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首页> 外文期刊>The American Journal of Tropical Medicine and Hygiene >Effects of Scale on Modeling West Nile Virus Disease Risk
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Effects of Scale on Modeling West Nile Virus Disease Risk

机译：规模对西尼罗河病毒疾病风险的影响

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摘要

Modeling vector-borne diseases is best conducted when heterogeneity among interacting biotic and abiotic processes is captured. However, the successful integration of these complex processes is difficult, hindered by a lack of understanding of how these relationships influence disease transmission across varying scales. West Nile virus (WNV) is the most important mosquito-borne disease in the United States. Vectored by Culex mosquitoes and maintained in the environment by avian hosts, the virus can spill over into humans and horses, sometimes causing severe neuroinvasive illness. Several modeling studies have evaluated drivers of WNV disease risk, but nearly all have done so at broad scales and have reported mixed results of the effects of common explanatory variables. As a result, fine-scale relationships with common explanatory variables, particularly climatic, socioeconomic, and human demographic, remain uncertain across varying spatial extents. Using an interdisciplinary approach and an ongoing 12-year study of the Chicago region, this study evaluated the factors explaining WNV disease risk at high spatiotemporal resolution, comparing the human WNV model and covariate performance across three increasing spatial extents: ultrafine, local, and county scales. Our results demonstrate that as spatial extent increased, model performance increased. In addition, only six of the 23 assessed covariates were included in best-fit models of at least two scales. These results suggest that the mechanisms driving WNV ecology are scale-dependent and covariate importance increases as extent decreases. These tools may be particularly helpful for public health, mosquito, and disease control personnel in predicting and preventing disease within local and fine-scale jurisdictions, before spillover occurs.
机译：当捕捉到相互作用的生物和非生物过程之间的异质性时，最好进行媒介传播疾病的建模。然而，这些复杂过程的成功整合是困难的，因为缺乏对这些关系如何影响不同规模疾病传播的理解。西尼罗河病毒（WNV）是美国最重要的蚊媒疾病。该病毒由库蚊传播，并由鸟类宿主在环境中维持，可扩散到人类和马体内，有时会导致严重的神经侵袭性疾病。一些模型研究已经评估了WNV疾病风险的驱动因素，但几乎所有的研究都是在大范围内进行的，并且报告了常见解释变量影响的混合结果。因此，在不同的空间范围内，与常见解释变量（尤其是气候、社会经济和人类人口）的精细尺度关系仍然不确定。本研究采用跨学科方法，并对芝加哥地区进行了为期12年的研究，以高时空分辨率评估了解释WNV疾病风险的因素，比较了人类WNV模型和三个不断增加的空间范围内的协变量表现：超细、地方和县尺度。我们的研究结果表明，随着空间范围的增加，模型性能也随之提高。此外，23个评估的协变量中只有6个被纳入至少两个尺度的最佳拟合模型。这些结果表明，驱动WNV生态的机制是规模依赖的，协变量的重要性随着程度的降低而增加。这些工具可能特别有助于公共卫生、蚊子和疾病控制人员在溢出发生之前预测和预防地方和小规模管辖区内的疾病。

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