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Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
Cara E Brook1, Graham R Northrup2, Alexander J Ehrenberg3
1Department of Integrative Biology, University of California, Berkeley, United States; Department of Ecology and Evolution, University of Chicago, United States.
Controlling COVID-19 spread on campuses requires combining behavioral changes like group size limits with frequent asymptomatic surveillance testing. This strategy effectively identifies infections, reduces transmission, and makes epidemics more predictable.
Area of Science:
- Epidemiology
- Public Health
- Infectious Disease Modeling
Background:
- Asymptomatic and presymptomatic transmission of SARS-CoV-2 (COVID-19) complicates control via traditional non-pharmaceutical interventions (NPIs).
- US universities implemented asymptomatic surveillance testing to manage campus outbreaks during the 2020-2021 and 2021-2022 academic years.
Purpose of the Study:
- To develop a stochastic branching process model for COVID-19 dynamics at UC Berkeley.
- To advise on optimal control strategies for university environments, integrating behavioral interventions and surveillance testing.
Main Methods:
- Developed a stochastic branching process model incorporating group size limits, symptom-based isolation, contact tracing, and asymptomatic surveillance testing.
- Modeled COVID-19 transmission dynamics within a university setting.
Main Results:
- Behavioral interventions, such as group size limits (≤6) and rapid isolation of symptomatic cases, are cost-effective for epidemic control.
- Asymptomatic surveillance testing effectively manages uncertainty from infections without symptoms, prioritizing frequent testing with rapid turnaround over high sensitivity.
- Contact tracing amplifies the impact of all isolations, improving intervention effectiveness.
- Combining NPIs with surveillance testing reduces daily case count variability and allows for predictable epidemic trajectories.
- Targeted testing of high-risk individuals can control broader community epidemics.
- 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 settings.
- The developed modeling tool can guide academic and professional communities in implementing effective return-to-work strategies.
- Frequent, rapid asymptomatic testing is crucial for managing SARS-CoV-2 transmission, especially given the prevalence of infections without symptoms.
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