Alessandro Sebastianelli, Dario Spiller, Raquel Carmo, James Wheeler, Artur Nowakowski, Ludmilla Viana Jacobson, Dohyung Kim, Hanoch Barlevi, Zoraya El Raiss Cordero, Felipe J Colón-González, Rachel Lowe, Silvia Liberata Ullo, Rochelle Schneider
Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level...
February 15, 2024: Scientific Reports