Repeat SARS-CoV-2 testing models for residential college populations
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Repeat SARS-CoV-2 testing models for residential college populations Joseph T. Chang1 · Forrest W. Crawford2 · Edward H. Kaplan3 Received: 17 July 2020 / Accepted: 13 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios. Keywords SARS-CoV-2; COVID-19 · Repeat testing · Residential college coronavirus screening · Epidemic model · Probability model Highlights •
Model accounts for test frequency, test specificity, dependence of test sensitivity on time since infection, laboratory turnaround and individual notification delays,
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Edward H. Kaplan
[email protected] Joseph T. Chang [email protected] Forrest W. Crawford [email protected] 1
Department of Statistics and Data Science, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511-6814, USA
2
Department of Biostatistics, Department of Ecology and Evolutionary Biology, Yale School of Management, Department of Statistics and Data Science, Yale School of Public Health, PO Box 208034, New Haven, CT, 06510, USA
3
Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, 165 Whitney Avenue, New Haven, CT 06511, USA
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imported infections from off campus, intensity and time dependence of transmissions, and initial infection conditions Characterizes the probability distribution of the time from infection until detection and isolation Compares random and regular testing schedules and analyzes the more realistic and also
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