Planning as Inference in Epidemiological Models
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 policymaking could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
Cite
@article{arxiv.2003.13221,
title = {Planning as Inference in Epidemiological Models},
author = {Frank Wood and Andrew Warrington and Saeid Naderiparizi and Christian Weilbach and Vaden Masrani and William Harvey and Adam Scibior and Boyan Beronov and John Grefenstette and Duncan Campbell and Ali Nasseri},
journal= {arXiv preprint arXiv:2003.13221},
year = {2022}
}
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