Weisent J, Seaver W, Odoi A, Rohrbach B
Department of Comparative Medicine, The University of Tennessee, Knoxville, TN, USA,
Int J Biometeorol. 2014 Nov;58(9):1865-78. doi: 10.1007/s00484-014-0788-6. Epub 2014 Jan 24.
Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R(2)) was used for evaluating training (1999-2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.
弯曲杆菌感染的发病率在温带气候区呈现出强烈的季节性特征和区域差异。区域感染风险预测可为识别受温度和降水影响的传播源提供线索。本研究的目的是：（1）评估美国佐治亚州九个气候分区弯曲杆菌病风险的时间模式和差异；（2）比较分析弯曲杆菌病风险随时间变化的单变量预测模型与纳入温度和/或降水的模型；（3）研究可能是异常值的假定随机游走序列和非随机事件的替代方法。对佐治亚州弯曲杆菌病风险的时间模式进行了直观和统计评估。使用单变量和多变量预测模型预测弯曲杆菌病风险，并使用决定系数（R²）评估训练样本（1999 - 2007年）和验证样本（2008年）。研究了统计控制图和滚动验证期，以更好地理解异常值的影响并改进预测。州和分区层面的弯曲杆菌病风险呈现季节性模式，高峰出现在6月至8月之间，弯曲杆菌病风险、降水和温度之间存在显著关联。州和综合分区的预测优于单独的分区预测，包含气候变量的模型与单变量模型相当。虽然滚动验证技术没有提高预测能力，但控制图识别出了需要进一步调查的高风险时间段。这些发现对于（1）确定气候因素如何影响弯曲杆菌属的环境来源和宿主，以及（2）识别人类弯曲杆菌感染风险的区域高峰及其潜在原因具有重要意义。
