Determination of dengue high-risk areas in the Philippines: a kernel density estimation, inverse distance weighting, and ecological niche modeling

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Abstract
Background: Dengue is an acute infectious tropical disease that poses a significant public health burden in the Philippines; however, studies employing spatial distribution modeling and ecological approaches to analyze dengue occurrence data remain limited. This study aims to determine the high-risk areas suitable for dengue occurrence and its determinants in the Philippines. Methods: Dengue case data from 2017 to 2024 were analyzed using kernel density estimation (KDE) and inverse distance weighting (IDW) spatial interpolation to characterize spatial intensity and estimate incidence in unsampled areas. An ecological niche model was developed using maximum entropy modeling, implemented through the MaxEnt software, with climatic, environmental, and socioeconomic predictors. Model performance was evaluated using the area under the curve (AUC), and predictor importance was assessed using jackknife testing. Results: Results show highest intensity in 2019 and consistent high case density in the National Capital Region (NCR). Meanwhile, high predicted incidence rates were consistently exhibited in northern Luzon. The maximum entropy model had a strong performance in predicting the suitable areas for dengue with a mean area under curve (AUC) of 0.847. Nighttime lights (32.3%), land cover (31.1%), and population density (9.4%) significantly contributed to the model. The NCR was found to be a high-risk suitable area for dengue occurrence along with some parts of other provinces. Conclusions: This study represents the first application of ecological niche modeling to dengue in the Philippines. The integration of KDE, IDW, and maximum entropy model provides a robust framework for identifying high-risk areas and key determinants, emphasizing the role of urbanization in dengue distribution. These findings are valuable to authorities for an informed risk-based surveillance, genotype-specific monitoring, and decision-making for geospatially targeted disease risk management. Graphical
Funding Information
  • Chiang Mai University

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