Program in Bioinformatics and Integrative Biology; Department of Microbiology and Physiological Systems
Bioinformatics | Computational Biology | Health Policy | Infectious Disease | Occupational Health and Industrial Hygiene | Virus Diseases
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.
Computational biology and bioinformatics, Computational models, Health policy, Infectious diseases
Rights and Permissions
Copyright © The Author(s) 2022. Open Access This 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/.
DOI of Published Version
Specht I, Sani K, Botti-Lodovico Y, Hughes M, Heumann K, Bronson A, Marshall J, Baron E, Parrie E, Glennon O, Fry B, Colubri A, Sabeti PC. The case for altruism in institutional diagnostic testing. Sci Rep. 2022 Feb 3;12(1):1857. doi: 10.1038/s41598-021-02605-4. PMID: 35115545; PMCID: PMC8813946. Link to article on publisher's site
Specht I, Sani K, Botti-Lodovico Y, Hughes M, Heumann K, Bronson A, Marshall J, Baron E, Parrie E, Glennon O, Fry B, Colubri A, Sabeti PC. (2022). The case for altruism in institutional diagnostic testing. COVID-19 Publications by UMass Chan Authors. https://doi.org/10.1038/s41598-021-02605-4. Retrieved from https://escholarship.umassmed.edu/covid19/383
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.