We used monthly precipitation and temperature data to give early warning of years with higher West Nile Virus (WNV) risk in Nebraska. We used generalized additive models with a negative binomial distribution and smoothing curves to identify combinations of extremes and timing that had the most influence, experimenting with all combinations of temperature and drought data, lagged by 12, 18, 24, 30, and 36 months. We fit models on data from 2002 through 2011, used Akaike's Information Criterion (AIC) to select the best‐fitting model, and used 2012 as out‐of‐sample data for prediction, and repeated this process for each successive year, ending with fitting models on 2002–2017 data and using 2018 for out‐of‐sample prediction. We found that warm temperatures and a dry year preceded by a wet year were the strongest predictors of cases of WNV. Our models did significantly better than random chance and better than an annual persistence naïve model at predicting which counties would have cases. Exploring different scenarios, the model predicted that without drought, there would have been 26% fewer cases of WNV in Nebraska through 2018; without warm temperatures, 29% fewer; and with neither drought nor warmth, 45% fewer. This method for assessing the influence of different combinations of extremes at different time intervals is likely applicable to diseases other than West Nile, and to other annual outcome variables such as crop yield.

中文翻译：

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利用气候解释和预测内布拉斯加州的西尼罗河病毒风险。


我们使用每月降水量和温度数据对内布拉斯加州西尼罗河病毒 (WNV) 风险较高的年份发出预警。我们使用具有负二项式分布和平滑曲线的广义加性模型来识别影响最大的极端值和时间组合，并对滞后 12、18、24、30 和 36 个月的温度和干旱数据的所有组合进行实验。我们对 2002 年至 2011 年的数据进行模型拟合，使用 Akaike 信息准则 (AIC) 选择最佳拟合模型，并使用 2012 年作为样本外数据进行预测，并连续每年重复此过程，以拟合结束基于 2002-2017 年数据建立模型，并使用 2018 年进行样本外预测。我们发现，温暖的气温和干旱年份之后的湿润年份是西尼罗河病毒病例的最强预测因素。在预测哪些县会出现病例方面，我们的模型明显优于随机概率模型，也优于年度持久性朴素模型。通过探索不同的情景，该模型预测，如果没有干旱，到 2018 年，内布拉斯加州的西尼罗河病毒病例数将减少 26%；如果没有温暖的气温，则减少 29%；在既没有干旱也没有温暖的情况下，减少了 45%。这种评估不同时间间隔的不同极端情况组合的影响的方法可能适用于西尼罗河以外的疾病，以及其他年度结果变量，例如作物产量。