A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.

Journal: BMC public health
PMID: 39893390

Abstract

BACKGROUND: Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting influenza outbreaks, aiming to achieve real-time and accurate influenza prediction.

Authors

  • Jiajia Luo
    Biomedical Engineering Department, Peking University, Beijing, 100191, China. jiajia.luo@pku.edu.cn.
  • Xuan Wang
    Baylor Scott & White Health, Dallas, TX, USA.
  • Xiaomao Fan
  • Yuxin He
    Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China.
  • Xiangjun Du
    School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R. China.
  • Yao-Qing Chen
    School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R. China.
  • Yang Zhao
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.

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