The Case for Altruism in Institutional Diagnostic Testing

• DOI: 10.1101/2021.03.16.21253669
• Publication Date: 2021-03-24T18:30:19Z
• Authors: Ivan Specht; Kian Sani; Yolanda Botti-Lod...; Michael Hughes; Kristin Heumann; Amy Bronson; John Marshall; Emily Baron; Eric Parrie; Olivia Glennon; Ben Fry; Andrés Colubri; Pardis C. Sabeti
• Keywords: 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

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.