88, Street 30 Zone 5 | 89, Summaya Street Zone 5 | 90, Suweileh Zone 27 | 91, Tabarbour Zone 21 | 92, Tla'a al Ali Zone 17 | 93, Um Al.Heran Zone 11 | 94, Um Al.Summaq Zone 17 | 95, Um Nuwwara and Adan Zone 5 | 96, Um Uthayna Zone 1 | 97, Wadi Abdoun Zone 1 | 98, Wadi AL.Haddadeh Zone 1 | 99, Wadi AL.Hajar Zone 29 | 100, Wadi Al.Remam Zone 5 | 101, Wadi AL.Seer Zone 20 | 102, Wadi Saqra Zone 1 | 103, Wehdat Zone 11 | 104, Yadoudeh Zone 13 | 105, Yajouz Zone 26 | 106, Zarqa Zone 29 | 107, Zezya Zone 24\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"zones = [z.strip().split(',') for z in zone_string.split('|')]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"zone_dict = {int(n):int(s.strip().split(' ')[-1]) for n,s in zones}"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized['zone'] = hospitalized.city_zone.replace(zone_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define Amman zone"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hospitalized['amman_zone'] = hospitalized.zone<28"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hospitalized in Amman"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalized_amman = hospitalized[hospitalized.amman_zone]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3048, 451)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized_amman.shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalized_amman.index.is_unique"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hosp_age_counts = hospitalized_amman.groupby('admission_date')[age_groups.columns].sum().resample('M', how='sum').values"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pre_2013 = hospitalized_amman.admission_date < datetime(2013, 1, 1)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age_under_2</th>\n",
" <th>age_2_5</th>\n",
" <th>age_6_11</th>\n",
" <th>age_over_11</th>\n",
" </tr>\n",
" <tr>\n",
" <th>admission_date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-03-31</th>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-04-30</th>\n",
" <td>17</td>\n",
" <td>22</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-05-31</th>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-06-30</th>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-07-31</th>\n",
" <td>19</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-31</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td>32</td>\n",
" <td>58</td>\n",
" <td>34</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-02-28</th>\n",
" <td>54</td>\n",
" <td>67</td>\n",
" <td>39</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-31</th>\n",
" <td>40</td>\n",
" <td>31</td>\n",
" <td>18</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-04-30</th>\n",
" <td>23</td>\n",
" <td>37</td>\n",
" <td>26</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-05-31</th>\n",
" <td>24</td>\n",
" <td>34</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-06-30</th>\n",
" <td>25</td>\n",
" <td>17</td>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-31</th>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-08-31</th>\n",
" <td>21</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-09-30</th>\n",
" <td>17</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-10-31</th>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-11-30</th>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31</th>\n",
" <td>23</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-31</th>\n",
" <td>60</td>\n",
" <td>68</td>\n",
" <td>37</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-29</th>\n",
" <td>55</td>\n",
" <td>83</td>\n",
" <td>39</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-03-31</th>\n",
" <td>49</td>\n",
" <td>71</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-04-30</th>\n",
" <td>34</td>\n",
" <td>43</td>\n",
" <td>32</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-31</th>\n",
" <td>34</td>\n",
" <td>27</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-06-30</th>\n",
" <td>23</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-07-31</th>\n",
" <td>29</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td>21</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-09-30</th>\n",
" <td>13</td>\n",
" <td>17</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-10-31</th>\n",
" <td>16</td>\n",
" <td>20</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-30</th>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td>43</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age_under_2 age_2_5 age_6_11 age_over_11\n",
"admission_date \n",
"2010-03-31 15 16 14 3\n",
"2010-04-30 17 22 14 6\n",
"2010-05-31 12 16 11 2\n",
"2010-06-30 21 6 7 3\n",
"2010-07-31 19 6 2 2\n",
"2010-08-31 7 8 3 3\n",
"2010-09-30 10 5 4 2\n",
