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| 蚊虫密度的气象影响因素分析 | |
| 引用本文： | 代培芳,赵俊英,刘美德,孔祥盛,翟如芳,王海娇,程璟侠. 蚊虫密度的气象影响因素分析[J]. 中国媒介生物学及控制杂志, 2011, 22(6): 547-549 |
| 作者姓名： | 代培芳 赵俊英 刘美德 孔祥盛 翟如芳 王海娇 程璟侠 |
| 作者单位： | 1. 山西省疾病预防控制中心病媒生物防控科,免疫规划科,山西太原030012 2. 军事医学科学院微生物流行病研究所病原微生物生物安全国家重点实验室 |
| 基金项目： | 山西省自然科学基金(2008011070); 山西省卫生厅科技攻关计划项目(200759,20100140)~~ |
| 摘 要： | 目的 探讨山西省运城市某流行性乙型脑炎(乙脑)高发县蚊虫密度与气象因素之间的关系,筛选适合因子预测蚊虫密度变化趋势.方法 监测2007-2009年5-10月运城市某县蚊虫密度并收集同期气象资料,气象数据经膨化处理,用SPSS17.0软件分析两者相关性,并用逐步回归分析建立蚊虫密度的气象因子拟合模型.结果 蚊虫季节消长曲线为单峰型,5月出现,8月达高峰,10月消亡.蚊虫密度与月平均温度、月平均气压等相关,与月日照、相对湿度无关.逐步回归分析得出蚊虫密度的气压回归方程,ap02(当月及前2个月的平均气压)和ap1(提前1个月的平均气压)有良好的拟合效果,两者相比ap1具有更好的实际操作性.结论 气象因素对蚊虫密度有重要影响,可以利用气压拟合模型预测蚊虫密度变化趋势. |
| 关 键 词： | 蚊虫密度 气象因素 拟合模型 传染病防控 |
Analysis on the influence of meteorological parameters on mosquito density |
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| DAI Pei-fang , ZHAO Jun-ying , LIU Mei-de , KONG Xiang-sheng , ZHAI Ru-fang , WANG Hai-jiao , CHENG Jing-xia. Analysis on the influence of meteorological parameters on mosquito density[J]. Chinese Journal of Vector Biology and Control, 2011, 22(6): 547-549 | |
| Authors: | DAI Pei-fang ZHAO Jun-ying LIU Mei-de KONG Xiang-sheng ZHAI Ru-fang WANG Hai-jiao CHENG Jing-xia |
| Affiliation: | DAI Pei-fang~1,ZHAO Jun-ying~1,LIU Mei-de~2,KONG Xiang-sheng~1,ZHAI Ru-fang~1,WANG Hai-jiao~1,CHENG Jing-xia~1 1 Shanxi Center for Disease Control and Prevention,Taiyuan 030012,Shanxi Province,China,2 Beijing Institute of Micobiology of Epidemiology,State Key Laboratory of Pathogen and Biosecurity |
| Abstract: | Objective To identify the relationship between mosquito density and meteorological parameters for selection of appropriate predicting factors for the change of mosquito density in Yuncheng,Shanxi.Methods Spearman' s rank correlation was used in the analysis of the correlation between meteorological parameters and mosquito density per month from 2007 to 2009. Stepwise regression analysis was performed to model the mosquito density change regarding meteorological parameters.Results Single wave was observed in... |
| Keywords: | Mosquito density Meteorological parameters Predicting model Disease control |
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