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fonnesbeck/Severity.ipynb

Created July 16, 2018 20:35
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Severity.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pylab as plt\n",
"sns.set_context('notebook')\n",
"\n",
"seed_numbers = 20090425, 19700903"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import current dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_file = 'data/AGE Cases and Healthy Controls Merged with Lab Results Year 2 to 4 v05.23.2017.csv'\n",
"rotavirus_data = (pd.read_csv(data_file, low_memory=False, na_values=[88, 99, 777, 888, 999])\n",
" .rename(columns={'rotacdc':'rotavirus',\n",
" 'sapo':'sapovirus',\n",
" 'astro':'astrovirus',\n",
" 'noro':'norovirus'}))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(rotavirus_data.houtcome==0).sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rotavirus_data['hicu'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ct_cols = ['sapo_ct', 'astro_ct', 'noro_ct']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"virus_cols = ['rotavirus','sapovirus','astrovirus','norovirus']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specify columns needed to calculate score, and restrict to cases"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"rotavirus_data['virus_pos'] = rotavirus_data[['rotavirus', 'sapovirus', 'astrovirus', 'norovirus']].sum(1) > 0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"severity_cols = ['caseid',\n",
" 'virus_pos',\n",
" 'daysill',\n",
" 'diarrh',\n",
" 'diarrhstart',\n",
" 'diarrhdays',\n",
" 'diarrhepiso',\n",
" 'vomit',\n",
" 'vomitstart',\n",
" 'vomitdays',\n",
" 'vomitepiso',\n",
" 'fever',\n",
" 'feverstart',\n",
" 'feverhigh',\n",
" 'feverdays',\n",
" 'feverdecimal',\n",
" 'measureby',\n",
" 'measurothsp',\n",
" 'newvisit',\n",
" 'irtherapy',\n",
" 'irtherapydur',\n",
" 'settingnew',\n",
" 'provider',\n",
" 'outadm',\n",
" 'inpelig',\n",
" 'surday',\n",
" 'oralrehydra',\n",
" 'behave',\n",
" 'eyes',\n",
" 'takefluids',\n",
" 'skintest']\n",
"\n",
"severity_data = rotavirus_data.loc[rotavirus_data.case==1, severity_cols + virus_cols + ct_cols]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"severity_data['coinfection'] = (severity_data[virus_cols].sum(axis=1) > 1).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"severity_data['care_level'] = None\n",
"severity_data.loc[severity_data.provider==1, 'care_level'] = 1\n",
"severity_data.loc[severity_data.provider==2, 'care_level'] = 3\n",
"severity_data.loc[severity_data.provider==3, 'care_level'] = 4\n",
"severity_data.loc[severity_data.provider.isin([2,3]) & \n",
" (severity_data.outadm==1) &\n",
" (severity_data.inpelig==1) &\n",
" (severity_data.surday==1), 'care_level'] = 2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3 2179\n",
"4 1274\n",
"1 155\n",
"2 97\n",
"Name: care_level, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"severity_data.care_level.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Treatment variable"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"severity_data['treatment'] = 0\n",
"severity_data.loc[severity_data.care_level.isin([1,2]), 'treatment'] = 2\n",
"severity_data.loc[severity_data.care_level.isin([3,4]) &\n",
" (severity_data.oralrehydra==1) &\n",
" (severity_data.irtherapy==1), 'treatment'] = 1\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"severity_data['fever_high_c'] = severity_data.feverhigh.apply(lambda f: (f - 32) * 5 / 9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dehydration severity"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"severe = ((severity_data.eyes==2).astype(int)\n",
" + (severity_data.behave>3).astype(int)\n",
" + (severity_data.skintest==3).astype(int)\n",
" + (severity_data.takefluids==2).astype(int)) >= 2\n",
"\n",
"moderate = ((severity_data.eyes==2).astype(int)\n",
" + (severity_data.behave>2).astype(int)\n",
" + (severity_data.skintest>=2).astype(int)\n",
" + (severity_data.takefluids>=1).astype(int)) >= 2"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 1736\n",
"0 1004\n",
"3 965\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dehydration = severe.astype(int) + (moderate.astype(int)*2)\n",
"\n",
"dehydration.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization of score components"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>diarrhdays</th>\n",
" <th>diarrhepiso</th>\n",
" <th>vomitdays</th>\n",
" <th>vomitepiso</th>\n",
" <th>fever_high_c</th>\n",
" </tr>\n",
" <tr>\n",
" <th>virus_pos</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>False</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>38.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>38.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diarrhdays diarrhepiso vomitdays vomitepiso fever_high_c\n",