"2010-10-31 5 5 4 4\n",
"2010-11-30 5 5 8 7\n",
"2010-12-31 7 8 5 10\n",
"2011-01-31 32 58 34 21\n",
"2011-02-28 54 67 39 24\n",
"2011-03-31 40 31 18 23\n",
"2011-04-30 23 37 26 12\n",
"2011-05-31 24 34 11 17\n",
"2011-06-30 25 17 13 9\n",
"2011-07-31 20 16 10 10\n",
"2011-08-31 21 7 0 3\n",
"2011-09-30 17 8 5 8\n",
"2011-10-31 20 9 6 10\n",
"2011-11-30 10 14 7 14\n",
"2011-12-31 23 21 21 18\n",
"2012-01-31 60 68 37 37\n",
"2012-02-29 55 83 39 25\n",
"2012-03-31 49 71 37 33\n",
"2012-04-30 34 43 32 23\n",
"2012-05-31 34 27 15 11\n",
"2012-06-30 23 21 15 10\n",
"2012-07-31 29 18 8 14\n",
"2012-08-31 21 11 7 5\n",
"2012-09-30 13 17 15 10\n",
"2012-10-31 16 20 4 9\n",
"2012-11-30 11 17 5 9\n",
"2012-12-31 43 46 31 17"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_counts = hospitalized_amman[pre_2013].groupby('admission_date')[age_groups.columns].sum().resample('M', how='sum')\n",
"age_counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rates and demographics (via [World Bank](http://databank.worldbank.org)) and 2004 census data (via Jordan [Department of Statistics](http://www.dos.gov.jo/dos_home_e/main/population/census2004/group3/table_31.pdf))."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Jordan population, 2008-2012\n",
"population = 5786000, 5915000, 6046000, 6181000, 6318000\n",
"\n",
"# Interpolated population by gender and age, 2010-2012\n",
"female_0 = 93649, 95739, 96486\n",
"male_0 = 98435, 100525, 101160\n",
"female_1 = 87941, 93511, 93067\n",
"male_1 = 92548, 98306, 97763\n",
"\n",
"kids_0 = np.array(female_0) + np.array(male_0)\n",
"kids_1 = np.array(female_1) + np.array(male_1)\n",
"kids = kids_0 + kids_1\n",
"\n",
"kids_under6mo = kids_6to12mo = kids_0/2.\n",
"\n",
"# Proportion in Amman\n",
"amman_urban_2004 = 1784502.\n",
"jordan_2004 = 5103639.\n",
"amman_prop = amman_urban_2004 / jordan_2004\n",
"\n",
"# Birth rates (per 1000)\n",
"birth_rate = 29.665, 29.322, 28.869, 28.317, 27.699\n",
"\n",
"# Neonatal mortality (per 1000)\n",
"neonatal_mort = 12.7, 12.4, 12.1, 11.8, 11.5\n",
"\n",
"# Infant mortality (per 1000)\n",
"infant_mort = 18.4, 17.9, 17.3, 16.8, 16.4"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 174541.974, 175027.377, 175002.282])"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"births = np.array(population[-3:])/1000. * birth_rate[-3:]\n",
"births"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 87270.987 , 87513.6885, 87501.141 ])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"births_6m = births/2.\n",
"births_6m"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1509.7880751, 1470.2299668, 1435.0187124])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_6m = births_6m/1000. * infant_mort[-3:]\n",
"deaths_6m"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.3496528653378501"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amman_prop"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 29986.6489389 , 30085.34181971, 30093.26626638])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(births_6m - deaths_6m)*amman_prop"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 96042., 98132., 98823.],\n",
" [ 96042., 98132., 98823.],\n",
" [ 180489., 191817., 190830.],\n",
" [ 372573., 388081., 388476.]])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n = np.array((kids_under6mo, kids_6to12mo, kids_1, kids))\n",
"n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([192084, 196264, 197646])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kids_0"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.93963578, 0.97734174, 0.9655141 ])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kids_1/kids_0"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.3496528653378501"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"amman_prop"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_amman = np.floor(n*amman_prop).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 33581, 34312, 34553],\n",
" [ 33581, 34312, 34553],\n",
" [ 63108, 67069, 66724],\n",
" [130271, 135693, 135831]])"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_amman"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Monthly admissions in Ammaon"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"admissions_by_month = hospitalized_amman.groupby('admission_date')['child_name'].count().resample('1M', how='sum')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"admission_date\n",
"2010-03-31 48\n",
"2010-04-30 59\n",
"2010-05-31 41\n",
"2010-06-30 37\n",
"2010-07-31 29\n",
"Freq: M, Name: child_name, dtype: int64"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"admissions_by_month.head()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Dict to return pcr_result variable corresponding to virus\n",
