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The Case for Altruism in Institutional Diagnostic Testing
Ivan Specht; Kian Sani; Yolanda Botti-Lodovico; Michael Hughes; Kristin Heumann; Amy Bronson; John Marshall; Emily Baron; Eric Parrie; Olivia Glennon; Ben Fry; Andres Colubri; Pardis C Sabeti.
Afiliação
  • Ivan Specht; The Broad Institute of MIT and Harvard; Harvard University
  • Kian Sani; The Broad Institute of MIT and Harvard; Harvard University
  • Yolanda Botti-Lodovico; The Broad Institute of MIT and Harvard; Howard Hughes Medical Institute
  • Michael Hughes; Colorado Mesa University
  • Kristin Heumann; Colorado Mesa University
  • Amy Bronson; Colorado Mesa University
  • John Marshall; Colorado Mesa University
  • Emily Baron; COVIDCheck Colorado
  • Eric Parrie; COVIDCheck Colorado
  • Olivia Glennon; Fathom Information Design
  • Ben Fry; Fathom Information Design
  • Andres Colubri; The Broad Institute of MIT and Harvard; Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School
  • Pardis C Sabeti; The Broad Institute or MIT and Harvard; Harvard University; Massachusetts Consortium on Pathogen Readiness; Howard Hughes Medical Institute
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21253669
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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.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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