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Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework.
Infect Drug Resist. 2020; 13:733-747.ID

Abstract

Objective

Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China.

Methods

We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects.

Results

In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead.

Conclusion

The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

Authors+Show Affiliations

Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32184635

Citation

Wang, Yongbin, et al. "Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework." Infection and Drug Resistance, vol. 13, 2020, pp. 733-747.
Wang Y, Xu C, Ren J, et al. Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework. Infection and drug resistance. 2020;13:733-747.
Wang, Y., Xu, C., Ren, J., Wu, W., Zhao, X., Chao, L., Liang, W., & Yao, S. (2020). Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework. Infection and Drug Resistance, 13, 733-747. https://doi.org/10.2147/IDR.S238225
Wang Y, et al. Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework. Infection and drug resistance. 2020;13:733-747. PubMed PMID: 32184635.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework. AU - Wang,Yongbin, AU - Xu,Chunjie, AU - Ren,Jingchao, AU - Wu,Weidong, AU - Zhao,Xiangmei, AU - Chao,Ling, AU - Liang,Wenjuan, AU - Yao,Sanqiao, Y1 - 2020/03/05/ PY - 2019/11/12/received PY - 2020/02/25/accepted PY - 2020/3/19/entrez PY - 2020/3/19/pubmed PY - 2020/3/19/medline KW - ETS KW - SARIMA KW - forecasting KW - incidence KW - models KW - tuberculosis SP - 733 EP - 747 JF - Infection and drug resistance VL - 13 N2 - Objective: Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China. Methods: We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects. Results: In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead. Conclusion: The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy. SN - 1178-6973 UR - https://www.unboundmedicine.com/medline/citation/32184635/Secular_Seasonality_and_Trend_Forecasting_of_Tuberculosis_Incidence_Rate_in_China_Using_the_Advanced_Error_Trend_Seasonal_Framework_ L2 - https://dx.doi.org/10.2147/IDR.S238225 DB - PRIME DP - Unbound Medicine ER -
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