<![CDATA[Dengue hemorrhagic fever (DHF) exhibits complex spatial and temporal dynamics that require analytical approaches capable of jointly accounting for geographic heterogeneity, temporal dependence, and environmental conditions. This study developed a hybrid spatio-temporal framework integrating a Bayesian Spatio-Temporal Conditional Autoregressive (ST-CAR) model with a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to characterize and predict DHF incidence in Makassar City, Indonesia. Monthly DHF cases from 149 urban villages were analyzed over 32 months, from January 2022 to August 2024, together with remotely sensed environmental covariates represented by the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). A Bayesian ST-CAR model with a Poisson likelihood was first fitted to account for spatial heterogeneity, temporal dependence, and environmental effects. The model revealed substantial residual spatial heterogeneity and temporal dependence, whereas spatial dependence was relatively weak. NDVI and NDWI showed negative posterior mean associations with DHF incidence; however, their 95% credible intervals included zero. Residual temporal analysis was subsequently performed to determine whether additional temporal structure remained after ST-CAR estimation. Among the candidate seasonal models, SARIMA (0,0,0) (1,0,0)6 produced the lowest AIC, AICc and BIC, indicating residual temporal dependence at a six-month seasonal lag. Incorporating this residual component into the ST-CAR predictions reduced the RMSE from 1.533081 to 1.533053, corresponding to an improvement of only approximately 0.0018%. Thus, the hybrid model provided negligible improvement in overall point-prediction accuracy, suggesting that the ST-CAR component had already captured most of the systematic spatio-temporal variation in DHF incidence. Nevertheless, the SARIMA component provided additional characterization of residual six-month temporal dependence. Overall, the proposed framework demonstrates the utility of combining Bayesian spatio-temporal modeling, remote-sensing covariates, and residual seasonal time-series analysis to distinguish dominant spatio-temporal variation from secondary residual temporal structure in dengue incidence.]]>
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