Abstract

Predominantly asymptomatic infections, such as those for SARS-CoV-2, require robust surveillance testing to identify people who are unknowingly spreading the virus. The US Air Force Academy returned to in-person classes for more than 4000 cadets aged 18-26 years during the fall 2020 semester to meet graduation and leadership training requirements. To enable this sustained cadet footprint, the institution developed a dynamic SARS-CoV-2 response plan using near-real-time data to inform decisions and trigger policies. A surveillance testing program based on mathematical modeling and a policy-driven campus reset option provided a scaled approach to react to SARS-CoV-2 conditions. This program adequately controlled the spread of the virus for the first 2 months of the academic semester but failed to predict or initially mitigate a significant outbreak in the second half of the semester. Although this approach did not completely eliminate SARS-CoV-2 infections in the population, it served as an early warning system to alert public health authorities to potential issues, which allowed timely responses while containment was still possible.

Full Text
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