<![CDATA[Purpose: This study aimed to analyze the spatio-temporal patterns of dengue virus infection via kernel density estimation to assess the relative risk distribution and identify transmission hotspots in the Bobonaro municipality. Methods: A retrospective analysis was conducted on confirmed dengue cases (n=311) reported from seven community health centers and one referral hospital in Bobonaro from January 2022 to December 2024. Kernel density estimation with optimal bandwidth selection was employed to map the relative risk distributions and identify spatial clusters. Demographic patterns across age categories were analyzed using negative binomial regression with a quadratic age-rank term, and sex distribution was analyzed using an exact two-proportion binomial test. Results: Annual dengue cases increased by 41.48% over the study period, with significant seasonal patterns observed during the dry and rainy seasons. Case counts increased from the infancy category toward a peak in the youth category (5–14 years, 158 cases, 50.8% of all cases) before declining in older age groups, a pattern confirmed by negative binomial regression with a quadratic age-rank term (incidence rate ratio [IRR] 8.45, 95% CI 2.70–25.37, p<0.001 for the linear term and 0.74, 95% CI 0.65–0.85, p<0.001 for the quadratic term). Females accounted for a slightly higher proportion of cases (51.8%, n=161) than males (48.2%, n=150), a difference that was not statistically significant (p= 0.571). Spatial analysis revealed persistent hotspots in southeastern Bobonaro, with new clusters emerging in the northern regions by 2023 and transmission typically contained within an 80 m radius. Conclusion: This study identified clear demographic vulnerabilities and dynamic spatial patterns of dengue transmission in Bobonaro, demonstrating the utility of GIS-based spatial analysis for strengthening surveillance and guiding targeted vector control in resource-constrained settings.]]>
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