Abstract
<jats:p>The spatiotemporal susceptible–infected–recovered (SIR) model is a foundational framework in spatial epidemiology and health geography, extending classical disease modeling by integrating geographic heterogeneity and human mobility into transmission dynamics. This entry provides a comprehensive overview of the model's conceptual basis, tracing its evolution from deterministic compartmental theory to spatially explicit approaches such as reaction–diffusion systems, metapopulation networks, and agent‐based simulations. Emphasis is placed on recent developments, particularly during the COVID‐19 pandemic, including Bayesian hierarchical extensions, hybrid AI‐enhanced frameworks, and the incorporation of high‐resolution mobility, demographic, and environmental data. The discussion critically evaluates the model's strengths—such as its capacity to inform geographically targeted interventions and support interdisciplinary public health insights—as well as limitations related to data demands, behavioral feedback, and predictive uncertainty. By combining geospatial thinking with epidemiological rigor, the spatiotemporal SIR model remains an essential tool for understanding, predicting, and managing infectious disease spread across both time and space.</jats:p>