Disease Spread with Mobility Heterogeneity in Campus Reopening Simulations

Abstract

The safe reopening of educational institutions during widespread infectious disease outbreaks presents a complex challenge for public health officials and university administrators. Traditional epidemiological models often assume homogenous mixing within populations, thereby failing to capture the intricate, heterogeneous mobility patterns characteristic of a university campus. This paper investigates the explicit link between mobility heterogeneity and disease transmission dynamics using an advanced agent-based modeling framework tailored to university environments. By simulating the daily activities of thousands of autonomous agents representing students, faculty, and staff, we evaluate how varying schedules, spatial constraints, and social interactions contribute to the emergence of localized outbreaks and superspreading events. Our simulations compare a baseline scenario of unmitigated campus operations against several intervention strategies, including hybrid instructional models, targeted mobility restrictions, and staggered scheduling. The analysis reveals that transmission is disproportionately driven by a small fraction of highly connected spatial hubs, such as large lecture halls and residential dining facilities, underscoring the non-linear relationship between mobility reduction and disease containment. The findings suggest that uniform mobility restrictions are less effective and more disruptive than targeted interventions aimed at specific high-contact nodes. This research provides actionable evidence for policymakers to design nuanced reopening protocols that balance educational continuity with public health safety.