title: Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.
abstract: Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Qianglin
authors: Q
authors: Zeng
authors: Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, China.
authors: Dandan
authors: D
authors: Li
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Gui
authors: G
authors: Huang
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Jin
authors: J
authors: Xia
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Xiaoming
authors: X
authors: Wang
authors: Central Laboratory, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Yamei
authors: Y
authors: Zhang
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Wanping
authors: W
authors: Tang
authors: Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.
authors: Hui
authors: H
authors: Zhou
mesh_terms: D002681:China / Q000453:epidemiology; D058872:Epidemics; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D012621:Seasons; D012984:Software; D014917:Whooping Cough / Q000453:epidemiology* / Q000473:pathology