目的分析2018~2019年北京市大兴区手足口病病原学特征及影响因素。方法数据来源于2018年1月~2019年12月大兴区手足口病监测及疫情采集的799例咽拭子标本的肠道病毒核酸检测结果,采用Stata/SE 15进行阳性病例病原学构成的描述性统计分析...目的分析2018~2019年北京市大兴区手足口病病原学特征及影响因素。方法数据来源于2018年1月~2019年12月大兴区手足口病监测及疫情采集的799例咽拭子标本的肠道病毒核酸检测结果,采用Stata/SE 15进行阳性病例病原学构成的描述性统计分析和利用Logistic回归的影响因素分析。结果在手足口病阳性样本中,2018年优势毒株为柯萨奇A组6型(Coxsackie virus group A type 6,CA6)(59.97%),2019年为柯萨奇A组16型(Coxsackie virus group A type 16,CA16)(48.82%)和CA6(40.53%),差异有统计学意义(P<0.05)。从不同社会经济学特征下手足口病病原的构成来看,描述性统计分析结果显示,CA6在不同年龄组、性别、职业和城乡分布均占最高比例,但病原学构成在不同社会经济学特征不存在统计学意义(P>0.05)。Logistic分析结果显示,CA16在2019年发生的可能性要更高(OR=3.654,P<0.01),CA6和其他肠道病毒发生在2018年的可能性要更高(OR<1,P<0.01);CA16更易发生在具备女性、2岁组、托幼、郊区等社会经济学特征的人群中(OR>1),CA6更易发生在具备男性、3岁组、学生、城区等社会经济学特征的人群中(OR>1),但差异无统计学意义。结论应进一步研发针对CA16和CA6等的多价手足口病疫苗,并结合病原在不同社会经济学特征的分布情况,有效应对手足口病病原学的动态变化;未来应进一步加强有关手足口病病原学构成及影响因素的研究。展开更多
Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study...Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.展开更多
文摘目的分析2018~2019年北京市大兴区手足口病病原学特征及影响因素。方法数据来源于2018年1月~2019年12月大兴区手足口病监测及疫情采集的799例咽拭子标本的肠道病毒核酸检测结果,采用Stata/SE 15进行阳性病例病原学构成的描述性统计分析和利用Logistic回归的影响因素分析。结果在手足口病阳性样本中,2018年优势毒株为柯萨奇A组6型(Coxsackie virus group A type 6,CA6)(59.97%),2019年为柯萨奇A组16型(Coxsackie virus group A type 16,CA16)(48.82%)和CA6(40.53%),差异有统计学意义(P<0.05)。从不同社会经济学特征下手足口病病原的构成来看,描述性统计分析结果显示,CA6在不同年龄组、性别、职业和城乡分布均占最高比例,但病原学构成在不同社会经济学特征不存在统计学意义(P>0.05)。Logistic分析结果显示,CA16在2019年发生的可能性要更高(OR=3.654,P<0.01),CA6和其他肠道病毒发生在2018年的可能性要更高(OR<1,P<0.01);CA16更易发生在具备女性、2岁组、托幼、郊区等社会经济学特征的人群中(OR>1),CA6更易发生在具备男性、3岁组、学生、城区等社会经济学特征的人群中(OR>1),但差异无统计学意义。结论应进一步研发针对CA16和CA6等的多价手足口病疫苗,并结合病原在不同社会经济学特征的分布情况,有效应对手足口病病原学的动态变化;未来应进一步加强有关手足口病病原学构成及影响因素的研究。
基金Projects(2007JT3018, 2008JT1013, 2009FJ4056) supported by the Key Project in Hunan Science and Technology Program, ChinaProject(20090161120014) supported by the New Teachers Sustentation Fund in Doctoral Program, Ministry of Education, China
文摘Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.
