A network-based group testing strategy for colleges.

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Group testing is crucial for efficient COVID-19 surveillance.
• College towns present unique challenges and opportunities for disease control due to high population density and mobility.

Purpose of the Study:

• To develop and evaluate a novel, locally-informed group testing strategy for COVID-19 detection in college towns.
• To compare the efficacy of the proposed strategy against existing methods using real-world mobility data.

Main Methods:

• Constructed a mobility network using cellphone data from over 190,000 individuals.
• Simulated four different group testing strategies, including a novel approach requiring only local network information.
• Evaluated strategies based on their effectiveness in reducing disease spread and the number of tests required.

Main Results:

• The proposed group testing strategy significantly outperformed three baseline strategies.
• The novel method demonstrated superior effectiveness in reducing COVID-19 spread.
• Fewer tests were required using the proposed group testing approach compared to alternatives.

Conclusions:

• A novel, locally-informed group testing strategy is effective for managing COVID-19 in college towns.
• This approach offers a more efficient method for disease surveillance and control.
• The findings support the adoption of advanced group testing for public health interventions.