Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China

• Published in: BMC infectious diseases 2023-05, Vol.23 (1), p.299-299, Article 299
• Main Authors: Zhu, Hansong, Chen, Si, Liang, Rui, Feng, Yulin, Joldosh, Aynur, Xie, Zhonghang, Chen, Guangmin, Li, Lingfang, Chen, Kaizhi, Fang, Yuanyuan, Ou, Jianming
• Format: Article
• Language: English
• Subjects: Air temperature; Algorithms; Analysis; Artificial Intelligence; Atmospheric models; China - epidemiology; Coxsackievirus infections; Deep learning; Diagnosis; DLNM; Epidemics; Generalized linear models; Geospatial data; Hand, Foot and Mouth Disease - epidemiology; Hand-foot-and-mouth disease; HFMD; Humans; Humidity; Incidence; Infectious diseases; Long short-term memory; LSTM; Meteorological; Meteorological Concepts; Model accuracy; Mouth Diseases; Neural networks; Neurons; Precipitation; Relative humidity; Risk factors; Root-mean-square errors; Rural areas; Temperature; Time series
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-023-08184-1

This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low ( 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.