Weather-Enhanced Machine Learning for Time-Resolved Risk Stratification of Clinically Managed Hymenoptera-Related Sting Events in an Urban German Region.

backgroundInsect stings, particularly those caused by Hymenoptera such as wasps and bees, are frequent triggers of severe allergic reactions and anaphylaxis, yet the ability to predict short-term risk periods based on environmental conditions has not been systematically evaluated. Meteorological factors influence both insect activity and human exposure, highlighting a relevant gap in preventive risk assessment.

methodsThis exploratory single-center study was conducted in Bochum, Germany, an urban region within the Rhine-Ruhr metropolitan area. A 17-year retrospective dataset (2005-2022) of clinically treated Hymenoptera-related sting events was analyzed to explore time-resolved, weather-informed patterns using artificial intelligence (AI)-based machine learning. The study emphasizes methodological feasibility and pattern identification rather than clinical prediction. Daily weather parameters were transformed into expert-informed indicators capturing current-season and carry-over environmental conditions. A multilayer perceptron (MLP) was trained to identify periods of increased sting occurrence, and model performance was evaluated primarily using recall to capture rare-event signals.

resultsA total of 346 clinically significant sting events were recorded. Weather variables showed strong spatial coherence across four stations and were associated with intra-seasonal clustering of sting events rather than absolute annual incidence. Exploratory analyses suggested that earlier seasonal onset correlated with higher sting counts (Pearson R = -0.52;

conclusionsThis study combines a long-term clinical insect sting dataset with high-resolution meteorological data to explore time-resolved, weather-informed risk patterns using machine learning. The findings demonstrate the technical feasibility of exposure-based risk stratification in a rare-event setting. However, benchmarking showed that the MLP did not consistently outperform simpler baseline approaches for binary warning of elevated-risk periods. This proof-of-concept should therefore be interpreted as exploratory and not as a stand-alone warning system, supporting further external validation in larger, multi-center cohorts before clinical, public health, or digital health implementation can be considered.