"virus_pos \n",
"False 2.0 4.0 2.0 3.0 38.888889\n",
"True 2.0 5.0 2.0 4.0 38.888889"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(severity_data.groupby('virus_pos')[['diarrhdays', 'diarrhepiso', 'vomitdays', 'vomitepiso', 'fever_high_c']].median())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>diarrhdays</th>\n",
" <th>diarrhepiso</th>\n",
" <th>vomitdays</th>\n",
" <th>vomitepiso</th>\n",
" <th>fever_high_c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2462.000000</td>\n",
" <td>2404.000000</td>\n",
" <td>3101.000000</td>\n",
" <td>3046.000000</td>\n",
" <td>1758.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.835906</td>\n",
" <td>5.706739</td>\n",
" <td>2.049661</td>\n",
" <td>4.688116</td>\n",
" <td>38.853179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.873456</td>\n",
" <td>5.253945</td>\n",
" <td>1.391721</td>\n",
" <td>4.723547</td>\n",
" <td>0.815487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>36.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>38.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>38.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.000000</td>\n",
" <td>7.000000</td>\n",
" <td>3.000000</td>\n",
" <td>6.000000</td>\n",
" <td>39.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10.000000</td>\n",
" <td>77.000000</td>\n",
" <td>10.000000</td>\n",
" <td>77.000000</td>\n",
" <td>42.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diarrhdays diarrhepiso vomitdays vomitepiso fever_high_c\n",
"count 2462.000000 2404.000000 3101.000000 3046.000000 1758.000000\n",
"mean 2.835906 5.706739 2.049661 4.688116 38.853179\n",
"std 1.873456 5.253945 1.391721 4.723547 0.815487\n",
"min 1.000000 0.000000 1.000000 1.000000 36.666667\n",
"25% 1.000000 3.000000 1.000000 2.000000 38.333333\n",
"50% 2.000000 5.000000 2.000000 4.000000 38.888889\n",
"75% 4.000000 7.000000 3.000000 6.000000 39.444444\n",
"max 10.000000 77.000000 10.000000 77.000000 42.222222"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"severity_data[['diarrhdays', 'diarrhepiso', 'vomitdays', 'vomitepiso', 'fever_high_c']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Diarrhea days')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe740650630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"severity_data.diarrhdays.hist()\n",
"plt.vlines([4,6], ymin=0, ymax=700)\n",
"plt.xlabel('Diarrhea days')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Diarrhea episodes')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe706c83be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"severity_data.diarrhepiso.hist()\n",
"plt.vlines([3,6], ymin=0, ymax=1800)\n",
"plt.xlim(0, 30)\n",
"plt.xlabel('Diarrhea episodes')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Vomit days')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7053e42b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"severity_data.vomitdays.hist()\n",
"plt.vlines([1,3], ymin=0, ymax=1400)\n",
"plt.xlim(0, 10)\n",
"plt.xlabel('Vomit days')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Vomit episodes')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6GJgPPNaE/ZuZWZM19GeqpPcDvw9cVCr+kqQFFMNK60eXRcQzku4EngW2A5f4TiUzs/bU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe705363d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"severity_data.vomitepiso.hist(bins=20)\n",
"plt.vlines([1,5], ymin=0, ymax=2000)\n",
"plt.xlim(0, 30)\n",
"plt.xlabel('Vomit episodes')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Max. Temperature')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAENCAYAAAAIbA6TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngvqaf47rAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7053420b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"severity_data.fever_high_c.hist(bins=10)\n",
"plt.vlines([38.4, 39], ymin=0, ymax=500)\n",
"plt.xlabel('Max. Temperature')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions for scoring each component"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def diarrh_duration_points(x):\n",
" if (x==0) or np.isnan(x):\n",
" return 0\n",
" elif 1 <= x <= 4:\n",
" return 1\n",
" elif 4 < x <= 5:\n",
" return 2\n",
" elif x >= 6:\n",
" return 3\n",
" else:\n",
" raise ValueError('Invalid diarrhea duration value:', x)\n",
" \n",
"def diarrh_episode_points(x):\n",
" if (x==0) or np.isnan(x):\n",
" return 0\n",
" elif 1 <= x < 4:\n",
" return 1\n",
" elif 4 <= x <= 5:\n",
" return 2\n",
" elif x > 5:\n",
" return 3\n",
" else:\n",
" raise ValueError('Invalid stool value:', x)\n",
" \n",
"def vomit_duration_points(x):\n",
" if (x==0) or np.isnan(x):\n",
" return 0\n",
" elif x == 1:\n",
