Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction

BACKGROUND: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders co...

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Autores principales: Morrison, Douglas E., Nianogo, Roch, Manuel, Vladimir, Arah, Onyebuchi A., Anderson, Nathaniel, Kuo, Tony, Inkelas, Moira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941563/
https://www.ncbi.nlm.nih.gov/pubmed/36825137
http://dx.doi.org/10.3389/fpubh.2023.856940
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author Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
author_facet Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
author_sort Morrison, Douglas E.
collection PubMed
description BACKGROUND: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. METHODS: We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. RESULTS: The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. CONCLUSIONS: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.
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spelling pubmed-99415632023-02-22 Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction Morrison, Douglas E. Nianogo, Roch Manuel, Vladimir Arah, Onyebuchi A. Anderson, Nathaniel Kuo, Tony Inkelas, Moira Front Public Health Public Health BACKGROUND: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. METHODS: We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. RESULTS: The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. CONCLUSIONS: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies. Frontiers Media S.A. 2023-02-07 /pmc/articles/PMC9941563/ /pubmed/36825137 http://dx.doi.org/10.3389/fpubh.2023.856940 Text en Copyright © 2023 Morrison, Nianogo, Manuel, Arah, Anderson, Kuo and Inkelas. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Public Health
Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title_full Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title_fullStr Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title_full_unstemmed Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title_short Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction
title_sort modeling covid-19 infection dynamics and mitigation strategies for in-person k-6 instruction
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941563/
https://www.ncbi.nlm.nih.gov/pubmed/36825137
http://dx.doi.org/10.3389/fpubh.2023.856940
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