Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
Department of Statistics, Oregon State University, Corvallis, OR, United States.
PeerJ. 2024 Jul 22;12:e17649. doi: 10.7717/peerj.17649. eCollection 2024.
Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread.
Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years.
Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model.
Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States.
监测对于快速实施由空气传播植物病原体引起的疾病的控制措施至关重要，但此类计划可能需要大量资源，特别是对于由远距离传播病原体引起的流行病。目前，美国用于监测、预测和传播疾病传播风险的葫芦科霜霉病平台维护成本高昂。在本研究中，我们专注于识别监测和治疗的关键地点，试图降低疾病监测成本，并确定可以在哪里应用控制措施来减轻疾病传播的风险。
基于田间之间的距离构建静态网络，而基于田间之间的距离和风速及风向构建动态网络，使用 2008 年至 2016 年收集的疾病数据。采用三种策略来识别高连通性的田间地点。首先，在一个流行年份的离散每周时间步长内，对节点之间病原体传播的概率和节点感染的概率进行建模。其次，从网络中选择性地删除被识别为重要的节点，并在每个流行年份重新计算节点感染的概率。第三，分析流行年份中节点感染的重复模式。
静态网络表现出无标度特性，节点度遵循幂律分布。介数中心度是识别网络中与疾病传播和预测相关的重要节点的最有用指标。基于介数中心度，马里兰州、北卡罗来纳州、俄亥俄州、南卡罗来纳州和弗吉尼亚州的田间地点在整个流行年份的疾病网络中最为中心。删除被识别为重要的田间地点会限制基于动态网络模型预测的疾病传播风险。
结合动态网络模型和中心度指标，可以确定美国东南部和大西洋中部地区高度连通的田间地点。这些高度连通的地点可用于为美国东部的监测和控制葫芦科霜霉病的策略提供信息。
