Journal of Shandong University (Health Sciences) ›› 2021, Vol. 59 ›› Issue (7): 112-118.doi: 10.6040/j.issn.1671-7554.0.2021.0049

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Application of ARIMA multiplicative seasonal model in the prediction of pulmonary tuberculosis incidence in Shandong Province

TIAN Qing, LIU Yongpeng, ZHANG Jingjing, LIU Hongqing   

  1. Department of Health Statistics, School of Public Health, Weifang Medical University, Weifang 261053, Shandong, China
  • Published:2021-07-16

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Abstract: Objective According to the seasonality and trend of pulmonary tuberculosis in Shandong Province, to establish an Autoregressive Integrated Moving Average(ARIMA)product seasonal model, predict the incidence of pulmonary tuberculosis in Shandong Province and adjust prevention and control measures. Methods An optimal model for the monthly data of pulmonary tuberculosis infectious diseases in Shandong Province from January 2010 to December 2019 was established using R software. The number of pulmonary tuberculosis cases from January to October 2020 was predicted and compared with the actual value to evaluate the prediction effect of the model. Further, the incidence trend of pulmonary tuberculosis cases from November 2020 to December 2021 was predicted. Results The number of pulmonary tuberculosis cases in Shandong Province showed an annual cycle, and the optimal model is ARIMA(3,1,0)(0,1,1)12. The accuracy of fitting results from January 2010 to December 2019 showed that the mean absolute percentage error was only 5.50%. The mean absolute percentage error of the model prediction from January to October 2020 was 21.69%. Compared with the same period, there was a slight change in the number of cases from November 2020 to December 2021. Conclusion The ARIMA product seasonal model can rather satisfactorily fit and predict the incidence of tuberculosis in Shandong Province.

Key words: Tuberculosis, Time series, ARIMA model, Seasonal, Predict

CLC Number: 

  • R183
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