Planning as Inference in Epidemiological Models

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 choic...

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Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Wood, Frank, Warrington, Andrew, Naderiparizi, Saeid, Weilbach, Christian, Vaden Masrani, Harvey, William, Scibior, Adam, Beronov, Boyan, Grefenstette, John, Campbell, Duncan, Nasseri, Ali
Format: Article
Language:English
Subjects:
Automation
Computer simulation
COVID-19
Disease control
Epidemiology
Infectious diseases
Inference
Software
Software development tools
Stability
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Summary: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 policymaking could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
ISSN:2331-8422
DOI:10.48550/arxiv.2003.13221