气候因素对浦东新区蚊虫密度影响的效应分析
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摘要
目的分析上海市气象情况变化趋势以及浦东新区人工小时法监测结果的蚊虫密度消长规律;探索各种气象因素对浦东新区蚊虫密度的影响。方法收集2011年3月至2015年11月上海市的单日气象数据,整理同时期浦东新区蚊虫人工小时法监测结果。使用分布滞后非线性模型研究气象因素对蚊虫密度指数的影响效应。结果单日平均气温与蚊虫密度指数呈明显非线性关系,滞后天数与效应强度呈"U"型,在温度为32℃时,相对危险度值最高达到2.2。滞后效应在3 d左右达到最强,随后逐渐降低,在10 d后又逐渐回升。湿度的影响情况与温度类似,风速的结果与温、湿度相反。结论温度对蚊虫密度指数效应在滞后3 d时最强,湿度对蚊虫密度影响效应无明显滞后。
Objective To analyze the trend in the meteorological characteristics of Shanghai, China, and the pattern of changes in mosquito density(surveyed by the labor hour method) in Pudong New Area, Shanghai, and to explore the effect of meteorological factors on mosquito density in Pudong New Area. Methods The single-day meteorological data from March 2011 to November 2015 in Shanghai were collected; meanwhile, the mosquito surveillance data based on the labor hour method in Pudong New Area during the same period were collected. The distributed lag non-linear model was used to determine the effect of meteorological factors on mosquito density index. Results A significant non-linear relationship was found between single-day average temperature and mosquito density index. The number of lag days showed a U-shaped relationship with the effect intensity. The relative ratio value reached a maximum of 2.2 at 32 ℃. The lag effect achieved a peak within about 3 days, then gradually decreased, and gradually recovered after 10 days. The effect of humidity was similar to that of temperature. The result of wind velocity was contrary to that of temperature and humidity. Conclusion The temperature shows a maximum effect on mosquito density index after 3 lag days, and the humidity has no significant lag effect on mosquito density.
Objective To analyze the trend in the meteorological characteristics of Shanghai, China, and the pattern of changes in mosquito density(surveyed by the labor hour method) in Pudong New Area, Shanghai, and to explore the effect of meteorological factors on mosquito density in Pudong New Area. Methods The single-day meteorological data from March 2011 to November 2015 in Shanghai were collected; meanwhile, the mosquito surveillance data based on the labor hour method in Pudong New Area during the same period were collected. The distributed lag non-linear model was used to determine the effect of meteorological factors on mosquito density index. Results A significant non-linear relationship was found between single-day average temperature and mosquito density index. The number of lag days showed a U-shaped relationship with the effect intensity. The relative ratio value reached a maximum of 2.2 at 32 ℃. The lag effect achieved a peak within about 3 days, then gradually decreased, and gradually recovered after 10 days. The effect of humidity was similar to that of temperature. The result of wind velocity was contrary to that of temperature and humidity. Conclusion The temperature shows a maximum effect on mosquito density index after 3 lag days, and the humidity has no significant lag effect on mosquito density.
引文
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[3]龚震宇,龚训良. 2004-2016年美国虫媒传染病报告疫情趋势分析[J].疾病监测,2018,33(8):707-708. DOI:10.3784/j.issn.1003-9961.2018.08.021.
[4]王金娜,凌锋,郭颂,等.浙江省蚊虫密度的相关气象因素研究[J].中国媒介生物学及控制杂志,2015,26(5):464-466,470.DOI:10.11853/j.issn.1003.4692.2015.05.008.
[5] Chaves LF,Morrison AC,Kitron UD,et al. Nonlinear impacts of climatic variability on the density-dependent regulation of an insect vector of disease[J]. Global Change Biol,2012,18(2):457-468. DOI:10.1111/j.1365-2486.2011.02522.x.
[6] Jian Y,Silvestri S,Belluco E,et al. Environmental forcing and density-dependent controls of Culex pipiens abundance in a temperate climate(Northeastern Italy)[J]. Ecol Mod,2014,272:301-310. DOI:10.1016/j.ecolmodel.2013.10.019.
[7] Gasparrini A,Armstrong B,Kenward MG. Distributed lag nonlinear models[J]. Stat Med,2010,29(21):2224-2234. DOI:10.1002/sim.3940.
[8] Gasparrini A. Modeling exposure-lag-response associations with distributed lag non-linear models[J]. Stat Med,2014,33(5):881-899. DOI:10.1002/sim.5963.
[9] Gasparrini A. Distributed lag linear and non-linear models in R:the package dlnm[J]. J Stat Softw,2011,43(8):1-20.