Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.

Journal: International journal of environmental research and public health
Published Date: Mar 23, 2016

Abstract

BACKGROUND: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model.

Authors

  • Lingling Zhou
    Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. zllgwy@126.com.
  • Jing Xia
    Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China. xiaj0608@163.com.
  • Lijing Yu
    Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. yulijing321@outlook.com.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Yun Shi
    Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
  • Shunxiang Cai
    Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China. shunxiangcai@163.com.
  • Shaofa Nie
    Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. sf_nie@mails.tjmu.edu.cn.

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