Cardenas Nicolas C, Valencio Arthur, Sanchez Felipe, O'Hara Kathleen C, Machado Gustavo
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA.
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.
Prev Vet Med. 2024 Sep;230:106264. doi: 10.1016/j.prevetmed.2024.106264. Epub 2024 Jul 6.
Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.
识别和限制动物移动是减轻畜牧系统中不同养殖场间疾病传播的常用方法。因此,有必要揭示不同养殖场间的移动动态,包括运输距离和基于网络的控制策略。在此,我们分析了三年间不同养殖场间的生猪移动情况,其中包括197,022次独特的动物运输、3973个养殖场以及跨越美国20个州运输的391,625,374头猪。我们每隔180天构建无加权、有向的时间网络,以计算养殖场间的移动距离、连通分量大小、网络忠诚度和度分布,并基于外向接触链确定基于网络的控制行动。我们的结果表明,养殖场间生猪移动的中位数距离为74.37公里,州内和州际移动的中位数分别为52.71公里和328.76公里。平均而言,2842个养殖场通过6705条边相连,形成了一个包含91%养殖场的弱巨型连通分量。养殖场层面的网络表现出忠诚度,中位数为0.65(四分位距:0.45 - 0.77)。结果突出了针对节点以降低疾病传播风险的有效性;我们证明,针对度或介数最高的25%的农场,传播分别限制在1.23%和1.7%的养殖场。虽然没有整个美国的完整运输数据,但我们的多州移动分析证明了此类数据对于加强主动疾病控制策略的设计和实施的价值和需求。