Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
Environ Health Perspect. 2023 Apr;131(4):47016. doi: 10.1289/EHP10986. Epub 2023 Apr 27.
West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence.
Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans.
We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of .
Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables.
We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change. https://doi.org/10.1289/EHP10986.
西尼罗河病毒（WNV）是美国蚊媒病的主要病原体。自 1999 年该疾病传入以来，许多地区的发病率已趋于稳定，这使得我们能够分析影响疾病发病率空间结构的气候条件。
我们的目标是确定影响人类 WNV 发病率空间范围和程度的季节性气候变量。
我们使用 2005 年至 2019 年美国县级病例报告和季节性平均气候变量，开发了一种当代 WNV 发病率的预测模型。我们使用随机森林模型，其样本外模型性能为 0.81。
我们的模型准确地捕捉到了从加拿大边境以南延伸到大平原中部的 V 形高 WNV 发病率区域，还捕捉到了密西西比河谷南部的中度 WNV 发病率区域。WNV 发病率最高的地区是冬季干燥寒冷、夏季潮湿温和的地区。随机森林模型将冬季平均降水量为 的县分为发病率水平比降水较多的县高 11 倍以上的两类。在气候预测因子中，冬季降水、秋季降水和冬季温度是三个最重要的预测变量。
我们考虑了气候条件可能最有利于 WNV 传播周期的哪些方面，并认为干燥寒冷的冬季是有利于蚊子传播 WNV 的最理想气候条件。我们的统计模型可能有助于预测气候变化对 WNV 风险的影响。https://doi.org/10.1289/EHP10986.
