The Impact of Climatic Factors on Respiratory Pharmaceutical Demand: A Comparison of Forecasting Models for Greece

Climate change is increasingly recognized as a driver of health-related outcomes, yet its impact on pharmaceutical demandremains largely understudied. As environmental conditions evolve and extreme weather events intensify, anticipating their influ-ence on medical needs is essential for designing resilient healthcare systems. This study examines the relationship between climatevariability and the weekly demand for respiratory prescription pharmaceuticals in Greece, based on a dataset spanning seven and ahalf years (390 weeks). Granger-causality spectra are employed to explore potential causal relationships. Following variable selec-tion, four forecasting models are implemented: Prophet, a Vector Autoregressive model with exogenous variables (VARX), RandomForest with Moving Block Bootstrap (MBB-RF), and Long Short-Term Memory (LSTM) networks. The MBB-RF model achievesthe best performance in relative error metrics while providing robust insights through variable importance rankings. The LSTMmodel outperforms most metrics, highlighting its ability to capture nonlinear dependencies. The VARX model, which includesProphet-based exogenous inputs, balances interpretability and accuracy, although it is slightly less competitive in overall predic-tive performance. These findings underscore the added value of climate-sensitive variables in modeling pharmaceutical demandand provide a data-driven foundation for adaptive strategies in healthcare planning under changing environmental conditions