"virus_lookup = {pcr_lookup[k]: k for k in pcr_lookup.keys()}"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Enrolling 5 days per week\n",
"p_enroll = 5./7"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1354, 451)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rsv_subset = hospitalized_amman[hospitalized_amman.RSV==1]\n",
"rsv_subset.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'age_under_2', 'age_2_5', 'age_6_11', 'age_over_11'"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['RSV', 'HMPV', 'Rhino', 'Influenza', 'Adeno', 'PIV', 'No virus', 'All']"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"viruses"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/core/frame.py:1819: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" \"DataFrame index.\", UserWarning)\n"
]
}
],
"source": [
"virus_subset = hospitalized_amman[hospitalized['RSV']==1].copy()\n",
"virus_subset['admission_year'] = virus_subset.admission_date.apply(lambda x: x.year)\n",
"_under_6 = virus_subset[virus_subset.age_under_2.astype(bool) | virus_subset.age_2_5].groupby('admission_year')['child_name'].count()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"_6_11 = virus_subset[virus_subset.age_6_11.astype(bool)].groupby('admission_year')['child_name'].count()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"_over_11 = virus_subset[virus_subset.age_over_11.astype(bool)].groupby('admission_year')['child_name'].count()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"_all = virus_subset.groupby('admission_year')['child_name'].count()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"admission_date\n",
"2010-03-31 28\n",
"2010-04-30 20\n",
"2010-05-31 3\n",
"2010-06-30 2\n",
"2010-09-30 1\n",
"2010-10-31 1\n",
"2010-11-30 4\n",
"2010-12-31 14\n",
"2011-01-31 131\n",
"2011-02-28 145\n",
"2011-03-31 48\n",
"2011-04-30 13\n",
"2011-05-31 4\n",
"2011-06-30 1\n",
"2011-09-30 1\n",
"2011-10-31 1\n",
"2011-11-30 2\n",
"2011-12-31 24\n",
"2012-01-31 145\n",
"2012-02-29 138\n",
"2012-03-31 116\n",
"2012-04-30 71\n",
"2012-05-31 20\n",
"2012-06-30 14\n",
"2012-07-31 1\n",
"2012-10-31 1\n",
"2012-11-30 3\n",
"2012-12-31 89\n",
"2013-01-31 153\n",
"2013-02-28 114\n",
"2013-03-31 46\n",
"Name: mother_name, dtype: int64"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"virus_subset.set_index(virus_subset.admission_date).groupby([pd.TimeGrouper('M')]).count()['mother_name']"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>under 6 mo.</th>\n",
" <th>6-11 mo.</th>\n",
" <th>11-23 mo.</th>\n",
" <th>all under 2 yr.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>admission_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td>49</td>\n",
" <td>20</td>\n",
" <td>4</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>255</td>\n",
" <td>77</td>\n",
" <td>38</td>\n",
" <td>370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>400</td>\n",
" <td>113</td>\n",
" <td>85</td>\n",
" <td>598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>219</td>\n",
" <td>62</td>\n",
" <td>32</td>\n",
" <td>313</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" under 6 mo. 6-11 mo. 11-23 mo. all under 2 yr.\n",
"admission_year \n",
"2010 49 20 4 73\n",
"2011 255 77 38 370\n",
"2012 400 113 85 598\n",
"2013 219 62 32 313"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"virus_df = pd.concat([_under_6, _6_11, _over_11, _all], axis=1)\n",
"virus_df.columns = ('under 6 mo.', '6-11 mo.', '11-23 mo.', 'all under 2 yr.')\n",
"virus_df"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 49, 20, 4, 73, 255, 77, 38, 370, 400, 113, 85, 598, 219,\n",
" 62, 32, 313])"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"virus_df.values.ravel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use proportion in Amman to calculate proportion of kids in each age group and year in Amman"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def rate_model(virus):\n",
" \n",
" # Extract data subset for passed virus\n",
" virus_subset = hospitalized_amman[hospitalized_amman[virus]==1].copy()\n",
" \n",
" # Create data frame of age x year counts\n",
" u6 = virus_subset.age_under_2.astype(bool) | virus_subset.age_2_5\n",
" _under_6 = virus_subset[u6].groupby('virus_year')['child_name'].count()\n",
" _6_11 = virus_subset[virus_subset.age_6_11.astype(bool)].groupby('virus_year')['child_name'].count()\n",
" _over_11 = virus_subset[virus_subset.age_over_11.astype(bool)].groupby('virus_year')['child_name'].count()\n",
" _all = virus_subset.groupby('virus_year')['child_name'].count()\n",
" \n",
" virus_df = pd.concat([_under_6, _6_11, _over_11, _all], axis=1)\n",
" virus_df.columns = ('under 6 mo.', '6-11 mo.', '11-23 mo.', 'all ages')\n",
