A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning.

Journal: BMC medical research methodology
Published Date: Dec 31, 2024

Abstract

BACKGROUND: The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models from a broad region directly to a small area. This paper builds a prediction approach for small size COVID-19 time series in a city.

Authors

  • Bin Hu
    Department of Thoracic Surgery Beijing Chao-Yang Hospital Affiliated Capital Medical University Beijing China.
  • Yaohui Han
    School of Public Health, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, Jiangsu, 221004, China.
  • Wenhui Zhang
    Department of Hepatobiliary and Pancreatic Surgery, The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, 261 HuanSha Road, Hangzhou, 310006, China.
  • Qingyang Zhang
    University of Nottingham, University Blv, Nottingham, NG7 2RD, England.
  • Wen Gu
    Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China.
  • Jun Bi
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Bi Chen
    School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, PR China 310018.
  • Lishun Xiao
    School of Public Health, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, Jiangsu, 221004, China. xiaolishun@xzhmu.edu.cn.

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