title: A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China.
abstract: OBJECTIVE
  Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.
  
  
  METHODS
  A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.
  
  
  RESULTS
  The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869 × 10(-4), 0.0029, 0.0419 with a corresponding testing error of 0.9375 × 10(-4), 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.
  
  
  CONCLUSIONS
  The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Lingling
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Lijing
authors.initials: L
authors.lastname: Yu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Zhouqin
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Lihong
authors.initials: L
authors.lastname: Tian
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Li
authors.initials: L
authors.lastname: Tan
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Shaofa
authors.initials: S
authors.lastname: Nie
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
mesh_terms: D002681:China / Q000453:epidemiology; D062665:Epidemiological Monitoring; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D012552:Schistosomiasis / Q000453:epidemiology*; D014504:Urban Health / Q000706:statistics & numerical data*