" return 1\n",
" elif x == 2:\n",
" return 2\n",
" elif x > 2:\n",
" return 3\n",
" else:\n",
" raise ValueError('Invalid vomit duration value:', x)\n",
" \n",
"def vomit_episode_points(x):\n",
" if (x==0) or np.isnan(x):\n",
" return 0\n",
" elif x==1:\n",
" return 1\n",
" elif 2 <= x <= 4:\n",
" return 2\n",
" elif x > 4:\n",
" return 3\n",
" else:\n",
" raise ValueError('Invalid vomit episode value:', x)\n",
" \n",
"def fever_points(x):\n",
" if (x < 37) or np.isnan(x):\n",
" return 0\n",
" elif 37.1 <= x <= 38.4:\n",
" return 1\n",
" elif 38.5 <= x <= 38.9:\n",
" return 2\n",
" elif x >= 39:\n",
" return 3\n",
" else:\n",
" raise ValueError('Invalid fever value:', x) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate points for each criterion"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"diarrh_duration = (severity_data.assign(diarrhhours=severity_data.diarrhdays)\n",
" .diarrhhours\n",
" .apply(diarrh_duration_points))\n",
"\n",
"diarrh_max_episodes = (severity_data.diarrhepiso.apply(diarrh_episode_points))\n",
"\n",
"vomit_duration = (severity_data.assign(vomithours=severity_data.vomitdays)\n",
" .vomithours\n",
" .apply(vomit_duration_points))\n",
"\n",
"vomit_max_episodes = (severity_data.vomitepiso.apply(vomit_episode_points))\n",
"\n",
"fever_high = (severity_data.fever_high_c.apply(fever_points))\n",
"\n",
"emergency = (severity_data.newvisit==1) * 3\n",
"\n",
"# rehydration = (severity_data.irtherapydur==1).astype(int)\n",
"\n",
"hospitalization = 2*(severity_data.settingnew==1).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sum points to get score"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"veskari_subset = (diarrh_duration + diarrh_max_episodes +\n",
" vomit_duration + vomit_max_episodes +\n",
" fever_high)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"veskari_score = (diarrh_duration + diarrh_max_episodes +\n",
" vomit_duration + vomit_max_episodes +\n",
" fever_high + emergency + \n",
" dehydration + hospitalization)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.12,'mean=8.32\\nstd=3.16')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe70171c048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.distplot(veskari_score, bins=veskari_score.max()-1, axlabel='MVS')\n",
"text = \"mean={}\\nstd={}\".format(np.round(veskari_score.mean(), 2), \n",
" np.round(veskari_score.std(), 2))\n",
"ax.text(0, 0.12, text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By comparison, here is the distribution in the Schnadower et al. paper that validates the MVS:\n",
"\n",
"![](http://d.pr/i/3ISR94+)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"severity_data['mvs'] = veskari_score\n",
"severity_data['mvs_symptoms'] = veskari_subset"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>caseid</th>\n",
" <th>virus_pos</th>\n",
" <th>daysill</th>\n",
" <th>diarrh</th>\n",
" <th>diarrhstart</th>\n",
" <th>diarrhdays</th>\n",
" <th>diarrhepiso</th>\n",
" <th>vomit</th>\n",
" <th>vomitstart</th>\n",
" <th>vomitdays</th>\n",
" <th>...</th>\n",
" <th>norovirus</th>\n",
" <th>sapo_ct</th>\n",
" <th>astro_ct</th>\n",
" <th>noro_ct</th>\n",
" <th>coinfection</th>\n",
" <th>care_level</th>\n",
" <th>treatment</th>\n",
" <th>fever_high_c</th>\n",
" <th>mvs</th>\n",
" <th>mvs_symptoms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EN1C0001</td>\n",
" <td>False</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>39.444444</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>EN1C0002</td>\n",
" <td>False</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>EN1C0003</td>\n",
" <td>False</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EN1C0004</td>\n",
" <td>False</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EN1C0006</td>\n",
" <td>False</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 44 columns</p>\n",
"</div>"
],
"text/plain": [
" caseid virus_pos daysill diarrh diarrhstart diarrhdays diarrhepiso \\\n",
"0 EN1C0001 False 2.0 0.0 NaN NaN NaN \n",
"1 EN1C0002 False 5.0 1.0 4.0 3.0 2.0 \n",
"2 EN1C0003 False 3.0 1.0 3.0 3.0 3.0 \n",
"3 EN1C0004 False 4.0 0.0 NaN NaN NaN \n",
"4 EN1C0006 False 3.0 0.0 NaN NaN NaN \n",
"\n",
" vomit vomitstart vomitdays ... norovirus sapo_ct astro_ct \\\n",
"0 1.0 2.0 2.0 ... 0.0 NaN NaN \n",
"1 1.0 5.0 5.0 ... NaN NaN NaN \n",
"2 0.0 NaN NaN ... NaN NaN NaN \n",
"3 1.0 4.0 2.0 ... 0.0 NaN NaN \n",
"4 1.0 3.0 3.0 ... NaN NaN NaN \n",
"\n",
" noro_ct coinfection care_level treatment fever_high_c mvs mvs_symptoms \n",
"0 NaN 0 4 0 39.444444 6 6 \n",
"1 NaN 0 4 0 NaN 9 7 \n",
"2 NaN 0 4 0 NaN 2 2 \n",
"3 NaN 0 4 0 NaN 4 4 \n",
"4 NaN 0 4 0 NaN 7 5 \n",
"\n",
