Examining SARS-CoV-2 Interventions in Residential Colleges Using an Empirical Network

Area of Science:

• Epidemiology
• Public Health
• Network Science

Background:

• University reopening strategies for SARS-CoV-2 require robust modeling.
• Previous models relied on simulated data, lacking real-world applicability.

Approach:

• Utilized an empirical proximity network of college freshmen derived from smartphone Bluetooth data.
• Simulated SARS-CoV-2 spread to investigate the efficacy of interventions like testing, isolation, mask wearing, and social distancing.
• Accounted for implementation challenges and imperfect compliance.

Key Points:

• Frequent testing significantly reduces spread when mask wearing and social distancing are not widely adopted.
• Testing has limited impact on reducing spread if mask wearing and social distancing are ubiquitous.
• Moderate levels of population immunity substantially decrease new infections, particularly when combined with other interventions.

Conclusions:

• Testing and isolation are effective but offer limited benefits when other interventions are universally adopted.
• High adherence to masking and social distancing may permit reduced testing frequency (e.g., bi-weekly to monthly) for university campuses.
• Empirical network data provides a more realistic approach to modeling infectious disease spread in specific populations.