" \n",
" # Al Bashir hospital market share\n",
" market_share = pm.Uniform('market_share', 0.5, 0.6)\n",
" \n",
" # Prior probability\n",
" prev_virus = [pm.Beta('prev_virus_%i' %i, 1, 5) for i in range(virus_df.size)]\n",
" per_1000 = pm.Lambda('per_1000', lambda p=prev_virus: np.array(p)*1000)\n",
" \n",
" # Correct for 5 days of enrollment per week\n",
" p_hosp = market_share * (5./7)\n",
" \n",
" # RSV in Amman\n",
" n_hosp = pm.Binomial('n_hosp', n_amman.T.ravel(), p_hosp, value=n_amman.T.ravel()*0.2)\n",
"\n",
" # Likelihood for number with RSV in hospital (assumes Pr(hosp | RSV) = 1)\n",
" y_hosp = pm.Binomial('y_hosp', n_hosp, prev_virus, value=virus_df.values.ravel(), observed=True)\n",
" \n",
" return locals()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Influenza rates"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M = pm.MCMC(rate_model('Influenza'))"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 79.8 sec"
]
}
],
"source": [
"M.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 72.9 sec"
]
}
],
"source": [
"M.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"prevalence_labels = ['%s %s' % (year, age) for year in ('2011', '2012', '2013') \n",
" for age in ('under 6 mo.', '6-11 mo.', '12-23 mo.', 'all ages')]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"SUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11977d198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M.per_1000, custom_labels=prevalence_labels, main='Influenza (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.857 0.261 0.006 [ 0.381 1.352]\n",
"\t0.627 0.227 0.006 [ 0.228 1.069]\n",
"\t0.332 0.119 0.003 [ 0.131 0.578]\n",
"\t0.504 0.102 0.003 [ 0.314 0.706]\n",
"\t1.524 0.357 0.01 [ 0.889 2.23 ]\n",
"\t0.534 0.207 0.004 [ 0.163 0.937]\n",
"\t0.435 0.133 0.003 [ 0.186 0.695]\n",
"\t0.696 0.121 0.004 [ 0.476 0.937]\n",
"\t2.507 0.459 0.015 [ 1.633 3.396]\n",
"\t0.837 0.254 0.006 [ 0.366 1.334]\n",
"\t0.594 0.157 0.004 [ 0.304 0.905]\n",
"\t1.103 0.157 0.006 [ 0.822 1.419]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.438 0.673 0.825 1.013 1.44\n",
"\t0.266 0.464 0.601 0.763 1.126\n",
"\t0.141 0.246 0.318 0.405 0.6\n",
"\t0.323 0.432 0.497 0.567 0.721\n",
"\t0.929 1.261 1.492 1.756 2.293\n",
"\t0.213 0.387 0.503 0.648 1.017\n",
"\t0.214 0.34 0.418 0.517 0.738\n",
"\t0.49 0.612 0.688 0.771 0.957\n",
"\t1.708 2.179 2.483 2.8 3.487\n",
"\t0.406 0.66 0.811 0.99 1.41\n",
"\t0.328 0.483 0.582 0.693 0.936\n",
"\t0.824 0.992 1.098 1.206 1.423\n",
"\t\n"
]
}
],
"source": [
"M.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M.write_csv('Influenza_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HMPV rates"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 72.2 sec"
]
}
],
"source": [
"M_hmpv = pm.MCMC(rate_model('HMPV'))\n",
"# M_hmpv.\n",
"M_hmpv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 66.1 sec"
]
}
],
"source": [
"M_hmpv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x119fc3320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_hmpv.per_1000, custom_labels=prevalence_labels, main='HMPV (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t1.514 0.344 0.009 [ 0.88 2.205]\n",
"\t0.843 0.255 0.005 [ 0.372 1.339]\n",
"\t0.361 0.121 0.003 [ 0.148 0.599]\n",
"\t0.747 0.125 0.004 [ 0.528 1.021]\n",
"\t7.233 0.791 0.033 [ 5.727 8.785]\n",
"\t2.913 0.479 0.015 [ 2.016 3.893]\n",
"\t1.232 0.224 0.006 [ 0.821 1.673]\n",
"\t3.134 0.275 0.014 [ 2.602 3.661]\n",
"\t1.927 0.394 0.011 [ 1.208 2.722]\n",
"\t1.935 0.394 0.01 [ 1.204 2.729]\n",
"\t0.501 0.143 0.003 [ 0.235 0.774]\n",
"\t1.189 0.16 0.006 [ 0.879 1.487]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.908 1.277 1.491 1.72 2.267\n",
"\t0.412 0.662 0.817 0.998 1.397\n",
"\t0.171 0.271 0.349 0.433 0.633\n",
"\t0.527 0.658 0.741 0.827 1.021\n",
"\t5.776 6.675 7.201 7.742 8.857\n",
"\t2.037 2.582 2.884 3.227 3.935\n",
"\t0.831 1.074 1.216 1.377 1.692\n",
"\t2.626 2.936 3.126 3.316 3.703\n",
"\t1.226 1.646 1.902 2.176 2.756\n",
"\t1.245 1.662 1.917 2.179 2.793\n",
"\t0.263 0.4 0.489 0.588 0.823\n",
"\t0.901 1.077 1.18 1.292 1.522\n",
"\t\n"
]
}
],
"source": [
"M_hmpv.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M_hmpv.write_csv('HMPV_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rhino rates"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 150000 of 150000 complete in 97.0 sec"
]
}
],
"source": [
"M_rhino = pm.MCMC(rate_model('Rhino'))\n",
"M_rhino.sample(150000, 140000)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 150000 of 150000 complete in 95.9 sec"
]
}
],
"source": [
"M_rhino.sample(150000, 140000)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b863278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_rhino.per_1000, custom_labels=prevalence_labels, main='Rhino (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t12.805 1.126 0.051 [ 10.72 15.081]\n",
