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Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis
BACKGROUND: Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. OBJECTIVE: We aimed to estim...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8030657/ https://www.ncbi.nlm.nih.gov/pubmed/33667173 http://dx.doi.org/10.2196/24292 |
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author | Benneyan, James Gehrke, Christopher Ilies, Iulian Nehls, Nicole |
author_facet | Benneyan, James Gehrke, Christopher Ilies, Iulian Nehls, Nicole |
author_sort | Benneyan, James |
collection | PubMed |
description | BACKGROUND: Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. OBJECTIVE: We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. METHODS: We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. RESULTS: Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. CONCLUSIONS: Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations. |
format | Online Article Text |
id | pubmed-8030657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-80306572021-05-07 Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis Benneyan, James Gehrke, Christopher Ilies, Iulian Nehls, Nicole JMIR Public Health Surveill Original Paper BACKGROUND: Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. OBJECTIVE: We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. METHODS: We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. RESULTS: Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. CONCLUSIONS: Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations. JMIR Publications 2021-04-07 /pmc/articles/PMC8030657/ /pubmed/33667173 http://dx.doi.org/10.2196/24292 Text en ©James Benneyan, Christopher Gehrke, Iulian Ilies, Nicole Nehls. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 07.04.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Benneyan, James Gehrke, Christopher Ilies, Iulian Nehls, Nicole Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title | Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title_full | Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title_fullStr | Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title_full_unstemmed | Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title_short | Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis |
title_sort | community and campus covid-19 risk uncertainty under university reopening scenarios: model-based analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8030657/ https://www.ncbi.nlm.nih.gov/pubmed/33667173 http://dx.doi.org/10.2196/24292 |
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