Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia
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 an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | Tropical Medicine and Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41182-025-00734-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850181343810420736 |
|---|---|
| author | Marko Ferdian Salim Tri Baskoro Tunggul Satoto Danardono |
| author_facet | Marko Ferdian Salim Tri Baskoro Tunggul Satoto Danardono |
| author_sort | Marko Ferdian Salim |
| collection | DOAJ |
| description | 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 R 2 = 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 R 2 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. |
| format | Article |
| id | doaj-art-a91f449c82e64559b149acdec8b0eacc |
| institution | OA Journals |
| issn | 1349-4147 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Tropical Medicine and Health |
| spelling | doaj-art-a91f449c82e64559b149acdec8b0eacc2025-08-20T02:17:56ZengBMCTropical Medicine and Health1349-41472025-04-0153111210.1186/s41182-025-00734-4Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, IndonesiaMarko Ferdian Salim0Tri Baskoro Tunggul Satoto1Danardono2Doctorate Program of Medical and Health Science, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah MadaDepartment of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah MadaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah MadaAbstract 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 R 2 = 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 R 2 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.https://doi.org/10.1186/s41182-025-00734-4DengueLocal determinantsSpatiotemporal analysisGeographically weighted panel regressionFixed-effects model |
| spellingShingle | Marko Ferdian Salim Tri Baskoro Tunggul Satoto Danardono Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia Tropical Medicine and Health Dengue Local determinants Spatiotemporal analysis Geographically weighted panel regression Fixed-effects model |
| title | Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia |
| title_full | Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia |
| title_fullStr | Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia |
| title_full_unstemmed | Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia |
| title_short | Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia |
| title_sort | understanding local determinants of dengue a geographically weighted panel regression approach in yogyakarta indonesia |
| topic | Dengue Local determinants Spatiotemporal analysis Geographically weighted panel regression Fixed-effects model |
| url | https://doi.org/10.1186/s41182-025-00734-4 |
| work_keys_str_mv | AT markoferdiansalim understandinglocaldeterminantsofdengueageographicallyweightedpanelregressionapproachinyogyakartaindonesia AT tribaskorotunggulsatoto understandinglocaldeterminantsofdengueageographicallyweightedpanelregressionapproachinyogyakartaindonesia AT danardono understandinglocaldeterminantsofdengueageographicallyweightedpanelregressionapproachinyogyakartaindonesia |