"\t3.505 0.55 0.016 [ 2.494 4.58 ]\n",
"\t1.281 0.238 0.006 [ 0.848 1.759]\n",
"\t4.806 0.359 0.019 [ 4.124 5.505]\n",
"\t20.541 1.461 0.078 [ 17.563 23.244]\n",
"\t5.739 0.689 0.024 [ 4.343 7.034]\n",
"\t2.654 0.335 0.011 [ 2.027 3.312]\n",
"\t7.909 0.482 0.029 [ 7.019 8.907]\n",
"\t27.348 1.741 0.105 [ 23.963 30.637]\n",
"\t8.21 0.831 0.032 [ 6.674 9.902]\n",
"\t3.025 0.357 0.013 [ 2.32 3.71]\n",
"\t10.494 0.59 0.038 [ 9.355 11.603]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t10.736 12.006 12.788 13.541 15.105\n",
"\t2.533 3.125 3.479 3.858 4.645\n",
"\t0.868 1.109 1.271 1.431 1.79\n",
"\t4.135 4.559 4.793 5.043 5.538\n",
"\t17.808 19.486 20.539 21.543 23.54\n",
"\t4.435 5.27 5.728 6.188 7.173\n",
"\t2.057 2.419 2.642 2.874 3.345\n",
"\t6.988 7.585 7.887 8.222 8.891\n",
"\t24.089 26.128 27.335 28.504 30.801\n",
"\t6.674 7.623 8.194 8.748 9.911\n",
"\t2.35 2.772 3.008 3.257 3.758\n",
"\t9.394 10.085 10.466 10.911 11.66\n",
"\t\n"
]
}
],
"source": [
"M_rhino.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_rhino.write_csv('Rhino_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>under 6 mo.</th>\n",
" <th>6-11 mo.</th>\n",
" <th>11-23 mo.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>163</td>\n",
" <td>44</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>267</td>\n",
" <td>74</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>359</td>\n",
" <td>107</td>\n",
" <td>76</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" under 6 mo. 6-11 mo. 11-23 mo.\n",
"virus_year \n",
"2011 163 44 30\n",
"2012 267 74 67\n",
"2013 359 107 76"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The data\n",
"M_rhino.virus_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RSV rates"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 72.2 sec"
]
}
],
"source": [
"M_rsv = pm.MCMC(rate_model('RSV'))\n",
"M_rsv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 72.6 sec"
]
}
],
"source": [
"M_rsv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"FAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b36d278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_rsv.per_1000, custom_labels=prevalence_labels, main='RSV (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t21.12 1.705 0.118 [ 17.914 24.408]\n",
"\t6.82 0.805 0.038 [ 5.35 8.415]\n",
"\t1.604 0.266 0.01 [ 1.112 2.117]\n",
"\t23.477 1.859 0.132 [ 19.802 26.795]\n",
"\t6.046 0.742 0.034 [ 4.659 7.5 ]\n",
"\t2.29 0.32 0.013 [ 1.689 2.909]\n",
"\t25.899 2.009 0.148 [ 22.041 29.442]\n",
"\t8.049 0.895 0.046 [ 6.453 9.859]\n",
"\t2.495 0.339 0.015 [ 1.85 3.163]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t17.979 19.899 21.076 22.3 24.517\n",
"\t5.363 6.256 6.796 7.367 8.446\n",
"\t1.131 1.421 1.584 1.768 2.17\n",
"\t20.054 22.132 23.405 24.851 27.108\n",
"\t4.692 5.523 6.001 6.54 7.563\n",
"\t1.714 2.067 2.272 2.499 2.954\n",
"\t22.313 24.397 25.845 27.345 29.759\n",
"\t6.446 7.416 8.008 8.637 9.853\n",
"\t1.88 2.257 2.485 2.711 3.216\n",
"\t\n"
]
}
],
"source": [
"M_rsv.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M_rsv.write_csv('RSV_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>under 6 mo.</th>\n",
" <th>6-11 mo.</th>\n",
" <th>11-23 mo.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>272</td>\n",
" <td>87</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>308</td>\n",
" <td>79</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>343</td>\n",
" <td>106</td>\n",
" <td>63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" under 6 mo. 6-11 mo. 11-23 mo.\n",
"virus_year \n",
"2011 272 87 38\n",
"2012 308 79 58\n",
"2013 343 106 63"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_rsv.virus_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adeno rates"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 71.6 sec"
]
}
],
"source": [
"M_adeno = pm.MCMC(rate_model('Adeno'))\n",
"M_adeno.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 72.6 sec"
]
}
],
"source": [
"M_adeno.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bd68518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_adeno.per_1000, custom_labels=prevalence_labels, main='Adeno (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t2.672 0.471 0.011 [ 1.774 3.582]\n",
"\t2.097 0.412 0.009 [ 1.333 2.925]\n",
"\t0.94 0.203 0.004 [ 0.57 1.338]\n",
"\t1.643 0.19 0.005 [ 1.289 2.018]\n",
"\t5.585 0.677 0.017 [ 4.31 6.928]\n",
"\t3.228 0.494 0.011 [ 2.34 4.224]\n",
"\t1.776 0.274 0.007 [ 1.271 2.338]\n",
"\t3.065 0.26 0.008 [ 2.565 3.571]\n",
"\t10.74 0.936 0.031 [ 8.993 12.639]\n",
"\t3.83 0.546 0.011 [ 2.754 4.895]\n",
