中国媒介生物学及控制杂志 ›› 2021, Vol. 32 ›› Issue (5): 604-607.DOI: 10.11853/j.issn.1003.8280.2021.05.019

• 技术与方法 • 上一篇    下一篇

基于季节性分解的圆形分布法在蚊虫监测数据统计分析中的应用

韩晓莉, 赵勇, 高文, 马丽华, 宋纪文   

  1. 河北省疾病预防控制中心有害生物防制所, 河北 石家庄 050021
  • 收稿日期:2020-12-15 出版日期:2021-10-20 发布日期:2021-10-20
  • 通讯作者: 赵勇,E-mail:532185519@qq.com
  • 作者简介:韩晓莉,女,硕士,副主任技师,主要从事病媒生物防制与抗药性监测工作,E-mail:529280408@qq.com

Application of circular distribution method based on seasonal decomposition in the statistical analysis of mosquito surveillance data

HAN Xiao-li, ZHAO Yong, GAO Wen, MA Li-hua, SONG Ji-wen   

  1. Institute for Vector Control, Hebei Center for Disease Control and Prevention, Shijiazhuang, Hebei 050021, China
  • Received:2020-12-15 Online:2021-10-20 Published:2021-10-20

摘要: 目的 采用基于季节分解的圆形分布法,分析河北省2016-2019年蚊虫分布的季节特征,探讨其应用于蚊虫监测数据统计分析的可行性。方法 使用Excel 2007和SPSS 19.0软件,采用季节分解法对河北省2016-2019年蚊虫密度的季节特征进行分解,分析河北省蚊虫密度的季节趋势,采用t检验对原始拟合蚊密度与原始蚊密度的差异进行比较;通过季节指数利用圆形分布法推测河北省蚊虫的密度高峰日和高峰期。结果 将2016-2019年河北省蚊密度进行季节分解,构建出趋势季节模型如下:Ÿt=(1.328+0.072t-0.001t2)+StŸt为某月的预测蚊密度,t为序列号,St为季节指数,F=2.679,P<0.05),通过趋势T的预测值和季节指数最终得到原始拟合的蚊密度,与原始蚊密度的走向基本一致。2016-2019年河北省蚊虫密度具有很强的季节性,且均呈现先升后降的季节趋势;蚊密度高峰日为8月2日,高峰期为6月27日至9月5日。结论 满足圆形分布法的应用条件时,基于季节分解的圆形分布法在分析蚊虫监测数据及季节特征方面具有较好的应用价值。

关键词: 季节性分解, 圆形分布法, 蚊密度

Abstract: Objective To analyze the seasonal characteristics of mosquito distribution in Hebei province, China from 2016 to 2019 using the circular distribution method based on seasonal decomposition, and to evaluate the feasibility of its application in the statistical analysis of mosquito surveillance data. Methods Excel 2007 and SPSS 19.0 softwares, as well as seasonal decomposition method, were used to decompose the seasonal characteristics of mosquito density in Hebei province from 2016 to 2019, and the seasonal trend of mosquito density was analyzed. The t-test was used to compare the original mosquito density and the original fitted mosquito density. The seasonal index and the circular distribution method were used to speculate the peak day and peak period of mosquito density in Hebei province. Results The mosquito density data in Hebei province from 2016 to 2019 were decomposed seasonally, and a season model of its trend was established as follows: Ÿt = (1.328+0.072t-0.001t2) + St (Ÿt: the predicted mosquito density in a month, t: serial number, St: seasonal index, F=2.679, P<0.05). The original fitted mosquito density obtained through the predicted value of trend T and the seasonal index had a basically consistent trend with the original mosquito density. The mosquito density data in Hebei province from 2016 to 2019 had obvious seasonality, which increased first and then decreased. The peak day of mosquito density was August 2, and the peak period was observed from June 27 to September 5. Conclusion When meeting the application conditions, the circular distribution method based on periodic decomposition has a good application value in analyzing mosquito surveillance data and seasonal characteristics.

Key words: Seasonal decomposition, Circular distribution method, Mosquito density

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