[SS10] Epidemic Modeling: Advances in Disease Control and Future Challenges

[SS10-02] Human Mobility and Effectiveness of Non-Pharmaceutical Interventions: A High-Fidelity Agent-Based Model of COVID-19

*Young Kim¹, Sunmi Lee¹ (1. Kyung Hee University (Korea))

Keywords:

Non-pharmaceutical Interventions,Lockdown,Human Mobility,COVID-19,Agent-based Modeling

Non-pharmaceutical interventions (NPIs), including social distancing (SD) and lockdowns, have proven essential in mitigating the spread of infectious diseases during pandemics. However, traditional modeling approaches often neglect or oversimplify mobility, leading to inaccurate assessments of public health strategies. To address this gap, we present a high-fidelity agent-based model (ABM) that integrates realistic, individual-level mobility data derived from large-scale communication records. This mobility-aware model captures detailed daily routines, including commuting, social interactions, and return-home behavior, and simulates their interaction with disease progression at fine spatial and temporal resolutions. We applied the model to the second wave of COVID-19 in Seoul, South Korea, and validated its output against empirical infection and quarantine data, achieving close alignment in both case trends and healthcare burden indicators. We then explored multiple intervention scenarios, focusing in particular on lockdown strategies modeled after Australia's rigorous approach. Our results highlight the decisive role of mobility in shaping the outcomes of NPIs. Lockdowns implemented in conjunction with SD significantly reduce infection peaks and accelerate containment. In contrast, lockdowns without concurrent SD yield only limited and short-term effects, failing to prevent resurgence. Notably, regional lockdowns without broader contact reduction may inadvertently sustain transmission within restricted areas due to residual mobility. These findings underscore the critical importance of explicitly modeling human mobility when evaluating public health interventions. By integrating high-resolution mobility data into an agent-based epidemiological framework, our approach offers a more realistic and policy-relevant assessment of NPIs. The study provides valuable insights for designing balanced, evidence-based pandemic strategies that minimize both epidemiological risks and socioeconomic costs, particularly in densely populated urban environments where mobility-driven contact patterns are complex and highly variable.