"\t1.781 0.271 0.006 [ 1.259 2.303]\n",
"\t4.539 0.32 0.013 [ 3.903 5.15 ]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t1.825 2.339 2.647 2.965 3.672\n",
"\t1.37 1.805 2.06 2.349 2.974\n",
"\t0.6 0.794 0.92 1.064 1.377\n",
"\t1.296 1.512 1.633 1.769 2.032\n",
"\t4.368 5.101 5.565 6.023 7.002\n",
"\t2.345 2.879 3.211 3.537 4.239\n",
"\t1.28 1.59 1.758 1.95 2.358\n",
"\t2.57 2.883 3.062 3.239 3.585\n",
"\t8.965 10.12 10.72 11.318 12.631\n",
"\t2.836 3.44 3.802 4.18 5.0\n",
"\t1.293 1.59 1.767 1.956 2.346\n",
"\t3.919 4.325 4.54 4.749 5.174\n",
"\t\n"
]
}
],
"source": [
"M_adeno.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M_adeno.write_csv('Adeno_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>under 6 mo.</th>\n",
" <th>6-11 mo.</th>\n",
" <th>11-23 mo.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>32</td>\n",
" <td>25</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>70</td>\n",
" <td>40</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>136</td>\n",
" <td>48</td>\n",
" <td>43</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" under 6 mo. 6-11 mo. 11-23 mo.\n",
"virus_year \n",
"2011 32 25 21\n",
"2012 70 40 43\n",
"2013 136 48 43"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_adeno.virus_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PIV rates"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 71.6 sec"
]
}
],
"source": [
"M_piv = pm.MCMC(rate_model('PIV'))\n",
"M_piv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 66.9 sec"
]
}
],
"source": [
"M_piv.sample(100000, 90000)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1187f43c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M_piv.per_1000, custom_labels=prevalence_labels, main='PIV (per 1000)')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"per_1000:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t1.37 0.332 0.006 [ 0.776 2.032]\n",
"\t0.561 0.214 0.004 [ 0.193 0.989]\n",
"\t0.385 0.128 0.002 [ 0.159 0.646]\n",
"\t2.91 0.471 0.01 [ 2.015 3.87 ]\n",
"\t0.946 0.276 0.005 [ 0.466 1.524]\n",
"\t0.887 0.186 0.004 [ 0.571 1.287]\n",
"\t3.66 0.542 0.011 [ 2.663 4.798]\n",
"\t1.48 0.347 0.007 [ 0.811 2.141]\n",
"\t0.402 0.126 0.002 [ 0.171 0.65 ]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.801 1.132 1.338 1.58 2.088\n",
"\t0.227 0.406 0.535 0.684 1.063\n",
"\t0.176 0.293 0.375 0.462 0.678\n",
"\t2.05 2.581 2.884 3.207 3.924\n",
"\t0.476 0.747 0.92 1.111 1.547\n",
"\t0.567 0.75 0.875 1.004 1.285\n",
"\t2.664 3.297 3.629 4.01 4.8\n",
"\t0.886 1.229 1.448 1.693 2.26\n",
"\t0.195 0.314 0.386 0.477 0.686\n",
"\t\n"
]
}
],
"source": [
"M_piv.per_1000.summary()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M_piv.write_csv('PIV_per_1000', variables=['per_1000'])"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>under 6 mo.</th>\n",
" <th>6-11 mo.</th>\n",
" <th>11-23 mo.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>36</td>\n",
" <td>11</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>46</td>\n",
" <td>18</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" under 6 mo. 6-11 mo. 11-23 mo.\n",
"virus_year \n",
"2011 16 6 8\n",
"2012 36 11 21\n",
"2013 46 18 9"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_piv.virus_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hospitalization\n",
"\n",
"Import hospitalization data"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"spreadsheets = !ls data/Al-Bashir*\n",
"\n",
"data = []\n",
"col_names = 'date', 'admissions', 'admissions_2', 'resp_admissions', 'resp_admissions_2'\n",
"for spreadsheet in spreadsheets:\n",
" this_file = pd.ExcelFile(spreadsheet)\n",
" # Need to fix data entry error in years > 2011\n",
" dot = spreadsheet.find('.')\n",
" fix_columns = int(spreadsheet[dot-4:dot]) > 2011\n",
" for name in this_file.sheet_names:\n",
" d = this_file.parse(name)\n",
" d.columns = col_names\n",
" if fix_columns:\n",
" total = d['resp_admissions'] + d['resp_admissions_2']\n",
" d['resp_admissions_2'] = d['resp_admissions']\n",
" d['resp_admissions'] = total\n",
" data.append(d)\n",
" \n",
"hospitalizations = pd.concat(data).set_index('date')"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>admissions</th>\n",
" <th>admissions_2</th>\n",
" <th>resp_admissions</th>\n",
" <th>resp_admissions_2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-03-14</th>\n",
" <td>23</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-15</th>\n",
" <td>27</td>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-16</th>\n",
" <td>11</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-17</th>\n",
" <td>17</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-18</th>\n",
" <td>23</td>\n",