"[5 rows x 44 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"severity_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"severity_data.to_excel('results/severity.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"severity_data_complete = severity_data.dropna(subset=virus_cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop those with no virus testing, and merge with severity score table"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2886, 44)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"severity_data_complete.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scores by setting and virus"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"settings = {1:'Inpatient', 2:'ED', 3:'Outpatient'}"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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IW1q4l2dvpri8mtO6JxPfROdnMTN6tG/LTad35/oR6bSLieDLdfk8Mz2bfRXVXocn0uxt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\nLfzzi728tCYHh4H5E2J5YN4YQgO83R2aOk7tU893dsG4WAYE+/FZ+gH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f4nzSPBZn65cr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5SjVQ0kRfszZICvvcM5LaUU8yfF0NKqWbI5197hCNEtdh6p4OGlu3BzduL1BRP6VeIQ4uPGnVPj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7015145f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"combinations = [(virus, setting+1) for virus in ('rotavirus', 'sapovirus', 'astrovirus', 'norovirus') \n",
" for setting in range(3)]\n",
"fig, axes = plt.subplots(4, 3, figsize=(12,8), sharex=True, sharey=True)\n",
"plt.tight_layout()\n",
"\n",
"for ax, (virus, setting) in zip(axes.ravel(), combinations):\n",
" sns.distplot(severity_data_complete.loc[(severity_data_complete.settingnew==setting) &\n",
" severity_data[virus]==1, 'mvs'], \n",
" ax=ax,\n",
" bins=10,\n",
" axlabel='{} {}'.format(settings[setting], virus));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Virus model for severity"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
]
}
],
"source": [
"from patsy import dmatrix\n",
"import theano.tensor as tt\n",
"from theano import shared"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"formula = 'rotavirus + sapovirus + astrovirus + norovirus'\n",
"formula += ' + rotavirus:sapovirus + rotavirus:astrovirus + rotavirus:norovirus'\n",
"formula += ' + sapovirus:astrovirus + sapovirus:norovirus + astrovirus:norovirus'\n",
"\n",
"X = np.asarray(dmatrix(formula, severity_data_complete))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"X_pred = np.array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
" [1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n",
" [1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n",
" [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n",
" [1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0],\n",
" [1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
" [1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1]])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Sequential sampling (1 chains in 1 job)\n",
"NUTS: [σ, β]\n",
"100%|██████████| 3000/3000 [00:23<00:00, 127.38it/s]\n",
"Only one chain was sampled, this makes it impossible to run some convergence checks\n"
]
}
],
"source": [
"from pymc3 import Model, sample, Normal, HalfCauchy, Deterministic\n",
"\n",
"with Model() as severity_model:\n",
" \n",
" β = Normal('β', 0, sd=10, shape=X.shape[1])\n",
" \n",
" σ = HalfCauchy('σ', 1)\n",
" y = Normal('y', β.dot(shared(X.T)), sd=σ, observed=severity_data_complete.mvs.values)\n",
" \n",
" μ = Deterministic('μ', β.dot(shared(X_pred.T)))\n",
" \n",
" main_effects = Deterministic('main_effects', μ[:4])\n",
" \n",
" contrasts = Deterministic('contrasts', μ[1:] - μ[0])\n",
" \n",
" trace = sample(1000, tune=2000, chains=1, random_seed=seed_numbers[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While severity tends to increase with rotavirus, sapovirus and astrovirus infection, coinfection tends to reduce severity relative to main effects (with the exception of astrovirus:norovirus)."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Q6Qo7EDicsDpzy2yNJvxeDgL2BD4FOhF+b41GQyeaYihWGzwpunJSiOR1i5nN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\nERHROileERHROv8BHc84NdBqBXcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe6e174add8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pymc3 import forestplot\n",
"\n",
