| 周晓磊,张博宇,丛显斌,李仲来,姚晓恒,鞠成,徐成,张贵军,段天一,陈磊,刘振才.最优回归子集法在达乌尔黄鼠疫源地风险分级中的应用[J].Chinese journal of Epidemiology,2014,35(2):170-173 |
| 最优回归子集法在达乌尔黄鼠疫源地风险分级中的应用 |
| Application of Best Subsets Regression on the risk classification for Spermophilus Dauricus Focus |
| Received:August 09, 2013 |
| DOI：10.3760/cma.j.issn.0254-6450.2014.02.015 |
| KeyWord: 最优回归子集法 达乌尔黄鼠疫源地 风险分级 预测 |
| English Key Word: Best subsets regression Spermophilus Dauricus Focus Risk classification Forecasting |
| FundProject:卫生行业科研专项项目(201202021) |
| Author Name | Affiliation | E-mail | | Zhou Xiaolei | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Zhang Boyu | Beijing Normal University | | | Cong Xianbin | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | Email:cxb0805@sina.com | | Li Zhonglai | Beijing Normal University | | | Yao Xiaohuan | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Ju Cheng | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Xu Cheng | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Zhang Guijun | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Duan Tianyi | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Chen Lei | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | | | Liu Zhencai | Chinese Base for Control of Plague and Brucellosis, Chinese Center for Disease Control and Prevention, Baicheng 137000, China | |
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| Abstract: |
| 目的 对达乌尔黄鼠疫源地动物鼠疫流行情况进行风险分级。方法 对内蒙古达乌尔黄鼠疫源地动物鼠疫流行总体数据7个监测指标（鼠密度、鼠体染蚤率、鼠体蚤指数、巢穴蚤染蚤率、巢穴蚤指数、洞干蚤染蚤率、洞干蚤指数）利用Matlab软件中最优回归子集法进行风险分级，采用指数平滑法预测2012年动物鼠疫流行的风险。按照检出鼠疫菌为流行（y=1），未检出菌视为不流行（y=0），将风险分为流行、高风险及不流行3级，若预报值y>2／3，预报为流行；若预报值y<1／3，预报为不流行；若1／3≤y≤2／3，预报为高风险。结果 对风险分级采用实际数据进行拟合，当y>2／3时预报流行的拟合率均为100％；回归模型的回归因子≥4个时，y<1／3时预报流行的拟合率均为100％；1／3≤y≤2／3时预报流行的拟合率约为50％。结论 风险分级预测结果表明2012年达乌尔黄鼠疫源地不会发生动物鼠疫流行，预测结果与实际情况相符（当年实际并未检出鼠疫菌）。 |
| English Abstract: |
| Objective To study the risk classification of animal plague in Spermophilus Dauricus Focus, using the Best Subsets Regression (BSR) model. Methods Matlab, BSR and exponential smoothing were employed to develop and evaluate a model for risk classification as well as to forecast plague epidemics at the Spennophilus Dauricus Focus. Data was based upon the Inner Mongolia surveillance programs. This model involved 7 risk factors , including density of Spermophilus dauricus, percentage of hosts infested, host flea index, percentage of nests infested, nest flea index, percentage of runways infested, and runway flea index. Results Forecasting values of the classification model(CM) were calculated and grouped into 3 risk levels. Values that over 2/3 of the CM would indicate the existence of potential epidemics while those below 1/3 would indicate that there were no risk for epidemics but when values that were in between would indicate that there exist for high risk. Annually, during the observation period in the Inner Mongolia Spermophilus Dauricus Foci, the detection of Yersinia pesos gave a risk rating value of 1 which stood for existing epidemics, while nil detection rate generated a `zero' value which representing the situation of non-epidemic. The overall plague epidemics forecasting surveillance programs in 2012 at the Spermophilus Dauricus Foci indicated that no active plague was obsered. When the forecasting values became over 2J3, combinations of all the risk factors would achieve the consistency rates of 100%. When the forecasting
values were below 1/3，combinations of at least the first 4 factors could also achieve the consistency rates of 100%. However, when the forecasting values fell in between, combinations of at least the first 4 factors would achieve the consistency rates of around 50%.Conclusion Results from our study showed that plague would not be active to become epidemic , in 2012. |
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