Epydemix
The ABC of Epidemics
Epydemix provides a unified, open-source framework that enables researchers and public health practitioners to design, simulate, and calibrate epidemic models within a single environment.
Python Package
epydemix
An open-source Python package designed for flexible, modular, and data-driven epidemic modeling.
Epydemix 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.
Tools
epyForecast
A desktop app to run short-term forecasts using real data, powered by Epydemix.
Details on how to download EpyForecast will be available soon.
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.
Papers & scientific outputs
Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Corrado Gioannini, Luca Rossi, Marco Ajelli, Nicola Perra, Alessandro Vespignani
PLOS Computational Biology 21, no. 11 (2025): e1013735. view pdf
Resources
