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首页> 外文期刊>International Journal of Health Geographics >Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama
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Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama

机译：在阿拉巴马州塔斯基吉开发基于GIS的东部马脑炎媒介-宿主模型

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Background A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2? from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 μm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4? was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2?. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2?. Results For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42. Conclusion These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.
机译：背景检查了阿拉巴马州塔斯基吉附近的一个地点的东部马脑脊髓炎病毒（EEEV）的媒介宿主活动。研究地点的土地覆盖图是在ArcInfo 9.2中创建的？从QuickBird数据中获得，该数据包含2008年7月15日获得的可见和近红外（NIR）波段信息（0.45至0.72μm）。地理参考的蚊虫和鸟类采样地点以及研究地点的相关土地覆盖属性被叠加到卫星数据上。 SAS 9.1.4？用来研究单变量统计数据，并使用野外和远程采样的蚊子和鸟类数据生成回归模型。回归模型表明，库蚊和北红衣主教分别是最丰富的蚊子和鸟类。然后在ArcGIS 9.2？的Geostatistical Analyst Extension中生成了空间线性预测模型。此外，使用ArcGIS 9.2？的ArcScene扩展，基于数字高程模型（DEM）生成了研究地点的模型。结果对于总蚊虫计数数据，对半变异函数进行了一次趋势普通克里格法拟合，分阶为5.041 km，块金为6.325 km，滞后量为7.076 km，范围为31.43 km，使用了12个滞后。对于成人Cx。 erracticus计数，将一阶趋势普通克里格法拟合到半变异函数上，其中底线为5.764 km，块金为6.114 km，滞后量为7.472 km，范围为32.62 km，使用了12个滞后。对于总鸟类计数数据，将一阶趋势普通克里格法拟合到半变异函数上，底槛为4.998 km，金块为5.413 km，滞后量为7.549 km，范围为35.27 km，使用了12个滞后。对于北部红衣主教计数数据，将一阶趋势普通克里金法拟合到半变异函数上，其中底线为6.387 km，块金为5.935 km，滞后量为8.549 km，范围为41.38 km，使用了12个滞后。 DEM分析的结果表明，总采样蚊虫数据与海拔高度之间存在统计学上显着的反线性关系（R2 = -.4262; p <.0001），标准偏差（SD）为10.46，总采样鸟类数据与海拔高度（ R2 = -.5111; p <.0001），SD为22.97。 DEM统计数据还表明，总采样Cx之间存在显着的反线性关系。 ereracticus数据和海拔高度（R2 = -.4711; p <.0001），SD为11.16，总采样北部基数数据和海拔高度（R2 = -.5831; p <.0001），SD为11.42。结论这些数据表明，GIS /遥感模型和空间统计数据可以捕获野外采样的蚊子和鸟类参数之间的时空函数关系，从而确定EEEV传播的风险。

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