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Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.
BMJ Open. 2019 07 31; 9(7):e024409.BO

Abstract

OBJECTIVE

Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.

METHODS

We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques.

RESULTS

In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12 model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high.

CONCLUSIONS

The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

Authors+Show Affiliations

Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China.

Pub Type(s)

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

Language

eng

PubMed ID

31371283

Citation

Wang, Yongbin, et al. "Temporal Trends Analysis of Tuberculosis Morbidity in Mainland China From 1997 to 2025 Using a New SARIMA-NARNNX Hybrid Model." BMJ Open, vol. 9, no. 7, 2019, pp. e024409.
Wang Y, Xu C, Zhang S, et al. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open. 2019;9(7):e024409.
Wang, Y., Xu, C., Zhang, S., Wang, Z., Yang, L., Zhu, Y., & Yuan, J. (2019). Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open, 9(7), e024409. https://doi.org/10.1136/bmjopen-2018-024409
Wang Y, et al. Temporal Trends Analysis of Tuberculosis Morbidity in Mainland China From 1997 to 2025 Using a New SARIMA-NARNNX Hybrid Model. BMJ Open. 2019 07 31;9(7):e024409. PubMed PMID: 31371283.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. AU - Wang,Yongbin, AU - Xu,Chunjie, AU - Zhang,Shengkui, AU - Wang,Zhende, AU - Yang,Li, AU - Zhu,Ying, AU - Yuan,Juxiang, Y1 - 2019/07/31/ PY - 2019/8/3/entrez PY - 2019/8/3/pubmed PY - 2020/7/24/medline KW - forecasting KW - models KW - statistics KW - tuberculosis SP - e024409 EP - e024409 JF - BMJ open JO - BMJ Open VL - 9 IS - 7 N2 - OBJECTIVE: Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity. METHODS: We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques. RESULTS: In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12 model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high. CONCLUSIONS: The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease. SN - 2044-6055 UR - https://www.unboundmedicine.com/medline/citation/31371283/Temporal_trends_analysis_of_tuberculosis_morbidity_in_mainland_China_from_1997_to_2025_using_a_new_SARIMA_NARNNX_hybrid_model_ L2 - http://bmjopen.bmj.com/cgi/pmidlookup?view=long&pmid=31371283 DB - PRIME DP - Unbound Medicine ER -