ABSTRACT
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 (WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW). This approach enables policymakers to explore more granular policies like localized closures (LC). WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW, 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. WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.
