Phytopathology. 2007 Feb;97(2):202-10. doi: 10.1094/PHYTO-97-2-0202.
ABSTRACT We first show how to estimate the exponential epidemic growth rate, r, for different combinations of three weather variables. Then we derive a method to quantify the sensitivity of r to a weather variable as a function of the pathogen life cycle variables of latent period, basic reproductive number, and the mean and standard deviation of the sporulation curve. The method can be used to identify the most important weather variable and pathogen life cycle component in terms of epidemic progress. The method is applied to yellow rust, caused by Puccinia striiformis, on winter wheat. We conclude that the most important weather variable for the progress of yellow rust is temperature, followed by dew period and light quantity. By far, the most important pathogen life cycle component is the basic reproductive number, especially at low and high temperatures. This disagrees with the general view that latent period is the most important variable at low temperatures. We discuss explanations of this.
摘要 我们首先展示如何针对三种不同的天气变量组合来估计指数型流行病增长率 r。然后，我们推导出一种方法，以量化 r 对天气变量的敏感性，该方法是作为潜伏周期、基本繁殖数以及孢子形成曲线的均值和标准差的病原体生命周期变量的函数。该方法可用于确定在流行病进展方面最重要的天气变量和病原体生命周期组成部分。该方法应用于由条形柄锈菌引起的冬小麦上的黄锈病。我们得出的结论是，对于黄锈病的进展，最重要的天气变量是温度，其次是露水期和光照量。到目前为止，最重要的病原体生命周期组成部分是基本繁殖数，尤其是在低温下。这与潜伏周期是低温下最重要变量的普遍观点相矛盾。我们讨论了对此的解释。
