Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration.
Saved in:
| Title: | Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration. |
|---|---|
| Authors: | Gozzi, Nicoló, Chinazzi, Matteo, Davis, Jessica T., Gioannini, Corrado, Rossi, Luca, Ajelli, Marco, Perra, Nicola, Vespignani, Alessandro |
| Source: | PLoS Computational Biology; 11/19/2025, Vol. 21 Issue 11, p1-18, 18p |
| Subject Terms: | EPIDEMIOLOGICAL models, OPEN source software, INFERENTIAL statistics, PUBLIC health, PYTHON programming language, STOCHASTIC models, PARAMETER estimation |
| Abstract: | We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. The framework supports flexible model structures that incorporate demographic information, age-stratified contact matrices, and dynamic public health interventions. A key feature of Epydemix is its integration of Approximate Bayesian Computation (ABC) techniques to perform parameter inference and model calibration through comparison between observed and simulated data. The package offers a range of ABC methods such as simple rejection sampling, simulation-budget-constrained rejection, and Sequential Monte Carlo (ABC-SMC). Epydemix is modular, and supports ABC-based calibration both for models defined within the package and for those developed externally. To demonstrate the computational framework capabilities, we discuss usage examples that include (i) simulating an intervention-driven model with time-varying parameters, and (ii) benchmarking calibration performance using synthetic epidemic data. We further illustrate the use of the package in a retrospective case study that includes scenario projections under alternative intervention assumptions. By lowering the barrier for the implementation of computational and inference approaches, Epydemix makes epidemic modeling more accessible to a wider range of users, from academic researchers to public health professionals. [ABSTRACT FROM AUTHOR] |
| Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!