Department of Diagnostic Medicine and Pathobiology College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; Center for Outcomes Research and Epidemiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA.
Department of Diagnostic Medicine and Pathobiology College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; Center for Outcomes Research and Epidemiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA.
Prev Vet Med. 2021 Aug;193:105391. doi: 10.1016/j.prevetmed.2021.105391. Epub 2021 May 29.
Livestock movements are a common pathway for the spread infectious diseases in a population. An understanding of livestock movement patterns is needed to understand national transmission risks of highly infectious diseases during epidemics. Social Network Analysis (SNA) is an approach that helps to describe the relationships among individuals and the implications of those relationships. We used SNA to describe the contact structure of livestock movements throughout the contiguous U.S. from April 1, 2015 to March 31, 2016. We describe 4 network types: beef cattle, dairy cattle, swine, and small ruminant. Livestock movement data were sourced from Interstate Certificates of Veterinary Inspection (ICVI) while county-level farm demographic data were from the National Agricultural Statistics Service (NASS). In the described networks, nodes are represented by counties and arcs by shipments between nodes; the networks were weighted based on the number of shipments between nodes. For the analyses, movement data were aggregated at the county level and on an annual basis. Measures of centrality and cohesiveness were computed and identification of trade-communities in all networks was conducted. During the study period, a total of 219,042 movements were recorded and beef cattle movements accounted for 63 % of all movements. At least 70 % of U.S. counties were present in each of the networks, but the density of arcs was less than 2% in all networks. In the beef cattle network, counties with high out-degree were strongly correlated (0.8) with the number of beef cows per county while for the dairy cattle network a strong correlation (>0.86) was found with the number of dairy cattle per km at the county level. All networks were found to have between 4 and 6 large communities (50 counties or more per community), and were geographically clustered except for the communities in the small ruminant network. Outputs reported in these analyses can help to understand the structure of the contact networks for beef cattle, dairy cattle, swine, and small ruminants. They may also be used in conjunction with simulation modeling to evaluate spread of highly infectious disease such as foot-and-mouth disease at the national level and to evaluate the application of intervention strategies.
牲畜流动是传染病在人群中传播的常见途径。了解牲畜流动模式对于理解传染病在大流行期间的国家传播风险是必要的。社会网络分析（SNA）是一种有助于描述个体之间关系及其关系含义的方法。我们使用 SNA 来描述 2015 年 4 月 1 日至 2016 年 3 月 31 日期间美国各地的牲畜流动接触结构。我们描述了 4 种网络类型：肉牛、奶牛、猪和小反刍动物。牲畜流动数据来源于州际兽医检验证书（ICVI），而县级农场人口统计数据则来自国家农业统计局（NASS）。在描述的网络中，节点由县表示，弧由节点之间的运输表示；网络根据节点之间的运输数量加权。在分析中，将移动数据汇总到县级和年度基础上。计算了中心度和内聚度的度量，并对所有网络中的贸易社区进行了识别。在研究期间，共记录了 219042 次移动，其中肉牛移动占所有移动的 63%。每个网络中至少有 70%的美国县存在，但所有网络中的弧密度均低于 2%。在肉牛网络中，具有高出度的县与每县的肉牛数量呈强相关（0.8），而在奶牛网络中，与县级每公里的奶牛数量呈强相关（>0.86）。所有网络都被发现有 4 到 6 个大社区（每个社区 50 个县或更多），并且除了小反刍动物网络中的社区外，这些社区都是地理上聚类的。这些分析中报告的结果可以帮助理解肉牛、奶牛、猪和小反刍动物的接触网络结构。它们也可以与模拟模型一起使用，以评估如口蹄疫等高度传染性疾病在国家层面的传播，并评估干预策略的应用。
