Indonesia is the country with the highest dengue burden in Southeast Asia, with 488 of 514 districts reporting dengue cases annually across its 17,000-island archipelago. Despite the substantial dengue burden, spatiotemporal transmission patterns remain poorly characterised across this geographically diverse nation. We analysed province-level dengue surveillance data (2016-2024) from Indonesia's Ministry of Health alongside climate and urbanisation variables. We examined spatiotemporal heterogeneity using phase lag analysis, bivariate mapping, and correlation analysis between dengue incidence, and climate and urbanisation variables, adjusting for surveillance capacity differences. A clear west-to-east progression of the dengue wave was found, with provinces in Sumatra and Kalimantan experiencing the earliest peaks, aligning with Australian-Asian monsoon patterns. Multi-annual outbreaks (2016, 2019, 2024) were correlated with El Nino phases. Precipitation and relative humidity showed predominantly positive correlations with dengue incidence, whereas temperature correlations were largely negative, reflecting complex relationships between climate variables and annual dengue transmission. After adjusting for surveillance, urban population density was positively correlated with dengue incidence (r = 0.490), while simple urbanisation showed a negative correlation (r = -0.310). Indonesia exhibits spatiotemporal heterogeneity in dengue transmission, with peak timing differences of up to 3-4 months between provinces, driven by monsoon seasonality. Province-specific intervention timing based on local transmission seasons, rather than uniform national scheduling, could improve control effectiveness. El Nino monitoring provides opportunities for multi-annual outbreak preparedness. Surveillance capacity adjustments indicate that urban density, rather than simple urbanisation, may drive transmission intensity. Climate-integrated surveillance and region-specific interventions are essential for effective dengue control across Indonesia's diverse epidemiological landscapes.
Djaafara et al. (Thu,) studied this question.