Data-driven Testing Program Improves Detection of COVID-19 Cases and Reduces Community Transmission

COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability, and organizations must be prepared to detect and mitigate its risk to their people and activities. In this report we share key lessons learned from an adaptive COVID-19 testing program implemented at a mid-sized university in the Midwest. The program utilized two simple, diverse, and easily interpretable machine learning models to quickly and accurately predict which students were at elevated risk for contracting COVID-19 and should be called proactively for testing. Our adaptive testing cohorts produced positivity rates that were 26% higher than the random cohort: 0.58% positivity (95% CI 0.47% to 0.68%) from 19,171 tests, and 0.46% positivity (95% CI 0.41% to 0.51%) from 64,003 tests, respectively. Within 14 days of their selection, 2.94% of the adaptive cohort tested positive, compared to 1.27% of the random cohort. Close contacts who were predicted by the adaptive testing models received a COVID-19 test within an average of 0.94 days (95% CI 0.78 to 1.11) of the source testing positive, while those who were manually contact traced were tested in an average of 1.92 days (95% CI 1.81 to 2.02). These results suggest that machine learning strategies can improve surveillance testing effectiveness, especially in a university setting, by effectively distributing testing resources and potentially reducing community transmission.