<![CDATA[This study maps the spatio-temporal risk dynamics of Dengue Hemorrhagic Fever (DHF) across 16 districts in Semarang City (2016–2025). Traditional epidemiological approaches using raw incidence rates often ignore spatial autocorrelation and struggle with overdispersion anomalies. To address this, we implemented a Hierarchical Bayesian framework using Integrated Nested Laplace Approximations (INLA) with a Negative Binomial distribution and a Besag-York-Mollié (BYM2) spatial architecture. We specified the spatial topology through a manually validated binary adjacency matrix to minimize subjectivity in defining regional boundaries. Our structured model improved computational performance significantly, reducing the Deviance Information Criterion (DIC) by 34.31% and the Root Mean Square Error (RMSE) by 15.30% compared to a baseline Poisson regression model. Using Geopandas and NetworkX for visualization, we identified Tembalang and Banyumanik districts as absolute Epicenter Nodes with an Exceedance Probability of 1.000. Spatial spillover network analysis demonstrated the propagation of epidemiological pressure from these epicenters to surrounding buffer zones, synchronized with the seasonal peak in the first quarter. This framework provides a precise computational foundation for vector control strategies, shifting from localized reactive approaches to preventive cluster mitigation.]]>
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