title: [Autoregressive integrated moving average model in predicting road traffic injury in China].
abstract: This research aimed to explore the application of autoregressive integrated moving average (ARIMA) model of time series analysis in predicting road traffic injury (RTI) in China and to provide scientific evidence for the prevention and control of RTI. Database was created based on the data collected from monitoring sites in China from 1951 to 2011. The ARIMA model was made. Then it was used to predict RTI in 2012. The ARIMA model of the RTI cases was Yt = e(Y˙t-1+0.456▿Yt-1+et) (et stands for random error). The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was no statistical significance. The model fitted the data well. True value of RTI cases in 2011 was within 95% CI of predicted values obtained from present model. The model was used to predict value of RTI cases in 2012, and the predictor (95%CI) was 207 838 (107 579-401 536). The ARIMA model could fit the trend of RTI in China.
authors.affiliation: Injury Prevention Research Institute/School of Public Health, Southeast University, Nanjing 210009, China.
authors.forename: Yuan-yuan
authors.initials: YY
authors.lastname: Pang
authors.forename: Xu-jun
authors.initials: XJ
authors.lastname: Zhang
authors.forename: Zhi-bin
authors.initials: ZB
authors.lastname: Tu
authors.forename: Meng-jing
authors.initials: MJ
authors.lastname: Cui
authors.forename: Yue
authors.initials: Y
authors.lastname: Gu
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D002681:China / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D008962:Models, Theoretical; D014947:Wounds and Injuries / Q000453:epidemiology / Q000517:prevention & control*