Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

Journal: Journal of medical Internet research
PMID: 37847532

Abstract

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve.

Authors

  • Liuyang Yang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Ting Zhang
    Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, Beijing 100020, China.
  • Xuan Han
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Jiao Yang
    School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian 116034, China; Key Laboratory of Particle & Radiation Imaging of Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing, China.
  • Yanxia Sun
    School of Rail Transportation, Nanjing Vocational Institute of Transport Technology, Nanjing, China.
  • Libing Ma
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Jialong Chen
    Department of Respiratory and Critical Care Medicine, Bejing Hospital, Beijing, China.
  • Yanming Li
    Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
  • Shengjie Lai
    WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom; Flowminder Foundation, Stockholm, SE-113 55, Sweden; School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Luzhao Feng
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Weizhong Yang
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Keywords

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