" <td>17</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" admissions admissions_2 resp_admissions resp_admissions_2\n",
"date \n",
"2010-03-14 23 21 15 14\n",
"2010-03-15 27 23 19 18\n",
"2010-03-16 11 8 6 3\n",
"2010-03-17 17 10 10 8\n",
"2010-03-18 23 17 12 10"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hospitalizations.head()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hospitalizations['virus_year'] = 2011\n",
"hospitalizations.loc[(hospitalizations.index >= datetime(2011, 3, 31)) \n",
" & (hospitalizations.index <= datetime(2012, 3, 31)), 'virus_year'] = 2012\n",
"hospitalizations.loc[hospitalizations.index > datetime(2012, 3, 31), 'virus_year'] = 2013"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"virus_year\n",
"2011 4123\n",
"2012 3865\n",
"2013 3776\n",
"Name: admissions_2, dtype: int64"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"under_2_hosp = hospitalizations.groupby('virus_year').sum()['admissions_2']\n",
"under_2_hosp"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rsv_by_year = rsv_subset.groupby('virus_year')[['age_under_2', 'age_2_5', 'age_6_11', 'age_over_11']]\n",
"rsv_counts = rsv_by_year.sum()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age_under_2</th>\n",
" <th>age_2_5</th>\n",
" <th>age_6_11</th>\n",
" <th>age_over_11</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>119</td>\n",
" <td>153</td>\n",
" <td>87</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>124</td>\n",
" <td>184</td>\n",
" <td>79</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>159</td>\n",
" <td>184</td>\n",
" <td>106</td>\n",
" <td>63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age_under_2 age_2_5 age_6_11 age_over_11\n",
"virus_year \n",
"2011 119 153 87 38\n",
"2012 124 184 79 58\n",
"2013 159 184 106 63"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rsv_counts"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age_under_2</th>\n",
" <th>age_2_5</th>\n",
" <th>age_6_11</th>\n",
" <th>age_over_11</th>\n",
" <th>admissions_2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_year</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>2011</th>\n",
" <td>119</td>\n",
" <td>153</td>\n",
" <td>87</td>\n",
" <td>38</td>\n",
" <td>4123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>124</td>\n",
" <td>184</td>\n",
" <td>79</td>\n",
" <td>58</td>\n",
" <td>3865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>159</td>\n",
" <td>184</td>\n",
" <td>106</td>\n",
" <td>63</td>\n",
" <td>3776</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age_under_2 age_2_5 age_6_11 age_over_11 admissions_2\n",
"virus_year \n",
"2011 119 153 87 38 4123\n",
"2012 124 184 79 58 3865\n",
"2013 159 184 106 63 3776"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"under2_rsv = pd.concat([rsv_counts, under_2_hosp], axis=1).dropna()\n",
"under2_rsv"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extract_virus(virus):\n",
" \n",
" virus_subset = hospitalized[hospitalized[virus]==1]\n",
" \n",
" virus_by_year = virus_subset.groupby('virus_year')[['age_under_2', 'age_2_5', 'age_6_11', 'age_over_11']]\n",
" virus_counts = virus_by_year.sum()\n",
" \n",
" return pd.concat([virus_counts, under_2_hosp], axis=1).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def hosp_subset(virus):\n",
" \n",
" data_subset = extract_virus(virus)\n",
" \n",
" admissions = data_subset.pop('admissions_2').values\n",
" data_subset = data_subset.values\n",
" \n",
" n_months, n_ages = data_subset.shape\n",
" \n",
" # Estimate age distribution of admissions\n",
" p_age = pm.Dirichlet('p_age', np.ones(4), value=data_subset.sum(0)[:-1]/data_subset.sum()) \n",
" counts = pm.Multinomial('counts', data_subset.sum(1), p_age, value=data_subset, observed=True)\n",
" \n",
" # Estimate denominators \n",
" n_hosp = []\n",
" for i,ni in enumerate(admissions):\n",
" \n",
" d = data_subset[i]\n",
" \n",
" n_init = (ni*(d/d.sum() if (d.sum()>10) else np.ones(4)*0.25)).astype(int)[:-1]\n",
" n_init = np.append(n_init, ni - n_init.sum())\n",
" \n",
" n_hosp.append(pm.Multinomial('n_hosp_{}'.format(i), ni, p_age, value=n_init))\n",
" \n",
" n_hosp_t = pm.Lambda('n_hosp_t', lambda x=n_hosp: np.array([[xij for xij in xi] for xi in x]).T)\n",
" \n",
" # Virus rates by age and time \n",
" mu_alpha = pm.Normal('mu_alpha', 0, 0.01, value=[0]*n_ages)\n",
" sigma_alpha = pm.Uniform('sigma_alpha', 0, 100, value=[10]*n_ages)\n",
" rho = pm.Uniform('rho', -1, 1, value=0)\n",
"\n",
" mu = [pm.Lambda('mu_{}'.format(i), lambda mu=mu_alpha: np.array([mu[i]]*n_months)) for i in range(n_ages)]\n",
" off_diag = np.eye(n_months, k=1)\n",
" Sigma = [pm.Lambda('Sigma_{}'.format(i), lambda s=sigma_alpha, r=rho: \n",
" (np.eye(n_months) + off_diag*r + off_diag.T*r)*(s[i]**2)) for i in range(n_ages)]\n",
