Unveiling Eco-Epidemiological Risk Assessment through Bayesian Spatio-Temporal SPDE-INLA Approach - Mukhsar, Mukhsar; Usman, Ida; Sani, Asrul; Gubu, La; Muzuni, Muzuni; Majid, Ruslan; Sudayasa, I Putu; Fahmiati, Fahmiati (Jurnal Matematika, Statistika dan Komputasi, 2026)
Dengue hemorrhagic fever (DHF) remains a major public health burden in tropical areas. The DHF driven by nonlinear interactions, vector dynamics, and population density. Characterizing spatio-temporal risk heterogeneity is critical to targeted intervention. We analyzed dengue risk in Kendari using a Bayesian Stochastic Partial Differential Equation (SPDE) via Integrated Nested Laplace Approximation (INLA). DHF monthly data from 2022–2024 were integrated with geospatial information to estimate relative risk and spatial risk contours. Model performance was compared with Generalized Linear Models (GLM), Intrinsic Conditional Autoregressive (ICAR), and second order Random Walk (RW2). Kendari Barat and Kendari districts emerged as primary hotspots, while Kadia, Mandonga, Baruga, Kambu, and Wua-Wua were high-risk districts. Abeli, Poasia, and Puuwatu districts exhibited moderate risk. Lalodati, Soropia, and Sampara districts showed lower risk. Risk contours revealed clusters concentrated in the urban core and along major corridors, highlighting the influence of settlement density and spatial connectivity. The SPDE–INLA model outperformed GLM, ICAR, and RW2 in capturing spatial structure and improving predictive accuracy. High resolution risk estimates supported specific district including intensified source reduction before and during peak rainfall, selective fogging, larval control, community education, microclimate monitoring, and early case screening in high-risk areas.