A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in Colombia

Abstract

Understanding dengue dynamics from a spatiotemporal perspective has become increasingly relevant in recent years, as it allows the identification of factors influencing disease transmission, including climatic conditions, human behavior, and the distribution of Aedes aegypti, the primary vector responsible for dengue virus transmission in tropical and subtropical regions. This knowledge is essential for supporting public health decision-making and reducing the impact of dengue on vulnerable populations. This study proposes a spatio-temporal deep learning framework based on the U-Net++ architecture to generate high-resolution dengue risk maps in Colombia. The model integrates climatic, environmental, demographic, and socioeconomic information derived from satellite and census sources. Two spatial approaches were evaluated: a high-dimensional geography (HDG) approach at the national scale and a low-dimensional geography (LDG) approach applied to five departments with high dengue incidence. The model integrating climatic, social, and environmental information achieved the best performance in both approaches. In the HDG configuration, the best model (M9) reached a test mIoU of 0.6646 (validation mIoU = 0.7361). In the LDG configuration, the corresponding model achieved an average test mIoU of approximately 0.73 across departments. These results highlight the potential of integrating heterogeneous climatic, environmental, and socioeconomic data within a spatiotemporal deep learning framework to characterize dengue risk patterns and support high-resolution surveillance.