Development of Models for Predicting the Number of Patients with Heatstroke on the Next Day Considering Heat Acclimatization

Abstract

We developed 55 models for predicting the number of ambulance transport due to heatstroke (hereafter, referred to as the number of patients with heatstroke) on the next day in Tokyo, using different combinations of 11 explanatory variables sets and five methods (three statistical models and two machine learning) for ten years (2010 – 2019). The root mean square error (RMSE) for the number of heatstroke patients was minimal when the best model was developed by combining six explanatory variables (temperature, relative humidity, wind speed, solar radiation, number of days since June 1, and the number of patients with heatstroke on the previous day) and the generalized additive model. The best model remarkably improved prediction by 52.1 % compared to a widely used model, which primarily utilizes temperature as an explanatory variable and the generalized linear model as a method. Further analysis investigating the contribution of the explanatory variables and prediction method showed that RMSE was reduced by 49.7 % using the above six explanatory variables compared to using the only temperature and by 14.6 % using the generalized additive model compared to using the generalized linear model.