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Time-series analysis of tuberculosis from 2005 to 2017 in China.
Epidemiol Infect. 2018 06; 146(8):935-939.EI

Abstract

Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

Authors+Show Affiliations

Kunshan Centers for Disease Control and Prevention,Kunshan,China.Kunshan Centers for Disease Control and Prevention,Kunshan,China.Kunshan Centers for Disease Control and Prevention,Kunshan,China.Kunshan Centers for Disease Control and Prevention,Kunshan,China.

Pub Type(s)

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

Language

eng

PubMed ID

29708082

Citation

Wang, H, et al. "Time-series Analysis of Tuberculosis From 2005 to 2017 in China." Epidemiology and Infection, vol. 146, no. 8, 2018, pp. 935-939.
Wang H, Tian CW, Wang WM, et al. Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect. 2018;146(8):935-939.
Wang, H., Tian, C. W., Wang, W. M., & Luo, X. M. (2018). Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiology and Infection, 146(8), 935-939. https://doi.org/10.1017/S0950268818001115
Wang H, et al. Time-series Analysis of Tuberculosis From 2005 to 2017 in China. Epidemiol Infect. 2018;146(8):935-939. PubMed PMID: 29708082.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Time-series analysis of tuberculosis from 2005 to 2017 in China. AU - Wang,H, AU - Tian,C W, AU - Wang,W M, AU - Luo,X M, Y1 - 2018/04/30/ PY - 2018/5/1/pubmed PY - 2019/1/18/medline PY - 2018/5/1/entrez KW - Generalised regression neural network model KW - notification rate KW - seasonal autoregressive integrated moving average model KW - tuberculosis SP - 935 EP - 939 JF - Epidemiology and infection JO - Epidemiol. Infect. VL - 146 IS - 8 N2 - Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence. SN - 1469-4409 UR - https://www.unboundmedicine.com/medline/citation/29708082/Time_series_analysis_of_tuberculosis_from_2005_to_2017_in_China_ L2 - https://www.cambridge.org/core/product/identifier/S0950268818001115/type/journal_article DB - PRIME DP - Unbound Medicine ER -