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

Abstract

Infectious diseases, like COVID-19, pose serious challenges to university campuses, and they typically adopt closure as a non-pharmaceutical intervention to control spread early and ensure a gradual return to normalcy. These policies, like remote instruction (SQ), reduce potential contact but also have broad side-effects on campus by hampering 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 (WF) and in turn explore more granular policies like localized closures (LC). WF can construct contact networks that capture behavior in a variety of spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WF enables us to design LC policies that close super-spreader locations on campus. On simulating disease spread with contact networks from WF, we find that LC maintains the same reduction in cumulative infections as SQ 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. WF can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework

“…In addition, universities are wellpositioned to test and implement new surveillance methods that can subsequently be applied at greater scale. For example, they were among the first to implement wastewater surveillance for SARS-CoV-2 18,26 , institution-wide viral sequencing 21,27 , and contact tracing via wifi network colocation data 28,29 . In Fall 2020, Colorado Mesa University (CMU) committed to in-person instruction of ~8,000 students for the 2020-2021 academic year, motivated by a desire to avoid amplifying resource disparities via remote learning.…”

“…In addition, universities are well-positioned to test and implement new surveillance methods that can subsequently be applied at greater scale. For example, they were among the first to implement wastewater surveillance for SARS-CoV-2 18, 26 , institution-wide viral sequencing 21, 27 , and contact tracing via wifi network co-location data 28, 29 .…”