The Case for Altruism in Institutional Diagnostic Testing

Male Bioinformatics Epidemiology Science Infectious Disease Microbiology Article 03 medical and health sciences COVID-19 Testing 0302 clinical medicine Diagnosis Prevalence Computational models Humans Q R Computational Biology COVID-19 Community Health and Preventive Medicine Health policy community transmission testing Computational biology and bioinformatics 3. Good health Occupational Health and Industrial Hygiene Virus Diseases Infectious diseases Medicine Epidemiological Models Female Public Health Contact Tracing
DOI: 10.1101/2021.03.16.21253669 Publication Date: 2021-03-24T18:30:19Z
ABSTRACT
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.
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