First and second authors: BioSP, INRA, 84914, Avignon, France.
Phytopathology. 2019 Feb;109(2):265-276. doi: 10.1094/PHYTO-07-18-0237-FI. Epub 2019 Jan 8.
Recent detections of Xylella fastidiosa in Corsica Island, France, has raised concerns on its possible spread to mainland France and the rest of the Mediterranean Basin. Early detection of infected plants is paramount to prevent the spread of the bacteria, but little is known about this pathosystem in European environments, hence standard surveillance strategies may be ineffective. We present a new methodological approach for the design of risk-based surveillance strategies, adapted to the emerging risk caused by X. fastidiosa. Our proposal is based on a combination of machine learning techniques and network analysis that aims at understanding the main abiotic drivers of the infection, produce risk maps and identify lookouts for the design of future surveillance plans. The identified drivers coincide with known results in laboratory studies about the correlation between environmental variables, such as water stress and temperature, and the presence of the bacterium in plants. Furthermore, the produced risk maps overlap nicely with detected foci of infection, while they also highlight other susceptible regions where X. fastidiosa has not been found yet. We conclude the paper presenting a list of recommended regions for a risk-based surveillance campaign based on the predicted spread and probability of early detection of the disease.
最近在法国科西嘉岛发现了韧皮部坏死病菌，这引发了人们对其可能传播到法国本土和地中海盆地其他地区的担忧。早期发现感染植物对于防止细菌传播至关重要，但人们对欧洲环境中的这种病原系统知之甚少，因此标准监测策略可能无效。我们提出了一种新的基于风险的监测策略设计方法，适用于韧皮部坏死病菌带来的新出现的风险。我们的建议基于机器学习技术和网络分析的结合，旨在了解感染的主要非生物驱动因素，生成风险图，并确定未来监测计划的监测点。确定的驱动因素与实验室研究中关于环境变量（如水分胁迫和温度）与植物中细菌存在之间的相关性的已知结果相吻合。此外，生成的风险图与已检测到的感染焦点很好地重叠，同时还突出了其他尚未发现韧皮部坏死病菌的易感区域。本文最后提出了基于疾病预测传播和早期检测概率的风险监测活动的建议区域列表。
