Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study.

Journal: JMIR medical informatics
Published Date: Aug 6, 2025

Abstract

BACKGROUND: Seasonal influenza is a major global public health concern, leading to escalated morbidity and mortality rates. Traditional early warning models rely on binary (0/1) classification methods, which issue alerts only when predefined thresholds are crossed. However, these models exhibit inflexibility, often leading to false alarms or missed warnings and failing to provide granular risk assessments essential for decision-making. Therefore, we propose a probability-based early warning system using machine learning to mitigate these limitations and to offer continuous risk estimations of alerts (0-1 variable) instead of rigid threshold-based alerts. Based on probabilistic prediction, public health experts can make more flexible decisions in combination with the actual situation, significantly reducing the uncertainty and pressure in the decision-making process and reducing the waste of public health resources and the risk of social panic.

Authors

  • Jinzhao Cui
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 31, Beijigesantiao street, Dongcheng District, Beijing, 102206, China, 86 18612690539.
  • Ting Zhang
    Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, Beijing 100020, China.
  • Yifeng Shen
    Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0882, Kanagawa, Japan.
  • Xiaoli Wang
    Demonstration Center of Future Product, Beijing Aircraft Technology Research Institute, COMAC, Beijing, China.
  • Liuyang Yang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Xuefeng Huang
    School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
  • Qiang Huang
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Yu Yang
    Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xian, Shaanxi, China.
  • Weizhong Yang
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Zhongjie Li
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 31, Beijigesantiao street, Dongcheng District, Beijing, 102206, China, 86 18612690539.

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