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
AUTHORS (13)
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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