Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. This study aims to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models are trained using data 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

翻译：呼吸系统疾病是全球医疗系统中最显著的经济负担之一。病例数量的变化在很大程度上取决于气候季节性效应、社会经济因素和污染。因此，理解这些变化并获得精确的预测，有助于卫生当局在有限经济和人力资源的分配上做出正确决策。本研究旨在利用四种统计学习技术（随机森林、XGBoost、Facebook的Prophet预测模型以及结合上述方法的集成方法），结合22个气候变化指数和气溶胶光学厚度作为污染指标，对哥斯达黎加七家地区医院因呼吸系统疾病导致的每周住院人数进行建模和预测。模型使用2000年至2018年的数据进行训练，并使用2019年的数据进行评估。针对七家地区医院中的每一家，均获得了可靠的预测结果。