The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

Journal: PloS one
Published Date: Jan 1, 2022

Abstract

BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China.

Authors

  • Daren Zhao
    Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China.
  • Huiwu Zhang
    Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China.
  • Qing Cao
    Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China. Electronic address: cq30553@rjh.com.cn.
  • Zhiyi Wang
    Department of Infectious Diseases, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Sizhang He
    Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P.R. China.
  • Minghua Zhou
    Department of Medical Administration, Luzhou People's Hospital, Luzhou, Sichuan, P.R. China.
  • Ruihua Zhang
    School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, P.R. China.

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