• Emergence and consolidation of spatial statistical modeling for disease mapping and cluster detection, integrating space–time scan statistics, likelihood-based clustering, and hierarchical models (Bayesian/GLMM) to handle overdispersion and spatial dependence [1], [13], [3], [12], [19], [8], [17].

• Surveillance-oriented spatial analysis emphasizing early detection of localized disease clusters through space–time methods and GIS-enabled data integration across multiple diseases and mortality studies [1], [5], [10], [6], [7].

• Joint- and multi-disease spatial analysis via shared components and Bayesian geo-additive approaches to dissect common versus disease-specific spatial variation [2], [16], [8].

• Environmental exposure mapping and GIS-based analysis of health outcomes (air pollution, malaria, kala-azar, breast cancer geography) highlighting observed spatial patterns and ecological confounding considerations [9], [19], [20], [6], [10].