Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

Journal: PloS one
PMID: 36913401

Abstract

BACKGROUND: Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction.

Authors

  • Yi Feng
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Xiya Cui
    School of Data and Computer Science, Shandong Women's Unversity, Jinan, Shandong, China.
  • Jingjing Lv
    Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China.
  • Bingyu Yan
    Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China.
  • Xin Meng
    Department of Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Yanhui Guo
    Department of Computer Science, University of Illinois Springfield, Springfield, IL, United States.

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