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机构地区:[1]四川大学华西公共卫生学院流行病学教研室,成都610041 [2]西南财经大学经济数学学院 [3]自贡市疾病预防控制中心
出 处:《现代预防医学》2010年第4期624-627,共4页Modern Preventive Medicine
摘 要:[目的]运用灰色模型预测某市肺结核发病率。探索数据波动较大的情况下,适用的灰色模型预测方法。[方法]根据某市1997~2008年肺结核发病率数据分别建立动态GM(1,1),新陈代谢GM(1,1)和常规GM(1,1)。比较3种模型预测精度和准确性,选择合适肺结核发病率的预测模型并进行外推预测。[结果]检验性预测中,3种模型预测精度均为一级,预测值相对误差依次为-13.56%、-12.27%和-31.18%。将动态GM(1,1)和新陈代谢GM(1,1)作为该市肺结核发病率预测模型,两种预测模型的预测精度分别为二级和四级。采用动态GM(1,1)对该市肺结核发病率进行外推预测,2009年、2010年该市肺结核发病率分别为90.22/10万89.66/10万,[结论]针对该市肺结核发病率数据波动较大的情况,采用动态GM(1.1)进行预测是比较适用的方法.[ Objective] To apply the grey model for prediction of the incidence of pulmonary tuberculosis in a city and to explore the applicable predictive method in case that the data is fluctuant. [Methods] According to the incidence of pulmonary tuberculosis in the city from 1997-2008, respectively, we built the Dynamic grey model ( 1, 1) , Metabolizing grey model (1, 1) and Conventional grey model (1, 1 ) for prediction. [Results] The result of prediction test showed that the predictive precision of the three models was good, the relative error of the predictive value were -13.56%, -12.27% and -31.18%, respectively. Therefore we considered the dynamic grey model ( 1, 1 ) and metabolizing grey model ( 1, 1 ) should be applied to the formal prediction. In the formal prediction, the precision of two models were grade two and grade four. We used the Dynamic grey model ( 1, 1) to predict the incidence of pulmonary tuberculosis in the city of the year 2009 and 2010, which were 90.22/100 000 and 89.66/100 000, respectively. [Conclusion ] The condition of the data in our study is fluctuant, therefore, using the Dynamic grey model (1, 1) for prediction is an applicable method.
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