Probabilistic program inference in network-based epidemiological simulations
Name
journal.pcbi.1010591.pdf
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6.78 MB
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Author(s)
Smedemark-Margulies, Niklas
Walters, Robin
Zimmermann, Heiko
Laird, Lucas
van der Loo, Christian
Kaushik, Neela
Caceres, Rajmonda
van de Meent, Jan-Willem
Date Issued
November 7, 2022
Publisher
Public Library of Science (PLoS)
Citation
Smedemark-Margulies, Niklas, Walters, Robin, Zimmermann, Heiko, Laird, Lucas, van der Loo, Christian et al. 2022. "Probabilistic program inference in network-based epidemiological simulations." 18 (11).
Version
Final published version
Abstract
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.
Subjects
Computational Theory and Mathematics
Cellular and Molecular Neuroscience
Genetics
Molecular Biology
Ecology
Modeling and Simulation
Ecology, Evolution, Behavior and Systematics
MIT Department
Lincoln Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1010591
