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 hospital