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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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013735","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T18:44:13Z","timestamp":1763577853000},"page":"e1013735","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9996-3194","authenticated-orcid":true,"given":"Nicol\u00f2","family":"Gozzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Chinazzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica T.","family":"Davis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4691-2377","authenticated-orcid":true,"given":"Corrado","family":"Gioannini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Rossi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Ajelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Perra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Vespignani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"pcbi.1013735.ref001","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.plrev.2024.10.011","article-title":"Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data","volume":"51","author":"G Chowell","year":"2024","journal-title":"Phys Life Rev"},{"issue":"1","key":"pcbi.1013735.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1002803","article-title":"Approximate Bayesian computation","volume":"9","author":"M Sunn\u00e5ker","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1013735.ref003","doi-asserted-by":"crossref","first-page":"100368","DOI":"10.1016\/j.epidem.2019.100368","article-title":"Approximate Bayesian Computation for infectious disease modelling","volume":"29","author":"A Minter","year":"2019","journal-title":"Epidemics"},{"issue":"31","key":"pcbi.1013735.ref004","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1098\/rsif.2008.0172","article-title":"Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems","volume":"6","author":"T Toni","year":"2009","journal-title":"J R Soc Interface"},{"key":"pcbi.1013735.ref005","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/1471-2334-11-37","article-title":"The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale","volume":"11","author":"W Van den Broeck","year":"2011","journal-title":"BMC Infect Dis"},{"key":"pcbi.1013735.ref006","first-page":"2020","article-title":"COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2","author":"NB Noll","year":"2020","journal-title":"MedRxiv"},{"key":"pcbi.1013735.ref007","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/1471-2105-13-76","article-title":"EpiFire: an open source C++ library and application for contact network epidemiology","volume":"13","author":"T Hladish","year":"2012","journal-title":"BMC Bioinformatics"},{"issue":"7","key":"pcbi.1013735.ref008","article-title":"Covasim: an agent-based model of COVID-19 dynamics and interventions","volume":"17","author":"CC Kerr","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1013735.ref009","doi-asserted-by":"crossref","unstructured":"Priest JD, Kishore A, Machi L, Kuhlman CJ, Machi D, Ravi SS. 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