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 asWiFi mobility models(WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC).WiMobcan construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally,WiMobenables us to designLCpolicies that close super-spreader locations on campus. By simulating disease spread with contact networks fromWiMob, we find thatLCmaintains the same reduction in cumulative infections asRIwhile showing greater reduction in peak infections and internal transmission. Moreover,LCreduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation.WiMobcan empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.