[Advances in agent-based modeling for infectious disease simulation and intervention]

Emerging and re-emerging infectious diseases continue to pose a persistent threat to global public health, underscoring the need for rigorous assessment of transmission risk and intervention effectiveness. Agent-based models (ABMs), which represent individuals as the fundamental modeling units and simulate their interactions and behaviors to generate population-level dynamics, have gained increasing prominence in recent years for simulation, surveillance, early warning, and evaluation of interventions for infectious diseases. This review outlines an overview of the core components and methodological framework of ABMs, synthesizes their applications in infectious-disease simulation, surveillance, early warning, management of high-risk settings, and estimation of healthcare resource needs, and discusses key challenges such as data availability, privacy concerns, model uncertainty, and computational demands. Overall, ABMs are evolving from purely computational tools into decision-support instruments, with the potential to provide quantitative evidence for more fine-grained and targeted infectious disease control strategies.