"forestplot(trace, varnames=['β'], ylabels=['intercept'] + formula.split(' + '));"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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hpfWcpFsS+6Wkm5nP5WnYbzYTz4KIWFFY707qOwAiYhnwDrBjpCd67iJ9WICU\n7O9sou0W90tO8OeTPoS8Lemu0rSnmX1c75qujB/Ul8FH7umE1gF9mpFaadpuHrCTpE6FxFYD/LVe\nPQByoroZOJw0mlgtaRr5yZaIWCNpPOni/RYwMSKWtiCOkveATQvr3UhTjuR2LgIukrQX8AdJz0fE\noxHRvxDj0Fx/aJmxzwfOynUPAh6R9EQTTzzWj30eaaRXOt5mwLbA3LxpLCkhDwP6AMfTuPptL+eT\n/fJR5YgxwBhJW5JGxsOB05po32y91bumq5NZB9Ua31N7lnTBvFTShvle0TGkUQWkxLRzof5mpAvu\nAgBJZ5JGO0VjgJNID5mMKTOeacBASRtIOhL4UqlAUv/8MIeAJaRpt9VltN1k7JJOlPS5vFqX65ba\nr98PDRkDnClpX0ldgCuBZ0vTgRHx53zsXwC/i4jFZcQ+DThK0jaSupFGZqW495B0WD7mCuB9yusX\nM7MO4VMntYj4EDgW+AqwEBgBnB4Rr+Yqt5Du1SyWdH9EvAJcC0whXej3AZ6u12YpUXYHfltmSOeR\nkupiUlK8v1C2G/AIsCwff0RETG5pwy2IfX/gWUnLgAeA8yLib7lsKHBb7ocBjbT/KPBD4NfAm8Au\nwMn1qo0FjqD8ZH878AJQS3qYZVyhrAswjPT7mw/sAFxeZvtmZhXX5l++tvWG/5DMrC21z5evzczM\nOgonNTMzqxpOamZmVjWc1MzMrGo4qZmZWdVwUjMzs6rhpGZmZlXDSc3MzKqGk5qZmVUNJzUzM6sa\nTmpmZlY1nNTMzKxqOKmZmVnVWGeTmqSDJb1W6Tg6AkmjJP2w0nGYmVVau756RtJoYE5E/KDdDmrt\nxa+eMbO21P6vnpHUuTXbW1utGYekoZKGtlZ7jRyjzfqto/xOzMzaw6dOapJqJQ2W9CKwXNI+kibn\nNzy/LOnYXO/bpDdRXyppmaTf5O1DJM2QtFTSK5KOz9u75Db2Lhxre0nvS9pBUj9Jc5qIo7OkkLRr\noc5oSVfk5e0kTczHWCTpSUll9Uc+5sWSXpT0rqRxkjYulJ8laXpu/wFJ3QtlIem7kl4HXs/bDpT0\nfG7reUkH5u0nS/pjvWNfIOmBBs6rn6Q5uS/mA7+UdIakp+rt//e+kXRU7vulkuZKuricfjBrDVNn\n1TFy8gymzqqrdCi2DmutkdopwNHAdsB9wMPADsD3gDsl7RERNwF3Av8VEZtHxDF53xnAwcBWwI+B\nOyR9NiI+AO7NbZcMAB6PiLebiWPriFjVTMwXAXOA7YHPAJezdlNoA4Ajgc8D/wicASDpMOCqXP5Z\nYCZwV719vwr0AXpJ2gaYBFzc6EgQAAAZJUlEQVQPbAv8DJgkaVvgAWAPSbsV9h0IjGkkpm7ANkAP\n4NstOIdbgO9ExBbA3sBjLdjHrNVMnVXHgFFTGP7QqwwYNcWJzdZaa01NXR8RsyUdDGwODIuINcBj\nkiaSks3QhnaMiLsLq+MkXQYcAEwgXbRvAr6fywcCNzYXRwtjXklKNj0iYjrwZAv3a+iY8wDy6HPf\nvP1U4NaImJrLLgPqJPWMiNpc56qIWJTLvw68HhG357Kxks4FjomI0ZImkPrxJzm57UlKdg1ZA/wo\nfzBAanYqeiUpsb4QEXWAryhVoueQSZUOoWyr1gQnjHim0mGstdphR1c6hPVaayW1UiLpDszOCa1k\nJrBjYztKOh24EOiZN21OGvFBGjFsIqkPMJ+UMO5rQRwtcTUp0T6cL/o3RcSwHNNE4KBcb+O87fy8\n/lRE9C+0M7+w/B6pD8j/Ti0VRMQySe+Q+qK2gXi7k/qqqNh3Y4BrgZ+Qkvv9EfFeI+e2ICJWNFLW\nkK8BPwCG5enbIRExpYz9rYNaVy6wpZHaqjVB505i/KC+9K7pWumwbB3UWkmtNG03D9hJUqdCYqsB\n/lqvHgCSegA3A4cDUyJitaRp5KdcImKNpPGkEcpbwMSIWNqCOEreAzYtrHcjTTmS27kIuEjSXsAf\nJD0fEY8Wk1bpIZGIGNp0F3zCPNL0X6mdzUjTinMbifdj9bMa4KG8/DCwnaR9Sf1xQRPHrt8Pyyn0\ng6RuH6sc8TxwnKQNgXOA8cBOTbRv1qp613Rl/KC+PPvGIvrsvI0Tmq211v6e2rOkC+ilkjaU1A84\nho/uJb0F7FyovxnpArwAQNKZpHs6RWOAk0jTeY3dQ2rMNGCgpA0kHQl8qVQgqb+kXZWGaUuA1fmn\ntYwBzpS0r6QuwJXAs4Wpx/oeBHaXNDA/5HIS0AuYCJDvEd5DGmFuA/y+jFheAPbKsWxMYSpY0kaS\nTpW0VUSs5KO+MGtXvWu6cna/XZzQ7FNp1aQWER8CxwJfARYCI4DTI+LVXOUW0r2bxZLuj4hXSFNq\nU0gJbx/g6XptlhJld+C3ZYZ0HimpLiYlxfsLZbsBjwDL8vFHRMTkMttvVEQ8CvwQ+DXwJrALcHIT\n9d8B+pNGj+8AlwL9I2JhodoY4Ajg7hY8CFNs+6+kactHSE9aPlWvymlAraQlwCDgGy1t28ysI2nX\nL19bVfMfkpm1pfb/8rWZmVklOamZmVnVcFIzM7Oq4aRmZmZVw0nNzMyqhpOamZlVDSc1MzOrGk5q\nZmZWNZzUzMysajipmZlZ1XBSMzOzquGkZmZmVcNJzczMqoaTmpmZVY11LqlJWiZp5+Zrrl8kjZL0\nw0rHYWZWSX6fWjuRNBm4IyJ+UelY2oj/kMysLa1/71OTtEErtdMvJ6F2I6nzuti2wdRZdYycPIOp\ns+oqHYrZeq9FSU3SYElzJS2V9JqkwyUdIGmKpMWS3pR0g6SNCvuEpHMlvSFpoaSrJXXKZZ0k/UDS\nTElvS/qVpK1y2UOSzql3/BcknVBod9e8PFrSSEkPSloOHCppsqRvFfY9Q9JTeVmSrsvHfFfSi5L2\nLqfDJB0t6c+SlkiaLWlooWxjSXdIeif3y/OSPiPpp8DBwA15+vSGwrl8V9LrwOt524F5v3fzvwfm\n7SdL+mO9WC6Q9EChL67Iy/0kzcm/t/nAL4v9UO93VOrLoyS9kn/HcyVdXE6/rK+mzqpjwKgpDH/o\nVQaMmuLEZlZhzX6Cl7QHcA6wf0TMk9QT2ADYGrgA+CPwOeC3wH8APy/sfjywH7A58AjwGvAL4Iz8\ncyjwNvAr4AbgNGAM8J28jqReQA9gUiMhDgSOAvoDGzVSp+TLwCHA7sC7wJ7A4mb2qW85cDrwMrA3\n8HtJ0yLifuDfgK2AnYAPgH2B9yPi+5L+Dw1PP34V6AO8L2kb0nmeC4wFTgQm5cTzAHCzpN0i4vXC\nuV/bSJzdgG1IfdcJOKmZ87oFGBART0rqCny+BX3xqfQc0tivdN20ak1wwohnKh3GWqsddnSlQzD7\n1FoyLbUa6AL0krQgImobqFMr6UbgS3w8qQ2PiEXAIkk/B04hJbVTgZ9FxBsAki4DXpJ0JnAfMFJS\nj4iYmeveGxEfNBLfhIh4Oi+vkJqcdl0JbEFKZs9FxF+aO/n6ImJyYfVFSWNJ531/bn9bYNeIeBH4\nUwuavCr3EZK+DrweEbfnsrGSzgWOiYjRkiaQ+vAnknbL5/FAI+2uAX5U6rdm+oUcey9JL0REHdDm\nQ45quIiWRmqr1gSdO4nxg/rSu6ZrpcMyW281O/0YEdOB84GhwNuS7pLUXdLukiZKmi9pCXAlsF29\n3WcXlmcC3fNy97xeLOsMfCYilpJGKyfnspOBO5sIcXYTZfXP5THSCPB/gLck3SRpSwBJQ/KU4WJg\nInBQaT1vI9frI+kPkhZIehcYVDjv24HfAXdJmifpvyRt2ExYxfjr9wt5fce8PIaU1CCN0u6PiPca\naXdBRKxo5thFXyONeGdKelxS3zL2XW/1runK+EF9GXzknk5oZh1Ai+6pRcSYiDiINJUVwHBgJPAq\nsFtEbAlcziefTtmpsFwDzMvL83JbxbJVwFt5fSxwSr6wbgL8oanw6q0vBzYtrHerdy7XR8QXgL1I\n05CX5O3DImLriNiaNJX5VGk9bysZQxod7RQRWwGjSucdESsj4scR0Qs4MLdzeiNxNhR//X6B1Ddz\n8/LDwHaS9iUltzGNtNnQ8T7WL5Lq98vzEXEcsANp1Dm+ibatoHdNV87ut4sTmlkH0GxSk7SHpMMk\ndQFWAO+TpiS3AJYAyyTtCZzdwO6XSOoqaSfgPGBc3j4WuEDS5yVtThrljYuIVbn8QdLF/Sd5+5oy\nzmkacIKkTfO9qH8vnMv+eaS1IekivyKfSzm2ABZFxApJB5BGTKX2D5W0j9JTmEtIU3ql9t8Cmvt+\n3YPA7pIGSuos6SSgF2nkSO6fe4CrSffLfl9G3C8Ae0naV9LGpJF3Ke6NJJ0qaauIWJljL7dfzMwq\nriUjtS7AMGAhMJ/0Sf5y4GLSBX0pcDMfJayiCaT7StNIU4q35O23kqbqngD+Rkou3yvtlO8D3Qsc\nQdOjkYZcB3xISiK38fGpyy1zrHWkab13gGvKbP8/SPe0lgL/l4+PaLqRks4S4C/A48Aduey/ga9L\nqpN0fUMNR8Q7pNHdRTm2S4H+EbGwUG0MqV/uLnwIaFZE/JX0IeER0pOWT9Wrchrp3ugS0pTqN1ra\ntplZR9FmX76WFKSpyeltcgDraPzlazNrS+vfl6/NzGz95qRmZmZVo83+90kR0aKhopmZWWvxSM3M\nzKqGk5qZmVUNJzUzM6saTmpmZlY1nNTMzKxqOKmZmVnVcFIzM7Oq4aRmZmZVw0nNzMyqhpOamZlV\njXU+qUlaJqm595RVvfw+tIcrHYeZWSW12atnbL3jPyQza0t+9Ux+A3VrtNNP0uTWaKuJY0hSm/w+\nJLXZ/7jazKwjWauLqKTBkuZKWirpNUmHSzpA0hRJiyW9KekGSRsV9glJ50p6Q9JCSVeXLuKSOkn6\ngaSZkt6W9CtJW+WyhySdU+/4L0g6odDurnl5tKSRkh6UtBw4VNJkSd8q7HuGpKfysiRdl4/5rqQX\nJe1dZl+MlvQ/kibl/nhW0i6F8gMlPZ/bf17SgYWyyZJ+Kulp4D1gZ0ndJT0gaZGk6ZLOynW7S3pf\n0jaF/f859+WGxfMq9Mt3Jb0OvC6pZ97Wud7xv5WXd5X0eI5zoaSG3mRuVWzqrDpGTp7B1Fl1lQ7F\nbK2VndQk7QGcA+wfEVsA/wrUAquBC4DtgL7A4cB/1Nv9eGA/oDdwHPDNvP2M/HMosDOwOXBDLhsD\nnFI4fi+gBzCpkRAHAj8FtgCeaqROyZeBQ4Ddga2Bk4B3mtmnIacAPwa6AtPz8ckJaBJwPbAt8DNg\nkqRtC/ueBnw7xzsTGAvMAboDXweulHR4RMwDpgBfq3eu90TEykbi+irQB+jVgnP4T+DhfA6fA/5f\nC/axKjF1Vh0DRk1h+EOvMmDUFCc2W2etzbTUaqAL0EvSgoiobaBOraQbgS8BPy9sHx4Ri4BFkn5O\nSga/AE4FfhYRbwBIugx4SdKZwH3ASEk9ImJmrntvRHzQSHwTIuLpvLxCanIadiUpmewJPBcRf2nu\n5Btxb0Q8l2O/k5S8AI4GXo+I2/P6WEnnAscAo/O20RHxct53J+AgoH9ErACmSfoFKfE9SkrwA4Gb\nlU7sZFJ/NOaq3N800w+Q+qIH0D0i5tD8BwIrQ88hjX0G63hWrQlOGPFMpcNoFbXDjq50CNbOyk5q\nETFd0vnAUGAvSb8DLiSNrn5GGoltmtv+U73dZxeWZ5JGI+R/Z9Yr6wx8JiLmSppEuoAPz/9+u4kQ\nZzdRVv9cHpN0A/A/QI2k+4CLI2KJpCHAkFy1M7CxpMWFfbcuNDW/sPweqS8aOq/Sue3YSLzdgUUR\nsbRe/f3y8j3A/5PUHdiN9HDGk02cYov7AriUNFp7TlIdcG1E3FrG/taEjn5xLY3UVq0JOncS4wf1\npXdN10qHZVa2tbqnFhFjIuIg0if7ICWbkcCrwG4RsSVwOZ98WmWnwnINMC8vz8ttFctWAW/l9bHA\nKZL6ApsAf2gqvHrry0lJtqRbvXO5PiK+AOxFmoa8JG8fFhFb5+TVH3iqtF4voTWl/nmVzm1uI/HO\nA7aRtEVD9SNiMWmKcABpxDY2mn58tVi2PP/bYF9ExPyIOCsiugPfAUaU7lVa9etd05Xxg/oy+Mg9\nndBsnbZW99QkHSapC7ACeJ80JbkFsARYJmlP4OwGdr9EUtc8zXYeUHoYYSxwgaTPS9ocuBIYFxGr\ncvmDpOTwk7x9TRkhTwNOkLRpvkj/e+Fc9pfUR9KGpIv+inwureVBYHdJAyV1lnQS6f7WxIYqR8Rs\n4BngKkkbS/rHHO+dhWpjgNNJ99bGtDSQiFhASo7fkLSBpG8CxQdaTpT0ubxaR0qIrdkX1sH1runK\n2f12cUKzddrajNS6AMOAhaRptx1Io7KLSaOHpcDNfJSwiiaQpiSnkR6guCVvvxW4HXgC+BspuXyv\ntFO+f3YvcARlXMiz64APSaO+2/h4gtgyx1pHmuZ7B7imzPYbFRHvkEZ5F+W2LyXdL1vYxG6nAD1J\no7b7gB9FxO8L5Q+Qph7fiogXygzpLNJI9B3SyLR442R/4FlJy/IxzouIv5XZvplZRbXbl68lBWlq\ncnq7HNDam798bWZtyV++NjOz9YuTmpmZVY12+98nRUSLho5mZmZryyM1MzOrGk5qZmZWNZzUzMys\najipmZlZ1XBSMzOzquGkZmZmVcNJzczMqoaTmpmZVQ0nNTMzqxpOamZmVjWc1MzMrGqs80lN0suS\n+lU6jkqTdLCk1yodh5lZJbX5+9T8HrX1ht+nZmZtad14n5qkNntTQGu1LamnpNrWaKuZ42zQRu22\n29sYzNrD1Fl1jJw8g6mz6iodinUwZSU1SUMkzZC0VNIrko7P23eV9LikdyUtlDQub38i7/qCpGWS\nTpLUT9IcSYMlzQd+meueJWm6pEWSHpDUPW8fJemaenFMkHRhXq6VdEReHirpHkl3SFoCnCFptKQr\nCvv2kzSnsD5Y0tx8Tq9JOrzMPhkqabykX+U2Xpa0X6H8HyRNlrQ4lx1bKBstaaSkByUtBw6VtFVu\na4GkmZJ+IKmTpC65jb0L+28v6X1JOzRwXrX53F4ElkvqLCkk7Vrv+Ffk5e0kTczHWCTpSUkV/9Bj\nVt/UWXUMGDWF4Q+9yoBRU5zY7GPK/QQ/AzgYmA+cCNyRL5L/CTwMHApsBOwHEBGH5OnHfypNP+b7\nX92AbYAeQCdJhwFXAV8GXgauAe4CDgHGAHdKuiQiQlLXXO/sRmI8Lsd2OtAFOKixk5G0B3AOsH9E\nzJPUE1ib0dKxwAnAmcAVwA3AFyVtCPwGuDXHfBAwQdJ+EVG6/zUQOAroT+q7m4CtgJ2BbUn9+mZE\n3CLpXuAU4Pt53wHA4xHxtqReDcR1CnA0sDAiVklNjt4vAuYA2+f1L+IpxarUc8ikSofQalatCU4Y\n8Uylw/jUaocdXekQqkZZSS0i7i6sjpN0GXAAsJKUoLpHxBzgqWaaWgP8KCI+AJB0KnBrREzN65cB\ndTnJPEm6uB4MPAF8HZgSEfMaaXtKRNyfl99v5kK+mpT4eklaEBG1zcTdmKci4sEc++3A+Xn7F4HN\ngWERsQZ4TNJEUrIZmutMiIin874rgZOAf46IpcBSSdcCpwG3kBL8TXyU1AYCNzYR1/URMbuF57AS\n+CzQI38AebKF+9k6Zl2/gJZGaqvWBJ07ifGD+tK7pmulw7IOotzpx9MlTctTVIuBvYHtgEtJN/Ge\ny1Ns32ymqQURsaKw3h2YWVqJiGXAO8COkZ5kuYuUCCBdyO9sou2WXsTJF+/zSQnmbUl3FaY9BxbO\n80WgprSef2oKTc0vLL8HbJzvY3UHZueEVjIT2LGReLcjjdZmNlL/MWATSX0k9QD2Be5r4hRb3BfA\n1cB04GFJb0gaUsa+Zu2md01Xxg/qy+Aj93RCs09ocVLLF9GbSdN120bE1sBLpCco50fEWRHRHfgO\nMKJ476YB9ae15pFGeqVjbUaaepubN40Fvp5j6AP8uoy2lwObFta7faxyxJiIOCgfP4Dhhe1b5/P8\nR2BWaT3/zGoihuJ57VTv3lRN4bzqx7uQj0a9n6ifk+N4UoIfCEzMI7rG1O+L92ikLyJiaURcFBE7\nA8cAF5Z7f9GsvfSu6crZ/XZxQrNPKGekthnpIrkAQNKZpJEakk6U9Llcry7XW53X3yLdH2rKGOBM\nSftK6gJcCTxbmg6MiD/n4/4C+F1ELC4j7mnAUZK2kdSNj6YGkbSHpMPyMVcA7xfibg3PkpLqpZI2\nzPcTjyGNPD8hIlaTktZPJW2Rk/iFwB2FamNIU5Sn5uVyTAMGStpA0pHAl0oFkvorPfAjYAmpH1qz\nL8zM2lyLk1pEvAJcC0whJap9gKdz8f7As5KWAQ8A50XE33LZUOC2PGU3oJG2HwV+SBqBvQnsApxc\nr9pY4AjKv5DfDrwA1JIeuhhXKOsCDCONkOYDOwCXl9l+oyLiQ9JDJF/JxxgBnB4Rrzax2/dIifAN\n0r3JMaQHTUptlhJld+C3ZYZ0HimpLiYlxfsLZbsBjwDLSL/jERExucz2zcwqqs2/fG3rDf8hmVlb\nWje+fG1mZtZanNTMzKxqOKmZmVnVcFIzM7Oq4aRmZmZVw08/WquQ9BDp/4hSX3fSl9A7oo4cG3Ts\n+Bzb2uvI8XXk2BZGxJHNVXJSszYlKSKiRY/itreOHBt07Pgc29rryPF15NhaytOPZmZWNZzUzMys\najipWVv7caUDaEJHjg06dnyObe115Pg6cmwt4ntqZmZWNTxSMzOzquGkZmZmVcNJzczMqoaTmpmZ\nVQ0nNTMzqxpOatYmJG0t6R5Jr0r6i6S+lY6pRNIekqYVfpZIOr/ScZVIukDSy5JekjRW0saVjqlE\n0nk5rpc7Qp9JulXS25JeKmzbRtLvJb2e/+3aweI7MfffGkn7dbDYrs7/zb4o6T5JW1cqvrXlpGZt\n5b+BhyJiT+CfgL9UOJ6/i4jXImLfiNgX+ALwHnBfhcMCQNKOwLnAfhGxN7ABcHJlo0ok7Q2cBRxA\n+p32l7RbZaNiNFD//wc4BHg0InYDHs3rlTKaT8b3EnAC8ES7R/Nxo/lkbL8H9o6IfwT+ClzW3kF9\nWk5q1uokbQkcAtwCEBEfRsTiykbVqMOBGRExs9KBFHQGNpHUGdiUjvM/mP0H4H8j4r2IWAU8Dhxf\nyYAi4glgUb3NxwG35eXbgK+2a1AFDcUXEX+JiNcqFFIxjoZiezj/bgH+F/hcuwf2KTmpWVvYGVgA\n/FLSnyX9QtJmlQ6qEScDYysdRElEzAWuAWYBbwLvRsTDlY3q714CDpG0raRNgaOAnSocU0M+ExFv\nAuR/d6hwPOuqbwK/rXQQ5XJSs7bQGegNjIyIfwaWU9kpoAZJ2gg4Fri70rGU5Ps/xwGfJ70GZDNJ\n36hsVElE/AUYTpqiegh4AVjV5E62TpL0fdLv9s5Kx1IuJzVrC3OAORHxbF6/h5TkOpqvAFMj4q1K\nB1JwBPC3