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. 2025 Apr 14;53(1):54.
doi: 10.1186/s41182-025-00734-4.

Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia

Marko Ferdian Salim  1   2 , Tri Baskoro Tunggul Satoto  3 , Danardono  4
Affiliations

Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia

Marko Ferdian Salim et al. Trop Med Health. .

Abstract

Background: Dengue remains a major public health concern in tropical regions, including Yogyakarta, Indonesia. Understanding its spatiotemporal patterns and determinants is crucial for effective prevention strategies. This study explores the spatiotemporal determinants of dengue incidence and evaluates the spatial variability of predictors using a geographically weighted panel regression (GWPR) approach.

Methods: This ecological study applied a spatiotemporal approach, analyzing dengue incidence across 78 sub-districts in Yogyakarta from 2017 to 2022. The dataset included meteorological variables (rainfall, temperature, humidity, wind speed, and atmospheric pressure), sociodemographic data (population density), and land-use characteristics (built-up areas, crops, trees, water bodies, and flooded vegetation). A GWPR model with a Fixed Exponential kernel was used to assess local variations in predictor influence.

Results: The Fixed Exponential Kernel GWPR model showed strong explanatory power (Adjusted R2 = 0.516, RSS = 43,097.96, AIC = 28,447.38). Local R-Square values ranged from 0.25 (low-performing sub-districts) to 0.75 (high-performing sub-districts), indicating significant spatial heterogeneity. Sub-districts such as Pakem, Cangkringan, and Girimulyo exhibited high local R2 values (>0.75), indicating robust model performance, whereas Kalibawang showed lower values (<0.25), suggesting weaker predictive power. High-dengue-burden sub-districts, including Kasihan (0.743), Banguntapan (0.731), Sewon (0.716), Wonosari (0.623), and Wates (0.540), demonstrated stronger associations between dengue incidence and key predictors. In Wonosari, the most influential predictors were Rainfall Lag 1, Rainfall Lag 3, temperature, humidity, wind speed, atmospheric pressure, and land-use variables, while in Wates, significant predictors included Rainfall Lag 1, Rainfall Lag 3, atmospheric pressure, and land-use factors. Lower model performance in Sedayu and Kalibawang suggests the necessity of incorporating additional predictors such as sanitation conditions and vector control activities.

Conclusions: The GWPR model provides valuable insights into the spatiotemporal dynamics of dengue incidence, emphasizing the role of localized predictors. Spatially adaptive prevention strategies focusing on high-risk areas are essential for effective dengue control in Yogyakarta and similar tropical regions.

Keywords: Dengue; Fixed-effects model; Geographically weighted panel regression; Local determinants; Spatiotemporal analysis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (Ref. No.: KE/FK/0584/EC/2023). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Spatial distribution of total dengue incidence in 2017–2022
Fig. 2
Fig. 2
Distribution of local R-square across sub-districts based on GWPR analysis
See this image and copyright information in PMC

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