Campus Reset: Dynamic Planning and Response to SARS-CoV-2 Infections at the US Air Force Academy.

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.

Efficiency of pooled surveillance testing in academic labs to detect and inhibit COVID-19 outbreaks.

Robust surveillance testing is a key strategic plan to prevent COVID-19 outbreaks and slow the spread of the SARS-CoV-2 pandemic; however, limited resources, facilities and time often impair the implementation of a widespread surveillance effort. To mitigate these resource limitations, we employed a strategy of pooling samples, reducing reagent cost and processing time. Through utilizing academic faculty and labs, successful pooled surveillance testing was conducted throughout Fall 2020 semester to detect positive SARS-CoV-2 infections in a population of 4400 students. During the semester, over 25,000 individual COVID status evaluations were made by pooling eight individual samples into one quantitative reverse transcription polymerase chain reaction. This pooled surveillance strategy was highly effective at detecting infection and significantly reduced financial burden and cost by $3.6 million.

Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

A closed-form equation, the Fizzle Equation, was derived from a mathematical model predicting Severe Acute Respiratory Virus-2 dynamics, optimized for a 4000-student university cohort. This equation sought to determine the frequency and percentage of random surveillance testing required to prevent an outbreak, enabling an institution to develop scientifically sound public health policies to bring the effective reproduction number of the virus below one, halting virus progression. Model permutations evaluated the potential spread of the virus based on the level of random surveillance testing, increased viral infectivity and implementing additional safety measures. The model outcomes included: required level of surveillance testing, the number of infected individuals, and the number of quarantined individuals. Using the derived equations, this study illustrates expected infection load and how testing policy can prevent outbreaks in an institution. Furthermore, this process is iterative, making it possible to develop responsive policies scaling the amount of surveillance testing based on prior testing results, further conserving resources.