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83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening

ABSTRACT IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening an...

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Autores principales: Morrison, Douglas E., Nianogo, Roch, Manuel, Vladimir G., Arah, Onyebuchi A., Anderson, Nathaniel, Kuo, Tony, Inkelas, Moira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827848/
http://dx.doi.org/10.1017/cts.2021.469
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author Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir G.
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
author_facet Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir G.
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
author_sort Morrison, Douglas E.
collection PubMed
description ABSTRACT IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening and maintenance of in-person learning. OBJECTIVES/GOALS: School closures resulting from the COVID-19 pandemic disrupt student education and health and exacerbate inequities. Public health agencies and school districts currently lack pragmatic models to assess the effects of potential strategies for resuming and maintaining in-person learning on outcomes such as transmission and attendance. METHODS/STUDY POPULATION: This study explored how various combinations of transmission-mitigating interventions affect health and learning outcomes in a range of underlying epidemiological conditions. The CTSA science team developed a conceptual framework and an agent-based simulation model with parameters including prevalence, transmission, testing, preventive and responsive actions, infection control, population behavior and awareness, and the potential impact of vaccine adoption and exemption policies. The team partnered with a large school district to ensure relevance of the program components to decision-making. RESULTS/ANTICIPATED RESULTS: The model shows that no single program element or condition ensures safety. Combining interventions can result in synergy in the mitigation efforts. Even without testing, an efficient health screening process with forthcoming risk reporting, combined with on-campus infection control, can reduce on-campus transmission. The resulting model is accessible online to enable exploration of likely scenarios. It is adaptable as COVID-19 science evolves, including for testing and vaccines. DISCUSSION/SIGNIFICANCE OF FINDINGS: This research provides public health agencies and school districts with a model that couples local conditions with programmatic elements to help inform the local COVID-19 response, recognizing that decisions about the school community are often complex politically, technically, and operationally when it comes to addressing a health crisis.
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spelling pubmed-88278482022-03-04 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening Morrison, Douglas E. Nianogo, Roch Manuel, Vladimir G. Arah, Onyebuchi A. Anderson, Nathaniel Kuo, Tony Inkelas, Moira J Clin Transl Sci Clinical Epidemiology ABSTRACT IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening and maintenance of in-person learning. OBJECTIVES/GOALS: School closures resulting from the COVID-19 pandemic disrupt student education and health and exacerbate inequities. Public health agencies and school districts currently lack pragmatic models to assess the effects of potential strategies for resuming and maintaining in-person learning on outcomes such as transmission and attendance. METHODS/STUDY POPULATION: This study explored how various combinations of transmission-mitigating interventions affect health and learning outcomes in a range of underlying epidemiological conditions. The CTSA science team developed a conceptual framework and an agent-based simulation model with parameters including prevalence, transmission, testing, preventive and responsive actions, infection control, population behavior and awareness, and the potential impact of vaccine adoption and exemption policies. The team partnered with a large school district to ensure relevance of the program components to decision-making. RESULTS/ANTICIPATED RESULTS: The model shows that no single program element or condition ensures safety. Combining interventions can result in synergy in the mitigation efforts. Even without testing, an efficient health screening process with forthcoming risk reporting, combined with on-campus infection control, can reduce on-campus transmission. The resulting model is accessible online to enable exploration of likely scenarios. It is adaptable as COVID-19 science evolves, including for testing and vaccines. DISCUSSION/SIGNIFICANCE OF FINDINGS: This research provides public health agencies and school districts with a model that couples local conditions with programmatic elements to help inform the local COVID-19 response, recognizing that decisions about the school community are often complex politically, technically, and operationally when it comes to addressing a health crisis. Cambridge University Press 2021-03-30 /pmc/articles/PMC8827848/ http://dx.doi.org/10.1017/cts.2021.469 Text en © The Association for Clinical and Translational Science 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Epidemiology
Morrison, Douglas E.
Nianogo, Roch
Manuel, Vladimir G.
Arah, Onyebuchi A.
Anderson, Nathaniel
Kuo, Tony
Inkelas, Moira
83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title_full 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title_fullStr 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title_full_unstemmed 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title_short 83649 Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening
title_sort 83649 modeling covid-19 infection dynamics and program interventions for k-12 school re-opening
topic Clinical Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827848/
http://dx.doi.org/10.1017/cts.2021.469
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