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Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening

After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spr...

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Detalles Bibliográficos
Autores principales: Yang, Qihui, Gruenbacher, Don M., Scoglio, Caterina M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919707/
https://www.ncbi.nlm.nih.gov/pubmed/35285285
http://dx.doi.org/10.1098/rsif.2021.0920
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author Yang, Qihui
Gruenbacher, Don M.
Scoglio, Caterina M.
author_facet Yang, Qihui
Gruenbacher, Don M.
Scoglio, Caterina M.
author_sort Yang, Qihui
collection PubMed
description After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R(0) = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial.
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spelling pubmed-89197072022-03-14 Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening Yang, Qihui Gruenbacher, Don M. Scoglio, Caterina M. J R Soc Interface Life Sciences–Physics interface After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R(0) = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial. The Royal Society 2022-03-14 /pmc/articles/PMC8919707/ /pubmed/35285285 http://dx.doi.org/10.1098/rsif.2021.0920 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Physics interface
Yang, Qihui
Gruenbacher, Don M.
Scoglio, Caterina M.
Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title_full Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title_fullStr Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title_full_unstemmed Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title_short Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
title_sort estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
topic Life Sciences–Physics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919707/
https://www.ncbi.nlm.nih.gov/pubmed/35285285
http://dx.doi.org/10.1098/rsif.2021.0920
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