Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.

Journal: BMC infectious diseases
PMID: 31088391

Abstract

BACKGROUND: Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model.

Authors

  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Shu-Yi An
    Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning, China.
  • Peng Guan
    Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China.
  • De-Sheng Huang
    Department of Mathematics, School of Fundamental Sciences, China Medical University, Shenyang, Liaoning, China.
  • Bao-Sen Zhou
    Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China. bszhou@cmu.edu.cn.

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