Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA
Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA
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DOI：
10.1089/vbz.2006.6.283
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发表时间：
2006-09-01
影响因子：
2.1
通讯作者：
Fish, Durland
中科院分区：
文献类型：
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作者：
Diuk-Wasser, Maria A.;Brown, Heidi E.;Fish, Durland
西尼罗河病毒（WNV）传播给人类的风险与特定地区受感染媒介蚊子的密度有关。目前估计病媒分布和丰度的技术主要是基于疾病控制和预防中心（CDC）的光诱收集，它只提供点数据。为了估计蚊子丰度的地区没有采样的陷阱，我们开发了逻辑回归模型的五种蚊子牵连的最有可能的载体西尼罗河病毒在康涅狄格州。利用2001年至2003年费尔菲尔德县32个圈闭的数据，建立了预测该县每30 × 30米像素丰度高低的模型。然后使用来自邻近纽黑文县16个陷阱的独立数据集对他们进行测试。从遥感数据中提取了丰度的环境预测因子。最好的预测模型包括非森林地区的尖音库蚊，地表水和距离河口的Cx。盐碱地，地表水和草地/农业的伊蚊vexans和季节差异的归一化差异植被指数和距离沼泽栖息地的黑尾蝇。未发现Cx的显著预测因素。餐馆。模型的敏感性为75%~ 87.5%，特异性为75%~ 93.8%。在纽黑文县，模型正确分类的陷阱Cx的81.3%。pipiens，Cx. salinarius、Ae. vexans和75.0%的Cs。黑尾虫。为两个县的每个物种生成了连续的栖息地适宜性表面地图，这可能有助于未来的监测和干预活动。
The risk of transmission of West Nile virus (WNV) to humans is associated with the density of infected vector mosquitoes in a given area. Current technology for estimating vector distribution and abundance is primarily based on Centers for Disease Control and Prevention (CDC) light trap collections, which provide only point data. In order to estimate mosquito abundance in areas not sampled by traps, we developed logistic regression models for five mosquito species implicated as the most likely vectors of WNV in Connecticut. Using data from 32 traps in Fairfield County from 2001 to 2003, the models were developed to predict high and low abundance for every 30 X 30 m pixel in the County. They were then tested with an independent dataset from 16 traps in adjacent New Haven County. Environmental predictors of abundance were extracted from remotely sensed data. The best predictive models included non-forested areas for Culex pipiens, surface water and distance to estuaries for Cx. salinarius, surface water and grasslands/agriculture for Aedes vexans and seasonal difference in the normalized difference vegetation index and distance to palustrine habitats for Culiseta melanura. No significant predictors were found for Cx. restuans. The sensitivity of the models ranged from 75% to 87.5% and the specificity from 75% to 93.8%. In New Haven County, the models correctly classified 81.3% of the traps for Cx. pipiens, 75.0% for Cx. salinarius, 62.5% for Ae. vexans, and 75.0% for Cs. melanura. Continuous surface maps of habitat suitability were generated for each species for both counties, which could contribute to future surveillance and intervention activities.