Rothamsted Research, Harpenden, UK.
Phytopathology. 2011 Oct;101(10):1184-90. doi: 10.1094/PHYTO-11-10-0331.
Information on the spatial distribution of plant disease can be utilized to implement efficient and spatially targeted disease management interventions. We present a pathogen-generic method to estimate the spatial distribution of a plant pathogen using a stochastic optimization process which is epidemiologically motivated. Based on an initial sample, the method simulates the individual spread processes of a pathogen between patches of host to generate optimized spatial distribution maps. The method was tested on data sets of Huanglongbing of citrus and was compared with a kriging method from the field of geostatistics using the well-established kappa statistic to quantify map accuracy. Our method produced accurate maps of disease distribution with kappa values as high as 0.46 and was able to outperform the kriging method across a range of sample sizes based on the kappa statistic. As expected, map accuracy improved with sample size but there was a high amount of variation between different random sample placements (i.e., the spatial distribution of samples). This highlights the importance of sample placement on the ability to estimate the spatial distribution of a plant pathogen and we thus conclude that further research into sampling design and its effect on the ability to estimate disease distribution is necessary.
有关植物病害空间分布的信息可用于实施高效且具有针对性的空间疾病管理干预措施。我们提出了一种基于随机优化过程的、针对病原体的通用方法，该过程具有流行病学依据。该方法基于初始样本，模拟病原体在宿主斑块之间的个体传播过程，从而生成优化的空间分布图。该方法在柑橘黄龙病数据集上进行了测试，并使用广为接受的kappa 统计量，与地统计学领域的克里金方法进行了比较，以量化地图精度。我们的方法生成了疾病分布的精确地图，kappa 值高达 0.46，并且基于 kappa 统计量，在一系列样本大小上均优于克里金方法。正如预期的那样，随着样本量的增加，地图的准确性也会提高，但不同随机样本位置（即样本的空间分布）之间存在大量差异。这凸显了样本位置对估计植物病原体空间分布能力的重要性，因此我们得出结论，有必要进一步研究采样设计及其对估计疾病分布能力的影响。
