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Respiratory diseases represent one of the most significant economic burdenson healthcare systems worldwide. The variation in the increasing number ofcases depends greatly on climatic seasonal effects, socioeconomic factors, andpollution. Therefore, understanding these variations and obtaining preciseforecasts allows health authorities to make correct decisions regarding theallocation of limited economic and human resources. This study aims to modeland forecast weekly hospitalizations due to respiratory conditions in sevenregional hospitals in Costa Rica using four statistical learning techniques(Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemblemethod combining the above methods), along with 22 climate change indices andaerosol optical depth as an indicator of pollution. Models are trained usingdata from 2000 to 2018 and are evaluated using data from 2019 as testing data.Reliable predictions are obtained for each of the seven regional hospitals

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data