November 6, 2020

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis

Category: Article Summary

Topic: Modeling and Prediction

Keywords (Tags): modeling prediction, schools testing

• A decision-tree analysis evaluating five SARS-CoV-2 testing strategies for college students returning to campus found that testing all students upon arrival and then retesting them seven days later identified the greatest number of cases. The five different strategies evaluated were: (1) classifying students with symptoms as having COVID-19, (2) RT-PCR testing for symptomatic students, (3) RT-PCR testing for all students, (4) RT-PCR testing for all students and retesting only symptomatic students with a negative first test, and (5) RT-PCR testing for all students and retesting all students with a negative first test seven days after the initial test. The percentage of correctly identified infections was 41%, 29%, 54%, 73%, and 87% for Strategies 1–5, respectively. The authors note that if test cost is a concern, schools can consider testing all returning students, and deciding whether or not to repeat testing based on the measured prevalence of infection.

Van Pelt et al. (Nov 2020). Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis. Journal of Adolescent Health. https://doi.org/10.1016/j.jadohealth.2020.09.038