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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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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. |
format | Online Article Text |
id | pubmed-8919707 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
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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