title: Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.
abstract: BACKGROUND
  Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.
  
  
  METHODS
  In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.
  
  
  RESULTS
  The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.
  
  
  CONCLUSIONS
  The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.
authors.affiliation: 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: 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: 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: School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Jiang
authors.affiliation: 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: School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Wei
authors.affiliation: School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Shaofa
authors.initials: S
authors.lastname: Nie
mesh_terms: D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D003710:Demography; D058872:Epidemics; D005260:Female; D005544:Forecasting; D006232:Hand, Foot and Mouth Disease / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D015203:Reproducibility of Results; D012621:Seasons; D013997:Time Factors