iFgQESuBe4EDKxzT30XELRHROyL+f3t3y1JBEEdh/DlFwSoKlhssVqNJBIMGsYmI4SIY\nzBYxiZ/BahdMajdZbJosgsFbVPCtCGL4G2ZAES2+zd7h/Mosy4bDlLPMDrPjpKWr89KZPnEtaQgg\njzeF83QVSW1gBliMLjxH0aVmvy4iroCOpJF8axI4KxjpKws0aOkxuwTGJPVJEmnuGrPJRtJgHluk\nzQ5Nmz+AA6Cdr9vAfsEsXUXSNLAGzEbEU+k83+EDje1PSBoFtoEe4AJYioj7sqne5G9CHWA4Ih5L\n53lP0iYwT1r+OQGWI+K5bKpE0hHQD7wAqxFxWDjPDjBB+uv6NbAB7AG7QIv0kjAXER83k5TMdwds\nAQPAA3AaEVMNybYO9AK3+bHjiFj572w/4VIzM7NqePnRzMyq4VIzM7NquNTMzKwaLjUzM6uGS83M\nzKrhUjMzs2q41MzMrBouNTMzq8YrDm62uBoH8dMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe6e148ac88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forestplot(trace, varnames=['μ'], ylabels=formula.replace(':', '+').split(' + '), quartiles=False,\n",
" main='Expected severity');"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.gridspec.GridSpec at 0x7fe6e1494e10>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe6e14492b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6), dpi=300)\n",
"forestplot(trace, varnames=['main_effects'], ylabels=formula.replace(':', '+').split(' + ')[:4], \n",
" quartiles=False, plot_kwargs={'linewidth':5, 'markersize':7})"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vbqqRPay1lNDTy5xrd2AfYEncNAY4I8bQDfhDHnWvA3ZL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe6e1357240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forestplot(trace, varnames=['contrasts'], ylabels=formula.replace(':', '+').split(' + ')[1:], quartiles=False,\n",
" main='Effect relative to rotavirus only');"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"from pymc3.stats import _hpd_df\n",
"\n",
"def median(x):\n",
" return pd.Series(np.median(x, 0), name='median')\n",
"\n",
"hpd = lambda x: _hpd_df(x, 0.05)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>median</th>\n",
" <th>lower 95%</th>\n",
" <th>upper 95%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>rotavirus</th>\n",
" <td>10.2</td>\n",
" <td>9.8</td>\n",
" <td>10.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus</th>\n",
" <td>8.6</td>\n",
" <td>8.2</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>astrovirus</th>\n",
" <td>8.3</td>\n",
" <td>7.7</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norovirus</th>\n",
" <td>8.4</td>\n",
" <td>8.2</td>\n",
" <td>8.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:sapovirus</th>\n",
" <td>10.4</td>\n",
" <td>9.0</td>\n",
" <td>11.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:astrovirus</th>\n",
" <td>10.6</td>\n",
" <td>8.9</td>\n",
" <td>12.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:norovirus</th>\n",
" <td>9.6</td>\n",
" <td>8.4</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus:astrovirus</th>\n",
" <td>7.5</td>\n",
" <td>6.2</td>\n",
" <td>8.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus:norovirus</th>\n",
" <td>8.7</td>\n",
" <td>7.7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>astrovirus:norovirus</th>\n",
" <td>9.8</td>\n",
" <td>8.6</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" median lower 95% upper 95%\n",
"rotavirus 10.2 9.8 10.6\n",
"sapovirus 8.6 8.2 9.0\n",
"astrovirus 8.3 7.7 9.0\n",
"norovirus 8.4 8.2 8.7\n",
"rotavirus:sapovirus 10.4 9.0 11.8\n",
"rotavirus:astrovirus 10.6 8.9 12.7\n",
"rotavirus:norovirus 9.6 8.4 10.8\n",
"sapovirus:astrovirus 7.5 6.2 8.7\n",
"sapovirus:norovirus 8.7 7.7 10.0\n",
"astrovirus:norovirus 9.8 8.6 11.1"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pymc3 import summary\n",
"summary_table = (summary(trace, varnames=['μ'], stat_funcs=[median, hpd])\n",
" .set_index(np.array(formula.split(' + ')))\n",
" .round(1)[['median', 'hpd_2.5', 'hpd_97.5']])\n",
"summary_table.columns = 'median', 'lower 95%', 'upper 95%'\n",
"summary_table.to_excel('results/predicted_veskari.xlsx')\n",
"summary_table"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>median</th>\n",
" <th>lower 95%</th>\n",
" <th>upper 95%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sapovirus</th>\n",
" <td>-1.6</td>\n",
" <td>-2.2</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>astrovirus</th>\n",
" <td>-1.9</td>\n",
" <td>-2.7</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norovirus</th>\n",
" <td>-1.8</td>\n",
" <td>-2.2</td>\n",
" <td>-1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:sapovirus</th>\n",
" <td>0.2</td>\n",
" <td>-1.3</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:astrovirus</th>\n",
" <td>0.4</td>\n",
" <td>-1.5</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rotavirus:norovirus</th>\n",
" <td>-0.7</td>\n",
" <td>-1.8</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus:astrovirus</th>\n",
" <td>-2.8</td>\n",
" <td>-4.1</td>\n",
" <td>-1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus:norovirus</th>\n",
