<![CDATA[Dengue Hemorrhagic Fever (DHF) remains one of the most prevalent vector-borne diseases in Indonesia, with Bandung City consistently reporting high annual incidence. Count regression models have been widely applied in disease epidemiology; however, many studies default to Poisson regression without testing for overdispersion, which violates a fundamental modeling assumption when variance exceeds the mean. This study proposes a Negative Binomial Regression (NBR) framework that jointly incorporates climatic variables (monthly rainfall, mean temperature, relative humidity) and sociodemographic covariates (population density, drainage quality index, vegetation cover) to model weekly DHF case counts across 30 sub-districts of Bandung City from 2019 to 2023. Overdispersion was formally assessed using the Cameron-Trivedi test. Incidence Rate Ratios (IRRs) and 95% confidence intervals were estimated for all predictors. Model selection was performed via AIC, BIC, and likelihood ratio tests against a Poisson baseline. Results demonstrate significant overdispersion (dispersion parameter ), confirming the appropriateness of NBR over Poisson regression. Monthly rainfall (IRR = 1.008, p < 0.001), lagged one-week cases ), and population density emerged as significant positive predictors, while drainage quality index was protective. The NBR model achieved substantially lower AIC (2810 vs 3240) and BIC (2820 vs 3245) compared to Poisson. These findings provide quantitative evidence for spatiotemporal DHF surveillance and can guide targeted vector-control resource allocation in urban West Java]]>
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