Development of a high-resolution Machine Learning model for predicting heat-related morbidity based on insurance data

Digital Poster/Demo
In session Postersession No. 2 , Sept. 3, 2025, 18:15 – 19:00
Exact timing: 18:15 – 19:00

Bouwer, Laurens Menno¹ , Torres-Matallana, J. A.¹ , Fenn, E.¹ , Hoffmann, P.¹ , Nikolaou, N.² , Augustin, J.³

Morbidity from heat extremes is much higher than mortality and has higher costs but has received less attention. In addition, there are a few, if any, models available to assess the current and potential future impacts of heat extremes on morbidity under climate change. In this study we develop a machine learning model based on a large insurance dataset for the springtime (Q2: April, May, June) and summertime (Q3: July, August, September) for the period 2013-2023 in Germany. From this dataset, we construct a spatially distributed 1km ² dataset on incidence of heat strokes and volume depletion for the federal state of North-Rhine Westphalia. We link this to detailed estimates of past heat extremes (maximum air temperature, average air temperature, number of hot days) as well as air pollution (NO ₂ , O ₃ , PM ₁₀ , and PM _2.5 ), and socioeconomic factors (education level, household income, and unemployment rate) to explain temporal and spatial differences in incidence. We present results for the XGBoost algorithm, as well as initial results for deep-learning algorithms.