Forecasting Neurological Emergency Cases in Relation to Synoptic Weather Patterns : A Suburban ER Study

Abstract

Morbidity related to meteorological parameters is a significant public health concern in the context of global warming. While numerous studies have examined the effects of specific meteorological parameters on medical data, fewer have employed a synoptic climatological approach that considers the patterns of weather systems over a specific geographic area. This study utilizes Péczely Weather Patterns, a synoptic meteorological system that characterizes daily weather patterns across the entire Carpathian Basin. By utilizing Péczely synoptic classification, this study explores the correlation between weather patterns and emergency room (ER) visits due to neurological emergencies. The analysis is grounded in medical data sourced from a prominent county hospital in Budapest. The data cover the period from 2015 to 2019, during which 34,560 patients were admitted to the ER with neurological problems. In this study, we mathematically modeled the relationships between synoptic weather types and neurological emergencies. We then analyzed the resulting probabilistic model to explore potential biometeorological correlations. On the basis of this model, we built a predictive simulation model to estimate the expected patient load. This approach has the potential to support the healthcare system from a human resource allocation perspective.