title: [Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha].
abstract: OBJECTIVE
  To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence.
  
  
  METHODS
  EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model.
  
  
  RESULTS
  After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83.
  
  
  CONCLUSIONS
  The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Central South University, Changsha 410078, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Tan
authors.forename: Lizhang
authors.initials: L
authors.lastname: Chen
authors.forename: Fuqiang
authors.initials: F
authors.lastname: Liu
mesh_terms: D002681:China / Q000453:epidemiology; D005544:Forecasting; D006232:Hand, Foot and Mouth Disease / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D012621:Seasons