Repeat SARS-CoV-2 testing models for residential college populations

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: COVID-19; Epidemic model; Probability model; Repeat testing; Residential college coronavirus screening; SARS-CoV-2.

Fig. 1

Impact of isolation. For a person isolated at a random time T after infection, the blue shaded area shows the expected number of further infections whose transmissions are prevented by the isolation, and the red area shows the expected number of further infections that escape isolation and are still transmitted

Fig. 2

Examples of test sensitivity functions

Fig. 3

Probability that the time from infection to isolation exceeds a. Here the isolation delay in all four scenarios was taken to be 1 day

Fig. 4

Transmission curves under different testing scenarios

Fig. 5

A web app available at https://jtwchang.shinyapps.io/testing/ that implements the model and facilitates exploring a variety of scenarios

Fig. 6

Two estimated generation time distributions found in published studies. We refer to these distributions as featuring relatively early transmission [28] and late transmission [25]

Fig. 7

Cumulative infections and undetected infections over time in scenarios with testing every 3 days and fixed infectivity function, sensitivity function, and delays, as R₀ varies