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Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.
Sci Rep. 2016 08 31; 6:32367.SR

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+Show Affiliations

Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, China.Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Central Laboratory, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.Department of Respiratory Medicine, Affiliated Hospital of Chengdu University, School of Clinical Medicine, Chengdu University, China.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

27577101

Citation

Zeng, Qianglin, et al. "Time Series Analysis of Temporal Trends in the Pertussis Incidence in Mainland China From 2005 to 2016." Scientific Reports, vol. 6, 2016, p. 32367.
Zeng Q, Li D, Huang G, et al. Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016. Sci Rep. 2016;6:32367.
Zeng, Q., Li, D., Huang, G., Xia, J., Wang, X., Zhang, Y., Tang, W., & Zhou, H. (2016). Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016. Scientific Reports, 6, 32367. https://doi.org/10.1038/srep32367
Zeng Q, et al. Time Series Analysis of Temporal Trends in the Pertussis Incidence in Mainland China From 2005 to 2016. Sci Rep. 2016 08 31;6:32367. PubMed PMID: 27577101.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016. AU - Zeng,Qianglin, AU - Li,Dandan, AU - Huang,Gui, AU - Xia,Jin, AU - Wang,Xiaoming, AU - Zhang,Yamei, AU - Tang,Wanping, AU - Zhou,Hui, Y1 - 2016/08/31/ PY - 2016/04/28/received PY - 2016/08/08/accepted PY - 2016/9/1/entrez PY - 2016/9/1/pubmed PY - 2018/5/23/medline SP - 32367 EP - 32367 JF - Scientific reports JO - Sci Rep VL - 6 N2 - 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. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/27577101/Time_series_analysis_of_temporal_trends_in_the_pertussis_incidence_in_Mainland_China_from_2005_to_2016_ L2 - http://dx.doi.org/10.1038/srep32367 DB - PRIME DP - Unbound Medicine ER -