Estimating the Temporal Epidemiological Trends of Tuberculosis Incidence by Using an Advanced Theta Method

Yongbin Wang Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China

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Bingjie Zhang Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China

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Chenlu Xue Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China

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Yanyan Li Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China

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Xinxiao Li Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China

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We aimed to assess the temporal epidemiological trends in tuberculosis (TB) by use of an advanced Theta method. The TB incidence data from Tianjin, Heilongjiang, Hubei, and Guangxi provinces in China, spanning January 2005 to December 2019, were extracted. We then constructed and compared various modeling approaches, including the seasonal autoregressive integrated moving average (SARIMA) model, the Theta model, the standard Theta model (STM), the dynamic optimized Theta model (DOTM), the dynamic standard Theta model (DSTM), and the optimized Theta model (OTM). During 2005–2019, these four provinces recorded a total of 2,068,399 TB cases. Analyses indicated that TB exhibited seasonality, with prominent peaks in spring and winter, and a slight downward trend was seen in incidence. In the Tianjin forecast, the OTM consistently demonstrated superior performance with the lowest values across metrics, including mean absolute deviation (0.159), mean absolute percentage error (7.032), root mean square error (0.21), mean error rate (0.068), and root mean square percentage error (0.093), compared with those of SARIMA (0.397, 16.654, 0.436, 0.169, and 0.179, respectively), Theta (0.166, 7.248, 0.231, 0.071, and 0.102, respectively), DOTM (0.169, 7.341, 0.234, 0.072, and 0.102, respectively), DSTM (0.169, 7.532, 0.203, 0.072, and 0.092, respectively), and STM (0.165, 7.218, 0.231, 0.070, and 0.101, respectively). Similar results were also observed in the other provinces, emphasizing the effectiveness of the OTM in estimating TB trends. Thus, the OTM may serve as a beneficial and effective tool for estimating the temporal epidemiological trends of TB.

Author Notes

Financial Support: This work was supported by Natural Science Foundation in Henan Province, the Key Scientific Research Project of Universities, the Innovation and Entrepreneurship Training Project for University Students of Henan Province, and Xinxiang Medical University (Grant nos. 222300420265, 21A330004, S202110472047, S202010472007, and XYXSKYZ201932).

Disclosures: The institutional review board of Xinxiang Medical University approved this study protocol (no. XYLL-2019072). All methods were carried out under relevant guidelines and regulations. The need for informed consent was waived by the study Ethics Committee of Xinxiang Medical University because we used secondary, anonymized data for our analysis.

Authors’ addresses: Yongbin Wang, Bingjie Zhang, Chenlu Xue, Yanyan Li, and Xinxiao Li, Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People’s Republic of China, E-mails: wybwho@163.com, 898390847@qq.com, 995841156@qq.com2320192093@qq.com, and 18339530223@163.com.

Address correspondence to Yongbin Wang, Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Rd., Hongqi District, Xinxiang, Henan Province 453003, People’s Republic of China. E-mail: wybwho@163.com
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