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Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Author

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  • Yan-Ling Zheng
  • Li-Ping Zhang
  • Xue-Liang Zhang
  • Kai Wang
  • Yu-Jian Zheng

Abstract

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.

Suggested Citation

  • Yan-Ling Zheng & Li-Ping Zhang & Xue-Liang Zhang & Kai Wang & Yu-Jian Zheng, 2015. "Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
  • Handle: RePEc:plo:pone00:0116832
    DOI: 10.1371/journal.pone.0116832
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    Citations

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    Cited by:

    1. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    2. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    3. Rui Zhang & Hejia Song & Qiulan Chen & Yu Wang & Songwang Wang & Yonghong Li, 2022. "Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-14, January.
    4. Daren Zhao & Huiwu Zhang & Qing Cao & Zhiyi Wang & Sizhang He & Minghua Zhou & Ruihua Zhang, 2022. "The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
    5. Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    6. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    7. Nyoni, Smartson. Pumulani & Nyoni, Thabani, 2019. "Forecasting TB notifications at Zengeza clinic in Chitungwiza, Zimbabwe," MPRA Paper 97331, University Library of Munich, Germany.
    8. Wudi Wei & Junjun Jiang & Hao Liang & Lian Gao & Bingyu Liang & Jiegang Huang & Ning Zang & Yanyan Liao & Jun Yu & Jingzhen Lai & Fengxiang Qin & Jinming Su & Li Ye & Hui Chen, 2016. "Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    9. Linda Watson & Siwei Qi & Andrea DeIure & Claire Link & Lindsi Chmielewski & April Hildebrand & Krista Rawson & Dean Ruether, 2021. "Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes," IJERPH, MDPI, vol. 18(16), pages 1-11, August.

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