Laboratorio de Biología de Vectores, Instituto de Zoología Tropical, Facultad de Ciencias, Universidad Central de Venezuela, Avenida Los Ilustres, Los Chaguaramos, Caracas, Venezuela.
Am J Trop Med Hyg. 2010 Feb;82(2):194-201. doi: 10.4269/ajtmh.2010.09-0040.
Mosquito-borne pathogen transmission exhibits spatial-temporal variability caused by ecological interactions acting at different scales. We used local spatial statistics and geographically weighted regression (GWR) to determine the spatial pattern of malaria incidence and persistence in northeastern Venezuela. Seven to 11 hot spots of malaria transmission were detected by using local spatial statistics, although disease persistence was explained only for four of those hot spots. The GWR models greatly improved predictions of malaria risk compared with ordinary least squares (OLS) regression models. Malaria incidence was largely explained by the proximity to and number of Anopheles aquasalis habitats nearby (1-3 km), and low-elevation terrains. Disease persistence was associated with greater human population density, lower elevations, and proximity to aquatic habitats. However, there was significant local spatial variation in the relationship between malaria and environmental variables. Spatial modeling improves the understanding of the causal factors operating at several scales in the transmission of malaria.
蚊媒病原体传播表现出时空可变性，这是由不同尺度上的生态相互作用引起的。我们使用局部空间统计和地理加权回归（GWR）来确定委内瑞拉东北部疟疾发病率和持续性的空间模式。通过局部空间统计，检测到了 7 到 11 个疟疾传播热点，但只有其中 4 个热点能够解释疾病的持续性。与普通最小二乘（OLS）回归模型相比，GWR 模型大大提高了疟疾风险预测的准确性。疟疾发病率在很大程度上与附近的按蚊 Aquasalis 栖息地的接近度和数量（1-3 公里）以及低海拔地形有关。疾病的持续性与更高的人口密度、更低的海拔和靠近水生生境有关。然而，疟疾与环境变量之间的关系存在显著的局部空间变化。空间建模提高了对疟疾传播中几个尺度上因果因素的理解。
