Lee Kyuyoung, Polson Dale, Lowe Erin, Main Rodger, Holtkamp Derald, Martínez-López Beatriz
Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA, USA.
Boehringer - Ingelheim Vetmedica, Inc., St. Joseph, MO, USA.
Prev Vet Med. 2017 Mar 1;138:113-123. doi: 10.1016/j.prevetmed.2017.02.001. Epub 2017 Feb 2.
The analysis of the pork value chain is becoming key to understanding the risk of infectious disease dissemination in the swine industry. In this study, we used social network analysis to characterize the swine shipment network structure and properties in a typical multisite swine production system in the US. We also aimed to evaluate the association between network properties and porcine respiratory and reproductive syndrome virus (PRRSV) transmission between production sites. We analyzed the 109,868 swine shipments transporting over 93 million swine between more than 500 production sites from 2012 to 2014. A total of 248 PRRSV positive occurrences were reported from 79 production sites during those 3 years. The temporal dynamics of swine shipments was evaluated by computing network properties in one-month and three-month networks. The association of PRRS occurrence in sow farms with centrality properties from one-month and three-month networks was assessed by using the multilevel logistic regression. All monthly networks showed a scale-free network topology with positive degree assortativity. The regression model revealed that out-degree centrality had a negative association with PRRS occurrence in sow farms in both one-month and three-month networks [OR=0.79 (95% CI, 0.63-0.99) in one-month network and 0.56 (95% CI, 0.36, 0.88) in three-month network] and in-closeness centrality model was positively associated with PRRS occurrence in sow farms in the three-month network [OR=2.45 (95% CI, 1.14-5.26)]. We also describe how the occurrence of porcine epidemic diarrheac (PED) outbreaks severely affected the network structure as well as the PRRS occurrence reports and its association with centrality measures in sow farms. The structure of the swine shipment network and the connectivity between production sites influenced on the PRRSV transmission. The use of network topology and characteristics combining with spatial analysis based on fine scale geographical location of production sites will be useful to inform the design of more cost-efficient, risk-based surveillance and control measures for PRRSV as well as other diseases in the US swine industry.
对猪肉价值链的分析正成为了解猪产业中传染病传播风险的关键。在本研究中，我们使用社会网络分析来描述美国一个典型的多地点生猪生产系统中的生猪运输网络结构和特性。我们还旨在评估网络特性与生产地点之间猪繁殖与呼吸综合征病毒（PRRSV）传播之间的关联。我们分析了2012年至2014年期间在500多个生产地点之间运输超过9300万头生猪的109,868次生猪运输。在这3年中，共报告了来自79个生产地点的248次PRRSV阳性事件。通过计算一个月和三个月网络中的网络特性来评估生猪运输的时间动态。使用多水平逻辑回归评估母猪场中PRRS发生情况与一个月和三个月网络的中心性特性之间的关联。所有月度网络均显示出具有正度相关性的无标度网络拓扑结构。回归模型显示，出度中心性在一个月和三个月网络中均与母猪场中PRRS的发生呈负相关[一个月网络中OR = 0.79（95% CI，0.63 - 0.99），三个月网络中OR = 0.56（95% CI，0.36，0.88）]，而入度中心性模型在三个月网络中与母猪场中PRRS的发生呈正相关[OR = 2.45（95% CI，1.14 - 5.26）]。我们还描述了猪流行性腹泻（PED）疫情的发生如何严重影响网络结构以及PRRS的发生报告及其与母猪场中心性测量的关联。生猪运输网络的结构以及生产地点之间的连通性影响了PRRSV的传播。结合基于生产地点精细地理位置的空间分析，利用网络拓扑结构和特征，将有助于为美国猪产业中PRRSV以及其他疾病设计更具成本效益、基于风险的监测和控制措施提供信息。
