Probabilistic program inference in network-based epidemiological simulations
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 07, 2022
- Source ID
- 10.1371/journal.pcbi.1010591
Entities
People
- Christian van der Loo
- Heiko Zimmermann
- Jan-Willem van de Meent
- Lucas Laird
- Neela Kaushik
- Niklas Smedemark-Margulies
- Rajmonda S. Caceres
- Robin Walters
Organizations
- 3M
- Air Force Research Laboratory
- Defense Advanced Research Projects Agency
- Intel Corporation
- MIT Lincoln Laboratory
- National Science Foundation
- Northeastern University
- Office Of The Under Secretary Of Defense