This paper presents a novel probabilistic approach for assessing the risk of West Nile Disease (WND) spillover to the human population. The assessment has been conducted under two different scenarios: (1) assessment of the onset of spillover, and (2) assessment of the severity of the epidemic after the onset of the disease. A compartmental model of differential equations is developed to describe the disease transmission mechanism, and a probability density function for pathogen spillover to humans is derived based on the model for the assessment of the risk of the spillover onset and the severity of the epidemic. The prediction strategy involves making a long-term forecast and then updating it with a short-term (lead time of two weeks or daily). The methodology is demonstrated using detailed outbreak data from high-case counties in California, including Orange County, Los Angeles County, and Kern County. The predicted results are compared with actual infection dates reported by the California Department of Public Health for 2022-2024 to assess prediction accuracy. The performance accuracy is evaluated using a logarithmic scoring system and compared with one of the most renowned predictive models to assess its effectiveness. In all prediction scenarios, the model demonstrated strong performance. Lastly, the method is applied to explore the impact of global warming on spillover risk, revealing an increasing trend in the number of high-risk days and a shift toward a greater proportion of these days over time for the onset of the disease.

翻译：本文提出了一种新颖的概率方法，用于评估西尼罗河病向人群溢出的风险。评估在两种不同情景下进行：(1) 评估溢出发生的起始点，(2) 评估疾病起始后流行病的严重程度。研究建立了一个微分方程区室模型来描述疾病传播机制，并基于该模型推导出病原体向人类溢出的概率密度函数，用于评估溢出起始风险和流行病严重程度。预测策略包括进行长期预测，然后利用短期（提前两周或每日）数据进行更新。该方法使用来自加利福尼亚州高病例县（包括橙县、洛杉矶县和克恩县）的详细疫情数据进行了验证。将预测结果与加州公共卫生部报告的2022-2024年实际感染日期进行比较，以评估预测准确性。使用对数评分系统评估性能准确性，并与最著名的预测模型之一进行比较以评估其有效性。在所有预测情景中，该模型均表现出强大的性能。最后，应用该方法探讨了全球变暖对溢出风险的影响，揭示了高风险天数呈上升趋势，并且疾病起始时这些高风险天数所占比例随时间推移而增加。