J. Contador

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

A spatio-temporal deep learning framework based on the U-Net++ architecture is proposed to generate high-resolution dengue risk maps in Colombia and highlights 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.