"\n",
" alpha = [pm.MvNormalCov('alpha_{}'.format(i), mu[i], Sigma[i], value=[0]*n_months) for i in range(n_ages)]\n",
" \n",
" p_virus = [pm.Lambda('p_virus_{}'.format(i), lambda a=a: pm.invlogit(a)) for i,a in enumerate(alpha)]\n",
" \n",
" # Viral rate likelihood\n",
" @pm.observed\n",
" def rate_like(value=data_subset.T, p=p_virus, n=n_hosp_t):\n",
" return np.sum([pm.binomial_like(value[i], n[i], p[i]) for i in range(n_ages)])\n",
" \n",
" return(locals())"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M = pm.MCMC(hosp_subset('RSV'))"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 20000 of 20000 complete in 32.0 sec"
]
}
],
"source": [
"M.sample(20000, 10000)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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"5P8AUwpdPvQzu90AAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11a9d40f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot(M.p_age, custom_labels=['age_under_2', 'age_2_5', 'age_6_11'])"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11ad3a940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"date_labels = ['2011', '2012', '2013']\n",
"pm.Matplot.summary_plot(M.p_virus[0], custom_labels=date_labels, main='RSV rates')"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sb"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"age_group_labels = ['Under 2 mo.', '2-5 mo.', '6-11 mo.', '12-23 mo.']"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rsv_rates = pd.concat([pd.DataFrame({'rate':p.trace().T[t], \n",
" 'age': age_group_labels[i], \n",
" 'year': date_labels[t]}) for i,p in enumerate(M.p_virus) for t in range(3)])"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11a7a8780>"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11a7a84e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sb.factorplot('year', 'rate', 'age', rsv_rates, kind='box', aspect=1.75)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 20000 of 20000 complete in 31.5 sec"
]
}
],
"source": [
"M_rhino = pm.MCMC(hosp_subset('Rhino'))\n",
"M_rhino.sample(20000, 10000)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rhino_rates = pd.concat([pd.DataFrame({'rate':p.trace().T[t], \n",
" 'age': age_group_labels[i], \n",
" 'year': date_labels[t]}) for i,p in enumerate(M_rhino.p_virus) for t in range(3)])"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"66n0RzIAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b22e4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sb.set(style=\"white\", palette=\"muted\")\n",
"\n",
"fg = sb.factorplot('year', 'rate', 'age', rhino_rates, kind='box', aspect=1.75)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Production runs"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running RSV\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 77.9 sec\n",
"Running HMPV\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 83.4 sec\n",
"Running Rhino\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 79.5 sec\n",
"Running Influenza\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 78.9 sec\n",
"Running Adeno\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 78.6 sec\n",
"Running PIV\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 78.0 sec\n",
"Running No virus\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 78.0 sec\n",
"Running All\n",
" [-----------------100%-----------------] 50000 of 50000 complete in 78.2 sec"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b6db908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11a42c048>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11c13fe48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b58df60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1b04e0>"
]
},
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"output_type": "display_data"
},
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11a4a5ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b2a8710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11c0b4da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for virus in viruses:\n",
" \n",
" print('\\nRunning', virus)\n",
" M = pm.MCMC(hosp_subset(virus))\n",
" M.sample(50000, 40000)\n",
" \n",
" sb.set(style=\"white\", palette=\"muted\")\n",
" \n",
" virus_rates = pd.concat([pd.DataFrame({'rate':p.trace().T[t], \n",
" 'age': age_group_labels[i], \n",
" 'year': date_labels[t]}) for i,p in enumerate(M.p_virus) for t in range(3)])\n",
" \n",
" fg = sb.factorplot('year', 'rate', 'age', virus_rates, kind='box', aspect=1.75)\n",
" fg.savefig(virus)\n",
" \n",
" M.write_csv(virus, variables=[x.__name__ for x in M.p_virus])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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