Alonso Chavez Vasthi, Parnell Stephen, VAN DEN Bosch Frank
Department of Computational and Systems Biology, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Department of Computational and Systems Biology, Rothamsted Research, Harpenden, AL5 2JQ, UK; University of Salford, School of Environment and Life Sciences, Manchester, M5 4WT, UK.
J Theor Biol. 2016 Oct 21;407:290-302. doi: 10.1016/j.jtbi.2016.07.041. Epub 2016 Jul 28.
The global increase in the movement of plant products in recent years has triggered an increase in the number of introduced plant pathogens. Plant nurseries importing material from abroad may play an important role in the introduction and spread of diseases such as ash dieback and sudden oak death which are thought to have been introduced through trade. The economic, environmental and social costs associated with the spread of invasive pathogens become considerably larger as the incidence of the pathogen increases. To control the movement of pathogens across the plant trade network it is crucial to develop monitoring programmes at key points of the network such as plant nurseries. By detecting the introduction of invasive pathogens at low incidence, the control and eradication of an epidemic is more likely to be successful. Equally, knowing the likelihood of having sold infected plants once a disease has been detected in a nursery can help designing tracing plans to control the onward spread of the disease. Here, we develop an epidemiological model to detect and track the movement of an invasive plant pathogen into and from a plant nursery. Using statistical methods, we predict the epidemic incidence given that a detection of the pathogen has occurred for the first time, considering that the epidemic has an asymptomatic period between infection and symptom development. Equally, we calculate the probability of having sold at least one infected plant during the period previous to the first disease detection. This analysis can aid stakeholder decisions to determine, when the pathogen is first discovered in a nursery, the need of tracking the disease to other points in the plant trade network in order to control the epidemic. We apply our method to high profile recent introductions including ash dieback and sudden oak death in the UK and citrus canker and Huanglongbing disease in Florida. These results provide new insight for the design of monitoring strategies at key points of the trade network.
近年来，全球植物产品流动的增加引发了外来植物病原体数量的上升。从国外进口材料的植物苗圃可能在诸如槭树枯梢病和橡树猝死病等疾病的传入和传播中发挥重要作用，这些疾病被认为是通过贸易传入的。随着入侵性病原体传播范围的扩大，与之相关的经济、环境和社会成本会大幅增加。为了控制病原体在植物贸易网络中的流动，在网络的关键点（如植物苗圃）开展监测计划至关重要。通过在低发病率时检测到入侵性病原体的传入，疫情的控制和根除更有可能成功。同样，一旦在苗圃中检测到疾病，了解售出感染植物的可能性有助于制定追踪计划，以控制疾病的进一步传播。在此，我们开发了一种流行病学模型，用于检测和追踪入侵性植物病原体进出植物苗圃的流动情况。我们使用统计方法，在首次检测到病原体的情况下预测疫情发病率，同时考虑到疫情在感染和症状出现之间存在无症状期。同样，我们计算在首次疾病检测之前的时间段内售出至少一株感染植物的概率。这种分析有助于利益相关者做出决策，即在苗圃中首次发现病原体时，确定是否需要将疾病追踪到植物贸易网络中的其他点，以控制疫情。我们将我们的方法应用于近期备受瞩目的疾病传入案例，包括英国的槭树枯梢病和橡树猝死病，以及佛罗里达州的柑橘溃疡病和黄龙病。这些结果为贸易网络关键点监测策略的设计提供了新的见解。
