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Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration.

Nicoló Gozzi1,2, Matteo Chinazzi2,3, Jessica T Davis2

  • 1ISI Foundation, Turin, Italy.

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|November 19, 2025
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Summary
This summary is machine-generated.

Epydemix is an open-source Python package simplifying the creation and calibration of epidemic models. It uses Approximate Bayesian Computation (ABC) for parameter inference, making complex modeling accessible to researchers and public health professionals.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Stochastic compartmental models are crucial for understanding epidemic dynamics.
  • Developing and calibrating these models, especially with real-world data and interventions, presents significant computational challenges.

Purpose of the Study:

  • To introduce Epydemix, an open-source Python package designed to streamline the development and calibration of stochastic compartmental epidemic models.
  • To provide a flexible framework integrating demographic data, contact matrices, and public health interventions.
  • To facilitate parameter inference and model calibration using Approximate Bayesian Computation (ABC) techniques.

Main Methods:

  • Epydemix supports flexible model structures and dynamic interventions.
  • It integrates various Approximate Bayesian Computation (ABC) methods, including rejection sampling and Sequential Monte Carlo (ABC-SMC).
  • The package is modular, allowing calibration of both internal and external models.

Main Results:

  • Demonstrated simulation of intervention-driven models with time-varying parameters.
  • Benchmarked calibration performance using synthetic epidemic data.
  • Illustrated retrospective case study with scenario projections under different intervention assumptions.

Conclusions:

  • Epydemix lowers the barrier for implementing advanced computational and inference approaches in epidemic modeling.
  • The package enhances accessibility for academic researchers and public health professionals.
  • It promotes wider adoption of sophisticated modeling techniques for disease control and prevention.