Many West Nile virus (WNV) forecasting frameworks incorporate entomological or avian surveillance data, which may be unavailable in some regions. We introduce a novel data-parsimonious probabilistic model to predict both the timing of outbreak onset and the seasonal severity of WNV spillover. Our approach combines a temperature-driven compartmental model of WNV with nonparametric kernel density estimation methods to construct a joint probability density function and a Poisson rate surface as function of mosquito abundance and normalized cumulative temperature. Calibrated on human incidence records, the model produces reliable forecasts several months before the transmission season begins, supporting proactive mitigation efforts. We evaluated the framework across three counties in California (Orange, Los Angeles, and Riverside), two in Texas (Dallas and Harris), and one in Florida (Duval), representing completely different ecology and distinct climatic regimes, and observed strong agreement across multiple performance metrics.

翻译：许多西尼罗河病毒（WNV）预测框架依赖于昆虫学或鸟类监测数据，这些数据在某些地区可能无法获取。我们提出了一种新颖的数据简约概率模型，用于预测西尼罗河病毒溢出的暴发时间点和季节严重性。该方法将温度驱动的西尼罗河病毒仓室模型与非参数核密度估计方法相结合，构建了以蚊虫丰度和归一化累积温度为变量的联合概率密度函数及泊松速率曲面。基于人类发病记录校准后，该模型可在传播季节开始前数月生成可靠预测，为主动防控措施提供支持。我们在代表完全不同生态特征与气候条件的三类地区——加利福尼亚州（奥兰治县、洛杉矶县、河滨县）、德克萨斯州（达拉斯县、哈里斯县）和佛罗里达州（杜瓦尔县）——对该框架进行了评估，多个性能指标均显示模型具有高度一致性。