Journal of Shandong University (Health Sciences) ›› 2018, Vol. 56 ›› Issue (9): 71-76.doi: 10.6040/j.issn.1671-7554.0.2017.1266

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Application of multiple seasonal model in prediction of tuberculosis epidemic

LIU Xiaodi1, MA Jie1, XIU Jingwei1, CUI Qingxia1,2, LI Wangchen1,2,WANG Zaixiang1,2   

  1. 1. School of Public Health and Management, Weifang Medical University, Weifang 261053, Shandong, China;
    2. “Health Shandong” Major Social Risk Prediction and Governance Collaborative Innovation Center, Weifang 261053, Shandong, China
  • Published:2022-09-27

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Abstract: Objective To explore the feasibility of moving multiple seasonal model for predicting tuberculosis and provide a scientific evidence for the targeted prevention and control policy of tuberculosis. Methods Based on the national tuberculosis data reported by the Chinese Center for Disease Control and Prevention from January 2011 to December 2016, a multiplicative seasonal model was established. The model was used to predict the number of cases of tuberculosis in China from January to September 2017 and to evaluate its predicted effect. Results From January 2011 to December 2016, the number of pulmonary tuberculosis cases in China showed a seasonal effect with annual cycle, and the trend of long-term declining. All parameters of ARIMA(0,1,1)(0,1,1)12(without constant term)were statistically tested(P<0.05). The residual sequence was a white noise sequence(P>0.05). Goodness of fit was the best(AIC=1 223.004, SBC=1 227.159). The projections for January to September 2017 were generally consistent with the actual values. Conclusion ARIMA(0,1,1)(0,1,1)12(without constant term)can be used to predict the epidemic situation of pulmonary tuberculosis in China. The model has good popularization and application value.

Key words: Multiple seasonal model, Tuberculosis, Epidemic, Prediction

CLC Number: 

  • R183
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