title: Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China.
abstract: Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.
authors.affiliation: College of Public Health, Xinjiang Medical University, Urumqi, 830011, People's Republic of China; College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
authors.forename: Yan-Ling
authors.initials: YL
authors.lastname: Zheng
authors.affiliation: College of Public Health, Xinjiang Medical University, Urumqi, 830011, People's Republic of China; College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
authors.forename: Li-Ping
authors.initials: LP
authors.lastname: Zhang
authors.affiliation: College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
authors.forename: Xue-Liang
authors.initials: XL
authors.lastname: Zhang
authors.affiliation: College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: College of Public Health, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
authors.forename: Yu-Jian
authors.initials: YJ
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D002681:China / Q000453:epidemiology; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D009017:Morbidity; D062486:Public Health Surveillance; D014376:Tuberculosis / Q000453:epidemiology*