epydemix
The ABC of Epidemics
epydemix is an open-source Python package designed for flexible, modular, and data-driven epidemic modeling.
It supports the full modeling pipeline—from constructing stochastic compartmental models to running simulations, integrating real-world data, and calibrating parameters. Users can incorporate age-structured contact patterns, dynamic interventions, and population demographics with ease. Built-in Approximate Bayesian Computation (ABC) methods enable robust parameter estimation and model fitting, supporting forecasting, scenario exploration, and policy-relevant analyses. Epydemix bridges the gap between theoretical modeling and practical application, helping researchers and public health professionals translate models into actionable insights.
Data
Epydemix includes access to population data and synthetic age-stratified contact matrices for over 400 countries and regions worldwide. These datasets enable users to construct realistic, demographically grounded epidemic models. Contact matrices capture interactions across key settings — home, work, school, and community — and are provided in multiple formats to support a range of modeling scenarios.
Tutorials
We provide a series of tutorials to help you get started with epydemix. Each tutorial includes annotated code examples to support practical, hands-on learning.
About
Epidemic modeling often requires navigating a complex array of tools for simulation, data integration, and parameter calibration. Epydemix provides a unified, open-source framework that streamlines this process, enabling researchers and public health practitioners to design, simulate, and calibrate models within a single environment. Developed through a collaborative effort across multiple institutions, Epydemix is a continuously evolving platform. New modules and capabilities are actively under development, ensuring that the tool remains responsive to emerging research needs and public health challenges.
This project is supported by cooperative agreement CDC-RFA-FT-23-0069 from the CDC’s Center for Forecasting and Outbreak Analytics. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. We also acknowledge the support of the Lagrange Project at the Institute for Scientific Interchange Foundation (ISI Foundation) funded by Fondazione Cassa di Risparmio di Torino.
Resources
Team
ISI Foundation
Nicolò Gozzi
Corrado Gioannini
Luca Rossi
Queen Mary University of London
Nicola Perra
Northeastern University
Alessandro Vespignani
Jessica Davis
Matteo Chinazzi
Indiana University
Marco Ajelli
epydemix
