Epipy

Epipy is a Python package for epidemiology. It contains (or will contain…) tools for analyzing and visualizing epidemiology data. Epipy can currently produce:

Installation

Install using pip:

pip install epipy

or clone the github repository and install using setup.py:

git clone https://github.com/cmrivers/epipy.git
python setup.py install

Note that I use the package mpltools to produce the nice ggplot-style plot aesthetics you see in my examples. Using mpltools is optional, but I highly recommend it.

Quickstart

Case tree plot

import epipy as epi
import pandas as pd

# optional import
from mpltools import style
style.use('ggplot')

example_df = epi.get_data('example_data')
fig, ax = epi.case_tree_plot(example_df, cluster_id = 'Cluster', \
          case_id ='ID', date_col='Date', color='Cluster', \
          gen_mean=4, gen_sd = 1)
ax.set_title('Example outbreak data')

Generation analysis

mers_df = epi.get_data('mers_line_list')
mers_G = epi.build_graph(mers_df, cluster_id='Cluster ID', case_id='Case #',
	        date_col='dates', color='Health status', gen_mean=5, gen_sd=4)
fig, ax, table = epi.generation_analysis(mers_G, attribute='Health status', \
                                         plot=True)

table returns:

Health status  Alive  Asymptomatic  Critical  Dead  Mild  Recovered  All
generation
0                  5             1         3    12     1          0   22
1                  6             7         3    10     8          0   35
2                  2             5         2     7     2          2   20
3                  0             5         4     5     0          2   16
4                  1             0         0     2     0          0    3
5                  0             0         0     1     0          0    1
6                  1             0         0     1     0          0    2
7                  1             0         0     0     0          0    1
8                  0             0         0     0     0          1    1
All               16            18        12    38    11          5  101

Epidemic curve

curve, fig, ax = epi.epicurve_plot(mers_df, date_col='dates', freq='m')
ax.set_title('Approximate onset or report date of MERS cases')

Stratified summary statistics

df = pd.DataFrame({'Age' : [10, 12, 14], 'Group' : ['A', 'B', 'B'] })
epi.summary(df.Age, by=df.Group)

returns:

    count  missing  min  median  mean      std   max
A      1        0   10      10    10       NaN   10
B      2        0   12      13    13  1.414214   14

Next: see an example with outbreak data or read the documentation