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Probabilistic program inference in network-based epidemiological simulations

Author(s)
Smedemark-Margulies, Niklas; Walters, Robin; Zimmermann, Heiko; Laird, Lucas; van der Loo, Christian; Kaushik, Neela; Caceres, Rajmonda; van de Meent, Jan-Willem; ... Show more Show less
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Abstract
<jats:p>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.</jats:p>
Date issued
2022-11-07
URI
https://hdl.handle.net/1721.1/146545
Department
Lincoln Laboratory
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
ISSN
1553-7358
Keywords
Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modeling and Simulation, Ecology, Evolution, Behavior and Systematics

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