" <td>-1.5</td>\n",
" <td>-2.6</td>\n",
" <td>-0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>astrovirus:norovirus</th>\n",
" <td>-0.4</td>\n",
" <td>-1.8</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" median lower 95% upper 95%\n",
"sapovirus -1.6 -2.2 -1.1\n",
"astrovirus -1.9 -2.7 -1.1\n",
"norovirus -1.8 -2.2 -1.3\n",
"rotavirus:sapovirus 0.2 -1.3 1.7\n",
"rotavirus:astrovirus 0.4 -1.5 2.3\n",
"rotavirus:norovirus -0.7 -1.8 0.6\n",
"sapovirus:astrovirus -2.8 -4.1 -1.5\n",
"sapovirus:norovirus -1.5 -2.6 -0.2\n",
"astrovirus:norovirus -0.4 -1.8 0.8"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"contrast_table = (summary(trace, varnames=['contrasts'], stat_funcs=[median, hpd])\n",
" .set_index(np.array(formula.split(' + '))[1:])\n",
" .round(1)[['median', 'hpd_2.5', 'hpd_97.5']])\n",
"contrast_table.columns = 'median', 'lower 95%', 'upper 95%'\n",
"contrast_table.to_excel('results/contrasts.xlsx')\n",
"contrast_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Posterior predictive checks"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 500/500 [00:00<00:00, 546.42it/s]\n"
]
}
],
"source": [
"from pymc3 import sample_ppc\n",
"\n",
"with severity_model:\n",
" severity_ppc = sample_ppc(trace, samples=500)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([68., 74., 48., 47., 51., 63., 42., 40., 29., 38.]),\n",
" array([0.02 , 0.118, 0.216, 0.314, 0.412, 0.51 , 0.608, 0.706, 0.804,\n",
" 0.902, 1. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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60GvI5CSvBt4FXF1V35tSbZM0rN0nAS8H7khygMG5yD0NXFTt+z7fXVVPVdW/A19lEPbr\nXZ+27wBuBqiqzwHPA06fSnWzM9Fh09dCuPcZOngPsNDNXwN8urorEuvY0HZ3pyf+nEGwt3L+9bjt\nrqonqur0qpqvqnkG1xqurqq7Z1Pu2PR5n/8tg4voJDmdwWmaR6Za5WT0afujwFaAJOcyCPdvTLXK\n6dsD/FJ318wlwBNVdWhse5/1FeWjrhr/G4Mr6u/q1v0ug//UMHihPwY8BHweOGfWNU+p3f8EHAbu\n6372zLrmabT7GdveQQN3y/R8vQO8F3gQ+Apw7axrnmLbzwM+y+BOmvuA18y65jG0+SPAIeApBr30\nHcCbgDcd9Xq/r/s3+cq43+cO+StJDVoLp2UkSWNmuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QG\n/S/NTSmhKrWuTAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe6e1254908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import percentileofscore\n",
"\n",
"plt.hist([np.round(percentileofscore(x, y)/100, 2) for x, y in zip(severity_ppc['y'], severity_data_complete.mvs.values)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Average severities by virus"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"formula = 'rotavirus + sapovirus + astrovirus + norovirus'\n",
"\n",
"X = np.asarray(dmatrix(formula, severity_data_complete))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"X_pred = np.array([[1, 1, 0, 0, 0],\n",
" [1, 0, 1, 0, 0],\n",
" [1, 0, 0, 1, 0],\n",
" [1, 0, 0, 0, 1]])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Sequential sampling (1 chains in 1 job)\n",
"NUTS: [σ, β]\n",
"100%|██████████| 3000/3000 [00:13<00:00, 229.53it/s]\n",
"Only one chain was sampled, this makes it impossible to run some convergence checks\n"
]
}
],
"source": [
"with Model() as avg_severity_model:\n",
" \n",
" β = Normal('β', 0, sd=10, shape=X.shape[1])\n",
" \n",
" σ = HalfCauchy('σ', 1)\n",
" y = Normal('y', β.dot(shared(X.T)), sd=σ, observed=severity_data_complete.mvs.values)\n",
" \n",
" μ = Deterministic('μ', β.dot(shared(X_pred.T)))\n",
" \n",
" avg_trace = sample(1000, tune=2000, chains=1, random_seed=seed_numbers[0])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>median</th>\n",
" <th>hpd_2.5</th>\n",
" <th>hpd_97.5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>rotavirus</th>\n",
" <td>10.1</td>\n",
" <td>9.7</td>\n",
" <td>10.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sapovirus</th>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>astrovirus</th>\n",
" <td>8.3</td>\n",
" <td>7.8</td>\n",
" <td>8.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norovirus</th>\n",
" <td>8.4</td>\n",
" <td>8.2</td>\n",
" <td>8.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" median hpd_2.5 hpd_97.5\n",
"rotavirus 10.1 9.7 10.5\n",
"sapovirus 8.5 8.2 8.9\n",
"astrovirus 8.3 7.8 8.9\n",
"norovirus 8.4 8.2 8.7"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(summary(avg_trace, varnames=['μ'], stat_funcs=[median, hpd]).round(1)\n",
" .set_index(np.array(formula.split(' + '))))"
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/fcd2ab48584cc47741ae56f1f73ae677"
},
"gist": {
"data": {
"description": "Untitled.ipynb",
"public": false
},
"id": "fcd2ab48584cc47741ae56f1f73ae677"
},
"kernel_info": {
"name": "python3"
},
"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": "3.6.5"
},
"nteract": {
"version": "0.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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