A tool for creating and testing compartmental epidemiological models faster than ever for the mathematical modeling of infectious diseases. An idea from https://github.com/henrifroese/infectious_disease_modelling.

DOI Information

Use epispot's DOI in your research! 🎆


Citation:

q9i, & QLabs. (2021, April 2). epispot/epispot: (Version 2.1.0). Zenodo. http://doi.org/10.5281/zenodo.4624423

Current Build Statuses

Workflow	Status
build.yml
coverage.yml
python-publish.yml
codeql-analysis.yml

Installation

Epispot can be installed via pip or via Anaconda. If using pip, install with:

pip install epispot

For Anaconda, install via conda-forge as:

conda config --add channels conda-forge
conda install -c conda-forge epispot

As a shorthand, use import epispot as epi.

You can also install the epispot-nightly package from pip:

pip install epispot-nightly

You can import it the same as import epispot as epi. Both packages cannot be used at the same time.

Getting Started

Start with the tutorials in tests/tutorials (see GitHub repo). Next, check out the documentation at https://epispot.github.io/epispot. You may also find it helpful to see the examples in the tests/examples folder.

Release Notes

See GitHub repo for more info.

Features

• SIR-based models

  • Susceptible
  • Infected
  • Recovered
  • Exposed
  • Dead
  • Critical
  • Hospitalized

• Custom-defined compartmental models

  • Create custom models using the Model class

• Graphing Capabilities

  • Plot real data from a .csv file
  • Plot model predictions interactively
  • Compare different model predictions

A Note on Documentation

Documentation can easily be accessed from function, class, and file docstrings. Docstrings provide additional documentation on a certain function. They can be accessed by the built-in Python help() command. These strings are formatted in Github-flavored markdown. Additionally, all files will have a 'STRUCTURE' label.

Credits

The epispot package is supported by the following contributors:

• Head of Software & Development: @quantum9innovation
• Head of Code Maintenance: @Quantalabs