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Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

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 poten...

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Autores principales: Wang, Yongbin, Xu, Chunjie, Ren, Jingchao, Wu, Weidong, Zhao, Xiangmei, Chao, Ling, Liang, Wenjuan, Yao, Sanqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062399/
https://www.ncbi.nlm.nih.gov/pubmed/32184635
http://dx.doi.org/10.2147/IDR.S238225
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author Wang, Yongbin
Xu, Chunjie
Ren, Jingchao
Wu, Weidong
Zhao, Xiangmei
Chao, Ling
Liang, Wenjuan
Yao, Sanqiao
author_facet Wang, Yongbin
Xu, Chunjie
Ren, Jingchao
Wu, Weidong
Zhao, Xiangmei
Chao, Ling
Liang, Wenjuan
Yao, Sanqiao
author_sort Wang, Yongbin
collection PubMed
description 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.
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spelling pubmed-70623992020-03-17 Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework Wang, Yongbin Xu, Chunjie Ren, Jingchao Wu, Weidong Zhao, Xiangmei Chao, Ling Liang, Wenjuan Yao, Sanqiao Infect Drug Resist Original Research 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. Dove 2020-03-05 /pmc/articles/PMC7062399/ /pubmed/32184635 http://dx.doi.org/10.2147/IDR.S238225 Text en © 2020 Wang et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Wang, Yongbin
Xu, Chunjie
Ren, Jingchao
Wu, Weidong
Zhao, Xiangmei
Chao, Ling
Liang, Wenjuan
Yao, Sanqiao
Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title_full Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title_fullStr Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title_full_unstemmed Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title_short Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework
title_sort secular seasonality and trend forecasting of tuberculosis incidence rate in china using the advanced error-trend-seasonal framework
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062399/
https://www.ncbi.nlm.nih.gov/pubmed/32184635
http://dx.doi.org/10.2147/IDR.S238225
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