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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.
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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History
Date
received
: 09
April
2020
Date
accepted
: 25
October
2021
Funding
Funded by: Defense Advanced Research Projects Agency
, doi 10.13039/100000185;