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李立, 杨召, 叶中辉, 郭奕瑞, 梁淑英, 尤爱国, 张肖肖, 王重建. 灰色GM(1,1)模型在结核病发病率预测中应用[J]. 中国公共卫生, 2014, 30(4): 396-397. DOI: 10.11847/zgggws2014-30-04-04
引用本文: 李立, 杨召, 叶中辉, 郭奕瑞, 梁淑英, 尤爱国, 张肖肖, 王重建. 灰色GM(1,1)模型在结核病发病率预测中应用[J]. 中国公共卫生, 2014, 30(4): 396-397. DOI: 10.11847/zgggws2014-30-04-04
LI Li, YANG Zhao, YE Zhong-hui.et al, . Application of gray GM (1,1)model to predict incidence of tuberculosis in Henan province[J]. Chinese Journal of Public Health, 2014, 30(4): 396-397. DOI: 10.11847/zgggws2014-30-04-04
Citation: LI Li, YANG Zhao, YE Zhong-hui.et al, . Application of gray GM (1,1)model to predict incidence of tuberculosis in Henan province[J]. Chinese Journal of Public Health, 2014, 30(4): 396-397. DOI: 10.11847/zgggws2014-30-04-04

灰色GM(1,1)模型在结核病发病率预测中应用

Application of gray GM (1,1)model to predict incidence of tuberculosis in Henan province

  • 摘要: 目的应用灰色GM(1,1)模型拟合结核病发病率,探讨其在结核病发病率预测中的应用。方法利用河南省2004—2011年结核病疫情资料,建立结核病发病率灰色GM(1,1)预测模型,评价模型预测效能,预测该省2012—2014年结核病发病率。结果河南省结核病发病率建立的灰色GM(1,1)预测模型的平均相对误差、后验差比值(C)、小误差概率(P)及平均级比偏差值(P)分别为3.71%、0.21、1.00和0.0269,模型预测效能较好,利用该模型对2012—2014年结核病发病率进行外推预测,结果分别为 65.82/10万、56.42/10万、47.31/10万。结论灰色GM(1,1)模型对于结核病发病率的预测效能较好,预测结果对于结核病预防控制具有重要指导意义。

     

    Abstract: ObjectiveTo develop a gray GM (1,1)model and to explore its potential application in prediction of tuberculosis incidence.MethodsA gray GM (1,1)model was established using the epidemic data of tuberculosis in Henan province from January 1,2004 to December 31,2011,and the predictive performance was tested and accessed.ResultsThe average relative error,posterior margin ratio,small error probability,and average level deviation were 3.71%,0.21,1.00,and 0.0269,respectively,suggesting the gray GM (1,1)model could be applied for predicting tuberculosis incidence.Based on the model,the tuberculosis incidence predicted for the province form 2012 to 2014 were 65.82/105,56.42/105,and 47.31/105,respectively.ConclusionThe gray GM (1,1)model could be used to predict the incidence of tuberculosis for the prevention management and measurement of the disease.

     

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