Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.

Journal: BMC public health
PMID: 39135162

Abstract

BACKGROUND: Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network-Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures.

Authors

  • Guofan Li
    School of Public Health, Hebei Medical University, No.361, Zhongshan East Road, Shijiazhuang, Hebei Province, 050017, China.
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
  • Guangyue Han
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Caixiao Jiang
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Minghao Geng
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Nana Guo
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Wentao Wu
    Department of Epidemiology and Health Statistics School of Public Health, Xi'an Jiaotong University, Xi'an, China.
  • Shangze Liu
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Zhihuai Xing
    Hebei Provincial Center for Disease Control and Prevention, No.97, Huai'an East Road, Shijiazhuang, Hebei Province, 050021, China.
  • Xu Han
  • Qi Li
    The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.

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