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山东大学学报 (医学版) ›› 2018, Vol. 56 ›› Issue (9): 71-76.doi: 10.6040/j.issn.1671-7554.0.2017.1266

• 公共卫生与管理学 • 上一篇    

乘积季节模型在我国肺结核疫情预测中的应用

刘晓迪1,马洁1,修璟威1,崔庆霞1,2,李望晨1,2,王在翔1,2   

  1. 1.潍坊医学院公共卫生与管理学院, 山东 潍坊 261053;2.“健康山东”重大社会风险预测与治理协同创新中心, 山东 潍坊 261053
  • 发布日期:2022-09-27
  • 通讯作者: 王在翔. E-mail:WANGZX1@126.com
  • 基金资助:
    “健康山东”重大社会风险预测与治理协同创新中心重点项目(XT1405003);潍坊医学院科技创新基金(K1302022)

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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摘要： 目的 探讨乘积季节模型预测肺结核发病例数的可行性,为肺结核的针对性防控提供理论依据。 方法 根据中国疾病预防控制中心2011年1月至2016年12月的全国肺结核上报资料建立乘积季节模型,并预测2017年1月至9月数据,评价其预测效果。 结果 2011年1月至2016年12月我国肺结核发病例数呈现以年为周期的季节效应,并且出现长期递减的趋势;乘积季节ARIMA(0,1,1)(0,1,1)12(不含常数项)模型所有参数都通过统计学检验(P<0.05), 残差序列为白噪声序列(P>0.05), 拟合优度相对最好(AIC=1 223.004, SBC=1 227.159);模型对2017年1月至9月的预测值与实际值基本吻合,预测效果较好。 结论 乘积季节ARIMA(0,1,1)(0,1,1)12(不含常数项)模型可用于预测我国肺结核疫情,具有较好的推广应用价值。

关键词: 乘积季节模型, 肺结核, 疫情, 预测

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

中图分类号: 

  • R183
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