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An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

Authors Wang Y, Xu C, Li Y, Wu W, Gui L, Ren J, Yao S

Received 29 September 2019

Accepted for publication 22 February 2020

Published 24 March 2020 Volume 2020:13 Pages 867—880

DOI https://doi.org/10.2147/IDR.S232854

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr Sahil Khanna


Yongbin Wang,1,* Chunjie Xu,2,* Yuchun Li,1 Weidong Wu,1 Lihui Gui,1 Jingchao Ren,1 Sanqiao Yao1

1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Sanqiao Yao; Yongbin Wang
Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453000, People’s Republic of China
Tel +86 373831646
Email 1153483589@qq.com; wybwho@163.com

Purpose: Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR).
Methods: We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate .
Results: During 2004– 2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)12, NNNAR(7,1,4)12 and SARIMA(2,0,2)(1,1,0)12-NNNAR(3,1,7)12, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results.
Conclusion: The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

Keywords: tuberculosis, SARIMA model, NNNAR model, hybrid model, forecasting, incidence rate

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