Spatiotemporal dynamics of infectious diseases: From mechanistic models to data-driven models

The recurrent outbreaks of global infectious diseases have highlighted the urgent need for a unified spatiotemporal modeling framework that can capture multiscale processes, mechanistic interactions, and data heterogeneity. Previous reviews have mainly addressed either mechanistic or data-based approaches, leaving a gap in understanding how these two perspectives can be systematically integrated. To address this gap, this paper traces the evolution of epidemic modeling from mechanism-driven frameworks grounded in epidemiological theory to data-driven paradigms enabled by machine learning. It first follows the progression from classical homogeneous mixing models to those incorporating heterogeneity in age, space, contact structure, and pathogen variation. It then outlines the rise of data-based approaches, beginning with statistical models and extending to modern machine learning methods. By outlining this methodological continuum, the review provides an integrated perspective on the co-evolution of mechanistic and data-driven approaches, offering a unified theoretical and computational foundation for the spatiotemporal analysis of epidemic dynamics and for intelligent, data-informed public health interventions.