Ferra Yanuar, Yudiantri Asdi, Aidinil Zetra, Sofiana Wudlu, Fenni Kurnia Mutiya, R. Rahmawita
Dengue Haemorrhagic Fever (DHF) remains a public health burden in Indonesia with substantial provincial variation. We modelled province-level DHF counts in 2023 using Bayesian spatial conditional autoregressive Poisson models with population offsets. Predictors were average annual temperature (per 1°C) and the number of public health workers (province-level count). Spatial dependence was supported by Moran’s I=0.4689 (p=0.021). We fitted models using Besag-York-Mollié (BYM) and Leroux priors via Markov chain Monte Carlo and compared fit using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). In the BYM model, temperature was associated with lower risk (RR=0.90; 95% CrI: 0.76 to 1.07), with uncertainty including unity, whereas workforce density was associated with higher reported risk (RR=1.05; 95% CrI: 1.03 to 1.07). Estimates were similar under the Leroux prior (temperature RR=0.89; 95% CrI: 0.74 to 1.07; workforce RR=1.04; 95% CrI: 1.02 to 1.07), and BYM showed marginally better fit. Risk mapping indicated elevated burden in parts of Kalimantan and eastern Indonesia. Findings may inform geographically targeted surveillance and vector control; the workforce association should be interpreted cautiously because it may reflect reporting capacity or reactive deployment. © the Author(s), 2026.
Department of Mathematics and Data Science, Universitas Andalas, Indonesia; Department of Political Sciences, Universitas Andalas, Indonesia; Department of Statistics, Universitas Negeri Padang, Indonesia
