中国媒介生物学及控制杂志 ›› 2014, Vol. 25 ›› Issue (5): 405-407.DOI: 10.11853/j.issn.1003.4692.2014.05.005

• 论著 • 上一篇    下一篇

上海市白纹伊蚊密度与气象因素关系的研究

周毅彬1, 冷培恩1, 顾君忠2, 龙春宇2, 陈鹏2   

  1. 1 上海市疾病预防控制中心病媒生物防治科,上海 200336;
    2 华东师范大学
  • 收稿日期:2014-03-26 出版日期:2014-10-20 发布日期:2014-10-20
  • 作者简介:周毅彬,男,博士,副主任医师,主要从事媒介相关工作,Email: zhouyibin@scdc.sh.cn
  • 基金资助:
    上海市公共卫生重点学科建设项目(12GWZX0101)

Study on relationship between population density of Aedes albopictusand meteorological factors in Shanghai, China

ZHOU Yi-bin1, LENG Pei-en1, GU Jun-zhong2, LONG Chun-yu2, CHEN Peng2   

  1. 1 Shanghai Center for Disease Control and Prevention, Shanghai 200336, China;
    2 East China Normal University
  • Received:2014-03-26 Online:2014-10-20 Published:2014-10-20
  • Supported by:
    Supported by the Construction Project of Shanghai Municipal Key Disciplines of Public Health (No. 12GWZX0101)

摘要: 目的 研究气象因素(组合)对白纹伊蚊密度的影响,及其在白纹伊蚊综合治理中的应用。方法 采用matlab多元逐步回归和神经网络研究上海地区2005年1月至2008年12月白纹伊蚊密度变化与气象因素间的关系。结果 多元逐步回归方程为y=-18.206 64x2+3.066 16x3-3.383 90x4+1.891 53x7+1.689 86x8+25.939 46x13+1.936 35x18-2217.100 90,其中x2代表最低气温,x3代表20-20降雨量(前一日20:00到次日20:00总降雨量),x4代表日照时数,x7代表最低相对湿度,x8代表日平均风速,x13代表20:00温度,x18代表08:00气压。回归方程获得较好的预测(拟合)结果,R?square为0.897 00,采用神经网络的方法R?square为0.913 19,在此应用场景下神经网络比回归分析具有更好的实际效果。结论 与蚊虫密度相关的气象因素主要为最低气温、20-20降雨量、日照时数、最低相对湿度、日平均风速、20:00温度和08:00气压。

关键词: 白纹伊蚊, 成蚊密度, 气象因素

Abstract: Objective To study the relationship between the population density of Aedes albopictus and meteorological factors and its application in the control of Ae. albopictus. Methods The relationship between Ae. albopictus density and meteorological factors in Shanghai from January 2005 to December 2008 was studied using multiple stepwise regression and neural network on Matlab. Results The combination of meteorological factors (minimum temperature, 8 pm-8 pm precipitation, sunshine duration, minimum relative humidity, wind speed, 8 pm temperature, and 8 pm atmospheric pressure) had a satisfactory predictive ability, with R-square of 0.897 00. The multiple stepwise regression equation was y=-18.206 64x2+3.066 16x3-3.383 90x4+1.891 53x7+1.689 86x8+25.939 46x13+1.936 35x18-2217.100 90 . R-square in prediction with neural network was 0.913 19. Neural network showed a better predictive ability than regression analysis. Conclusion The main meteorological factors closely related to mosquito density are minimum temperature, 8 pm-8 pm precipitation, sunshine duration, minimum relative humidity, wind speed, 8 pm temperature, and 8 am atmospheric pressure.

Key words: Aedes albopictus, Mosquito density, Meteorological factor

中图分类号: