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\n \n\n \n \n \n \n \n \n Planning as Inference in Epidemiological Dynamics Models.\n \n \n \n \n\n\n \n Wood, F.; Warrington, A.; Naderiparizi, S.; Weilbach, C.; Masrani, V.; Harvey, W.; Åšcibior, A.; Beronov, B.; Grefenstette, J.; Campbell, D.; and Nasseri, S. A.\n\n\n \n\n\n\n Frontiers in Artificial Intelligence, 4. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Planning paper\n  \n \n \n \"Planning arxiv\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{WOO-22,\n\tAUTHOR={Wood, Frank and Warrington, Andrew and Naderiparizi, Saeid and Weilbach, Christian and Masrani, Vaden and Harvey, William and Åšcibior, Adam and Beronov, Boyan and Grefenstette, John and Campbell, Duncan and Nasseri, S. Ali},   \n\tTITLE={Planning as Inference in Epidemiological Dynamics Models},      \n\tJOURNAL={Frontiers in Artificial Intelligence},      \n\tVOLUME={4},      \n\tYEAR={2022},      \n\tURL_Paper={https://www.frontiersin.org/article/10.3389/frai.2021.550603},       \n\turl_ArXiv={https://arxiv.org/abs/2003.13221},\n\tDOI={10.3389/frai.2021.550603},      \n\tISSN={2624-8212},   \n\tsupport = {D3M,COVID,ETALUMIS},\n  \tABSTRACT={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.}\n}\n\n
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\n 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.\n
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