Why the study?
Hand, foot, and mouth disease incidence has a pronounced seasonal tendency and is closely related to meteorological factors, motivating a combined model to improve prediction accuracy.
A combined SARIMA-XGBoost model incorporating meteorological factors significantly improves the prediction accuracy of hand, foot, and mouth disease incidence compared to traditional and standalone machine learning models.
May improve HFMD forecasting in Xinjiang; leaves open prospective validation before surveillance use.
Hand, foot, and mouth disease (HFMD) is a common childhood illness. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. A SARIMA model is used for seasonal trends, and an XGBoost algorithm is applied for the nonlinear effects of meteorological factors. In this paper, we propose a SARIMA-XGBoost combined model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The geographical and temporal weight model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The results show that the factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the SARIMA-XGBoost model is superior to other models in accuracy, especially in some areas with high incidence.
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Man et al. (2023) studied this question.
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