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Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic

Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities invo...

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Autores principales: McCarthy, John E., Dewitt, Barry D., Dumas, Bob A., McCarthy, Myles T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842882/
https://www.ncbi.nlm.nih.gov/pubmed/33507962
http://dx.doi.org/10.1371/journal.pone.0245381
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author McCarthy, John E.
Dewitt, Barry D.
Dumas, Bob A.
McCarthy, Myles T.
author_facet McCarthy, John E.
Dewitt, Barry D.
Dumas, Bob A.
McCarthy, Myles T.
author_sort McCarthy, John E.
collection PubMed
description Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one’s seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. A linearity assumption governing several potentially modifiable risks factors—such as duration of the activity, density of participants, and infectiousness of the attendees—makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.
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spelling pubmed-78428822021-02-02 Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic McCarthy, John E. Dewitt, Barry D. Dumas, Bob A. McCarthy, Myles T. PLoS One Research Article Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one’s seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. A linearity assumption governing several potentially modifiable risks factors—such as duration of the activity, density of participants, and infectiousness of the attendees—makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model. Public Library of Science 2021-01-28 /pmc/articles/PMC7842882/ /pubmed/33507962 http://dx.doi.org/10.1371/journal.pone.0245381 Text en © 2021 McCarthy et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
McCarthy, John E.
Dewitt, Barry D.
Dumas, Bob A.
McCarthy, Myles T.
Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title_full Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title_fullStr Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title_full_unstemmed Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title_short Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic
title_sort modeling the relative risk of sars-cov-2 infection to inform risk-cost-benefit analyses of activities during the sars-cov-2 pandemic
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842882/
https://www.ncbi.nlm.nih.gov/pubmed/33507962
http://dx.doi.org/10.1371/journal.pone.0245381
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