Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses

Frontiers in Digital Health

Digital Health

Munmun De Choudhury¹  Vedant Das Swain¹  Maanit Madan¹  Sonia Sargolzaei¹  B. Aditya Prakash¹  Gregory D. Abowd²  Jiajia Xie³  James Cai⁴  Lauren N. Steimle⁵ 

[1] College of Computing, Georgia Institute of Technology, Atlanta, GA, United States;College of Computing, Georgia Institute of Technology, Atlanta, GA, United States;College of Engineering, Northeastern University, Boston, MA, United States;College of Computing, Georgia Institute of Technology, Atlanta, GA, United States;H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States;Department of Computer Science, Brown University, Providence, RI, United States;H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States;

关键词: COVID-19;    mobility;    modeling;    policy;    non-pharmaceutical intervention;    WiFi;   

DOI  :  10.3389/fdgth.2023.1060828

 received in 2022-10-03, accepted in 2023-04-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility—a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

【 授权许可】

Unknown   
© 2023 Das Swain, Xie, Madan, Sargolzaei, Cai, De Choudhury, Abowd, Steimle and Prakash.