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dc.contributor.authorSpecht, Ivan
dc.contributor.authorSani, Kian
dc.contributor.authorBotti-Lodovico, Yolanda
dc.contributor.authorHughes, Michael
dc.contributor.authorHeumann, Kristin
dc.contributor.authorBronson, Amy
dc.contributor.authorMarshall, John
dc.contributor.authorBaron, Emily
dc.contributor.authorParrie, Eric
dc.contributor.authorGlennon, Olivia
dc.contributor.authorFry, Ben
dc.contributor.authorColubri, Andrés
dc.contributor.authorSabeti, Pardis C.
dc.date2022-08-11T08:08:27.000
dc.date.accessioned2022-08-23T15:55:39Z
dc.date.available2022-08-23T15:55:39Z
dc.date.issued2021-03-25
dc.date.submitted2021-06-23
dc.identifier.citation<p>medRxiv 2021.03.16.21253669; doi: https://doi.org/10.1101/2021.03.16.21253669. <a href="https://doi.org/10.1101/2021.03.16.21253669" target="_blank" title="view preprint in MedRxiv">Link to preprint on medRxiv</a></p>
dc.identifier.doi10.1101/2021.03.16.21253669
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29806
dc.description<p>This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.</p> <p>The PDF available for download is Version 2 of this preprint. The complete version history of this preprint is available at <a href="https://doi.org/10.1101/2021.03.16.21253669" target="_blank" title="medRxiv">https://doi.org/10.1101/2021.03.16.21253669</a>.</p>
dc.description.abstractAmid 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.
dc.language.isoen_US
dc.relationNow published in Scientific Reports doi: 10.1038/s41598-021-02605-4
dc.rightsThe copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEpidemiology
dc.subjectcommunity transmission
dc.subjectCOVID-19
dc.subjecttesting
dc.subjectCommunity Health and Preventive Medicine
dc.subjectDiagnosis
dc.subjectEpidemiology
dc.subjectInfectious Disease
dc.subjectMicrobiology
dc.subjectVirus Diseases
dc.titleThe Case for Altruism in Institutional Diagnostic Testing [preprint]
dc.typePreprint
dc.source.journaltitlemedRxiv
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=3034&amp;context=faculty_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/2015
dc.identifier.contextkey23487311
refterms.dateFOA2022-08-23T15:55:40Z
html.description.abstract<p><p id="x-x-x-x-x-p-4">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.</p>
dc.identifier.submissionpathfaculty_pubs/2015
dc.contributor.departmentDepartment of Microbiology and Physiological Systems
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology


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The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.