Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt.

Journal: Frontiers in public health
Published Date: May 20, 2024

Abstract

OBJECTIVE: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.

Authors

  • Xu-Dong Liu
    Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China.
  • Bo-Han Hou
    Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, China.
  • Zhong-Jun Xie
    Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, China.
  • Ning Feng
    Office of International Cooperation, Chinese Center for Disease Control and Prevention, Chaoyang District, Beijing, China.
  • Xiao-Ping Dong
    National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Chaoyang District, Beijing, China.

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