AUTHOR=Wood Frank , Warrington Andrew , Naderiparizi Saeid , Weilbach Christian , Masrani Vaden , Harvey William , Ścibior Adam , Beronov Boyan , Grefenstette John , Campbell Duncan , Nasseri S. Ali TITLE=Planning as Inference in Epidemiological Dynamics Models JOURNAL=Frontiers in Artificial Intelligence VOLUME=4 YEAR=2022 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.550603 DOI=10.3389/frai.2021.550603 ISSN=2624-8212 ABSTRACT=

In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policy-making could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.