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Planning as Inference in Epidemiological Models
lib:dc5f4d2eaa4dd0b4 (v1.0.0)
Authors: Frank Wood,Andrew Warrington,Saeid Naderiparizi,Christian Weilbach,Vaden Masrani,William Harvey,Adam Scibior,Boyan Beronov,Ali Nasseri
ArXiv: 2003.13221
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/2003.13221v2
In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
ArXiv: 2003.13221
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/2003.13221v2
In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
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