Spatiotemporal Dynamics of Dengue Risk in Bangladesh: A GIS Based Approach

Dengue, commonly known as breakbone fever, has been prevalent in Bangladesh since 2000. Monsoon conditions and warmer temperatures create suitable breeding environment for the vectors Aedes aegypti and Aedes albopictus. Although dengue outbreaks peak during the post-monsoon months (September and October), 2023 showed an unusual shift, with cases peaking as early as June. The case fatality rate (0.54%) in 2023 was the highest in the past two decades. This study offers an exhaustive examination of the spatiotemporal dynamics of dengue incidence clusters and composite risk across Bangladesh using daily dengue case data from 2019 to 2024. Spatial autocorrelation analysis (Local Moran's I) revealed that the central and south coastal districts, especially Dhaka, Manikganj, and Barisal were the major dengue hotspots during the peak outbreak years (2019, 2023, and 2024). Jaccard Similarity Index (JSI) analysis further indicated strong seasonal and interannual reorganization of dengue hotspots, especially during the post-monsoon season, with increasing hotspot concentration in coastal districts after 2020. To assess hazard, vulnerability and risk, we applied a GIS-based multi-criteria decision-making model (MCDM) framework and compared both weighted (AHP-based) and unweighted approaches. The weighted AHP model was more accurate (AUC = 0.75) than the unweighted model (AUC = 0.55) in predicting dengue risk zones. A negative binomial mixed-effects regression showed that lagged temperature, precipitation and population density increased dengue incidence, while higher normalized difference built-up index was associated with lower incidence. These findings emphasize the need for integrated dengue control strategies targeting high-risk areas and equal healthcare access.