uofuepibio / epiworldpy

epiworldpy: Python bindings for epiworld

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This is a python wrapper of the epiworld c++ library, an ABM simulation engine. This is possible using the pybind11 library (which rocks!).

The epiworld module is already implemented in R.

Installation

• clone this repository
• pip install ./epiworldpy

Examples

Base setup

Here we show how to create a SEIR object and add terms to it. We will use the following data:

# Loading the module
import epiworldpy as m
covid19 = m.ModelSEIR(
  name              = 'covid-19',
  n                 = 10000,
  prevalence        = .01,
  contact_rate      = 2.0,
  transmission_rate = .1,
  incubation_days   = 7.0,
  recovery_rate     = 0.14
)

We can now take a look at the model

# Creating the object
covid19.print(False)

________________________________________________________________________________
________________________________________________________________________________
SIMULATION STUDY

Name of the model   : Susceptible-Exposed-Infected-Removed (SEIR) (connected)
Population size     : 10000
Agents' data        : (none)
Number of entities  : 0
Days (duration)     : 0 (of 0)
Number of viruses   : 1
Last run elapsed t  : -
Rewiring            : off

Global actions:
 (none)

Virus(es):
 - covid-19 (baseline prevalence: 1.00%)

Tool(s):
 (none)

Model parameters:
 - Avg. Incubation days : 7.0000
 - Contact rate         : 2.0000
 - Prob. Recovery       : 0.1400
 - Prob. Transmission   : 0.1000

<epiworldpy._core.ModelSEIRCONN at 0x7fe01a91bcb0>

And run it and see what we get

covid19.run(100, 223)
covid19.print(False)

_________________________________________________________________________
Running the model...
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
 done.
________________________________________________________________________________
________________________________________________________________________________
SIMULATION STUDY

Name of the model   : Susceptible-Exposed-Infected-Removed (SEIR) (connected)
Population size     : 10000
Agents' data        : (none)
Number of entities  : 0
Days (duration)     : 100 (of 100)
Number of viruses   : 1
Last run elapsed t  : 105.00ms
Last run speed      : 9.47 million agents x day / second
Rewiring            : off

Global actions:
 (none)

Virus(es):
 - covid-19 (baseline prevalence: 1.00%)

Tool(s):
 (none)

Model parameters:
 - Avg. Incubation days : 7.0000
 - Contact rate         : 2.0000
 - Prob. Recovery       : 0.1400
 - Prob. Transmission   : 0.1000

Distribution of the population at time 100:
  - (0) Susceptible :  9900 -> 8454
  - (1) Exposed     :   100 -> 140
  - (2) Infected    :     0 -> 135
  - (3) Recovered   :     0 -> 1271

Transition Probabilities:
 - Susceptible  1.00  0.00  0.00  0.00
 - Exposed      0.00  0.86  0.14  0.00
 - Infected     0.00  0.00  0.85  0.15
 - Recovered    0.00  0.00  0.00  1.00

<epiworldpy._core.ModelSEIRCONN at 0x7fe01a91bcb0>

Acknowledgements