Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• The high proportion of asymptomatic and presymptomatic infections from SARS-CoV-2 (COVID-19) complicates traditional non-pharmaceutical interventions (NPIs).
• US universities implemented asymptomatic surveillance testing to supplement NPIs and manage campus outbreaks during the 2020-2021 and 2021-2022 academic years.

Approach:

• Developed a stochastic branching process model of COVID-19 dynamics at UC Berkeley to evaluate control strategies.
• Integrated behavioral interventions (group size limits, symptom-based isolation, contact tracing) with asymptomatic surveillance testing.

Key Points:

• Behavioral interventions, particularly group size limits of six or fewer, are cost-effective for reducing superspreading.
• Rapid isolation of symptomatic cases can halt epidemics, contingent on asymptomatic transmission rates.
• Frequent asymptomatic surveillance testing with rapid turnaround is more effective than high test sensitivity for managing uncertainty.
• Contact tracing amplifies the impact of all isolations, rendering delayed interventions effective.
• Combining NPIs and surveillance testing stabilizes daily case counts, leading to more predictable epidemics.
• Targeted testing of high-risk individuals can control broader community transmission.
• Asymptomatic surveillance remains effective in vaccinated settings for identifying breakthrough infections and reducing caseloads.

Conclusions:

• A combination of behavioral interventions and asymptomatic surveillance testing provides a robust strategy for controlling COVID-19 in university environments.
• The developed modeling tool and blueprint can guide other communities in implementing effective return-to-work strategies.