Probabilistic program inference in network-based epidemiological simulations.

Autor: Niklas Smedemark-Margulies, Robin Walters, Heiko Zimmermann, Lucas Laird, Christian van der Loo, Neela Kaushik, Rajmonda Caceres, Jan-Willem van de Meent
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: PLoS Computational Biology, Vol 18, Iss 11, p e1010591 (2022)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1010591
Popis: Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. We demonstrate the effectiveness of probabilistic programming for parameter inference in these models. We consider an agent-based simulation that represents mobility networks as degree-corrected stochastic block models, whose parameters we estimate from cell phone co-location data. We then use probabilistic program inference methods to approximate the distribution over disease transmission parameters conditioned on reported cases and deaths. Our experiments demonstrate that the resulting models improve the quality of fit in multiple geographies relative to baselines that do not model network topology.
Databáze: Directory of Open Access Journals
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