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The case for altruism in institutional diagnostic testing
Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813946/ https://www.ncbi.nlm.nih.gov/pubmed/35115545 http://dx.doi.org/10.1038/s41598-021-02605-4 |
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author | Specht, Ivan Sani, Kian Botti-Lodovico, Yolanda Hughes, Michael Heumann, Kristin Bronson, Amy Marshall, John Baron, Emily Parrie, Eric Glennon, Olivia Fry, Ben Colubri, Andrés Sabeti, Pardis C. |
author_facet | Specht, Ivan Sani, Kian Botti-Lodovico, Yolanda Hughes, Michael Heumann, Kristin Bronson, Amy Marshall, John Baron, Emily Parrie, Eric Glennon, Olivia Fry, Ben Colubri, Andrés Sabeti, Pardis C. |
author_sort | Specht, Ivan |
collection | PubMed |
description | Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members’ close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network. |
format | Online Article Text |
id | pubmed-8813946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88139462022-02-07 The case for altruism in institutional diagnostic testing Specht, Ivan Sani, Kian Botti-Lodovico, Yolanda Hughes, Michael Heumann, Kristin Bronson, Amy Marshall, John Baron, Emily Parrie, Eric Glennon, Olivia Fry, Ben Colubri, Andrés Sabeti, Pardis C. Sci Rep Article Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members’ close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network. Nature Publishing Group UK 2022-02-03 /pmc/articles/PMC8813946/ /pubmed/35115545 http://dx.doi.org/10.1038/s41598-021-02605-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Specht, Ivan Sani, Kian Botti-Lodovico, Yolanda Hughes, Michael Heumann, Kristin Bronson, Amy Marshall, John Baron, Emily Parrie, Eric Glennon, Olivia Fry, Ben Colubri, Andrés Sabeti, Pardis C. The case for altruism in institutional diagnostic testing |
title | The case for altruism in institutional diagnostic testing |
title_full | The case for altruism in institutional diagnostic testing |
title_fullStr | The case for altruism in institutional diagnostic testing |
title_full_unstemmed | The case for altruism in institutional diagnostic testing |
title_short | The case for altruism in institutional diagnostic testing |
title_sort | case for altruism in institutional diagnostic testing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813946/ https://www.ncbi.nlm.nih.gov/pubmed/35115545 http://dx.doi.org/10.1038/s41598-021-02605-4 |
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