Mosquito‐borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito‐borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait‐based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing‐based approach to mapping temperature‐dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature‐dependent traits and field‐based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6–25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait‐based mechanistic models. This study represents one of the highest resolution validations of trait‐based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito‐borne disease risk.

中文翻译：

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对温度依赖性蚊子和病毒特征的遥感可预测基于现场监测的疾病风险


蚊媒疾病对全球疾病负担造成了重大影响，并且受到环境条件的强烈影响。持续和快速的环境变化需要更好地了解蚊媒疾病对温度等环境因素的反应，以及绘制和监测风险的新方法。最近开发的基于性状的机理模型提高了对传输温度依赖性的理解，但模型预测在现场验证仍然具有挑战性。以西尼罗河病毒 （WNV） 为案例研究，我们说明了使用一种基于遥感的新型方法在高空间分辨率和整个昼夜周期中绘制与温度相关的蚊子和病毒特征。我们使用蚊子和 WNV 监测数据验证了该方法，这些数据控制了 WNV 生态学中的其他关键因素，发现温度依赖性状和基于现场的风险指标之间有很强的一致性。此外，我们发现蚊子的 WNV 感染率与温度呈单峰关系，峰值为 ~24.6–25.2°C，处于基于特征的机制模型预测的 WNV 传播最佳温度的 95% 可信区间的中间。这项研究代表了基于特征的模型预测的最高分辨率验证之一，并说明了一种新颖的遥感方法在预测蚊媒疾病风险方面的效用。