title: Application of a novel grey self-memory coupling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea.
abstract: OBJECTIVE
  In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China.
  
  
  METHODS
  The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates.
  
  
  RESULTS
  Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China.
  
  
  CONCLUSIONS
  The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.
authors.affiliation: School of Science, Nantong University, Nantong, Jiangsu Province, China; College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province, China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Guo
authors.affiliation: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province, China.
authors.forename: Sifeng
authors.initials: S
authors.lastname: Liu
authors.affiliation: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province, China.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Wu
authors.affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America.
authors.forename: Lingling
authors.initials: L
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D002681:China; D004403:Dysentery / Q000453:epidemiology*; D006069:Gonorrhea / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical