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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.
  • Specht I; The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. ispecht@broadinstitute.org.
  • Sani K; Harvard College, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA. ispecht@broadinstitute.org.
  • Botti-Lodovico Y; The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
  • Hughes M; FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA.
  • Heumann K; The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
  • Bronson A; Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA.
  • Marshall J; Colorado Mesa University, Grand Junction, CO, 81501, USA.
  • Baron E; Colorado Mesa University, Grand Junction, CO, 81501, USA.
  • Parrie E; Colorado Mesa University, Grand Junction, CO, 81501, USA.
  • Glennon O; Colorado Mesa University, Grand Junction, CO, 81501, USA.
  • Fry B; COVIDCheck Colorado, Denver, CO, 80202, USA.
  • Colubri A; COVIDCheck Colorado, Denver, CO, 80202, USA.
  • Sabeti PC; Fathom Information Design, Boston, MA, 02114, USA.
Sci Rep ; 12(1): 1857, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671608
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Observational study Limits: Female / Humans / Male Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-021-02605-4

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Observational study Limits: Female / Humans / Male Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-021-02605-4