Realistic mobility integration in agent-based model for targeted public health strategies

The effectiveness of non-pharmaceutical interventions (NPIs) like social distancing (SD) and lockdowns significantly varies based on regional demographics and mobility patterns. Traditional epidemiological models often do not fully capture mobility behaviors, leading to inaccurate assessments of disease spread and intervention impacts. To address these limitations, we present a high-fidelity agent-based model (ABM) integrating detailed individual-level mobility data derived from real-world communication records. This model employs population synthesis (PS) to generate realistic demographic agents and simulates their daily mobility, including commuting, social interactions, and home-return activities. Our framework explicitly captures epidemiological states such as pre-symptomatic, asymptomatic, and quarantined conditions, allowing accurate simulation of spatial–temporal transmission dynamics. We applied this model to the second COVID-19 wave in Seoul, validated it against empirical data and performed scenario-based analyses to evaluate various NPIs. The results indicated that combined lockdown and SD measures effectively suppressed infection peaks, but isolated lockdowns without SD provided only temporary and limited containment. Our study underscores the critical importance of detailed mobility data in pandemic modeling, demonstrating the model’s potential to serve as a foundational tool that can be extended to support policymakers in designing balanced interventions that navigate trade-offs between public health outcomes and socioeconomic impacts.