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      Planning as Inference in Epidemiological Dynamics Models

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          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.

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          Most cited references108

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

            Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
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              A Contribution to the Mathematical Theory of Epidemics

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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                31 March 2022
                2021
                31 March 2022
                : 4
                : 550603
                Affiliations
                [1] 1 Department of Computer Science, University of British Columbia , Vancouver, BC, Canada
                [2] 2 Associate Academic Member and Canada CIFAR AI Chair, Mila Institute , Montreal, QC, Canada
                [3] 3 Department of Engineering Science, University of Oxford , Oxford, United Kingdom
                [4] 4 Epistemix Inc. , Pittsburgh, PA, United States
                Author notes

                Edited by: Weida Tong, National Center for Toxicological Research (FDA), United States

                Reviewed by: Theophane Weber, DeepMind Technologies Limited, United Kingdom

                Dong Wang, National Center for Toxicological Research (FDA), United States

                *Correspondence: S. Ali Nasseri, ali.nasseri@ 123456ubc.ca

                This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence

                Article
                550603
                10.3389/frai.2021.550603
                9009395
                0a060d36-349b-4c46-812d-e6b493236f03
                Copyright © 2022 Wood, Warrington, Naderiparizi, Weilbach, Masrani, Harvey, Ścibior, Beronov, Grefenstette, Campbell and Nasseri.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 April 2020
                : 25 October 2021
                Funding
                Funded by: Defense Advanced Research Projects Agency , doi 10.13039/100000185;
                Categories
                Artificial Intelligence
                Methods

                public health preparedness,epidemiological dynamics,bayesian inference,probabilistic programming,covid-19

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