Dutta Bhagat Lal, Ezanno Pauline, Vergu Elisabeta
INRA, UMR1300 Biologie, Epidémiologie et Analyse de Risque en santé animale, CS 40706, F-44307 Nantes, France; LUNAM Université, Oniris, Ecole nationale vétérinaire, agroalimentaire et de l'alimentation Nantes Atlantique, UMR BioEpAR, F-44307 Nantes, France; INRA, UR341 Unité Mathématiques et Informatique Appliquées, Domaine de Vilvert, 78352 Jouy-en-Josas cedex, France.
INRA, UMR1300 Biologie, Epidémiologie et Analyse de Risque en santé animale, CS 40706, F-44307 Nantes, France; LUNAM Université, Oniris, Ecole nationale vétérinaire, agroalimentaire et de l'alimentation Nantes Atlantique, UMR BioEpAR, F-44307 Nantes, France.
Prev Vet Med. 2014 Nov 1;117(1):79-94. doi: 10.1016/j.prevetmed.2014.09.005. Epub 2014 Sep 28.
A good knowledge of the specificities of the animal trade network is highly valuable to better control pathogen spread on a large regional to transnational scale. Because of their temporal dynamical nature, studying multi-annual datasets is particularly needed to investigate whether structural patterns are stable over the years. In this study, we analysed the French cattle movement network from 2005 to 2009 for different spatial granularities and temporal windows, with the three-fold objective of exploring temporal variations of the main network characteristics, computing proxies for pathogen spread on this network, which accounts for its time-varying properties and identifying specificities related to the main types of animals and farms (dairy versus beef). Network properties did not qualitatively vary among different temporal and spatial granularities. About 40% of the holdings and 80% of the communes were directly interconnected. The width of the aggregation time window barely impacted normalised distributions of indicators. A period of 8-16 weeks would suffice for robust estimation of their main trends, whereas longer periods would provide more details on tails. The dynamic nature of the network could be seen through the small overlap between consecutive networks with 65% of common active nodes for only 3% of common links over 2005-2009. To control pathogen spread on such a network, by reducing the largest strongly connected component by more than 80%, movements should be prevented from 1 to 5% of the holdings with the highest centrality in the previous year network. The analysis of breed-wise and herd-wise subnetworks, dairy, beef and mixed, reveals similar trends in temporal variation of average indicators and their distributions. The link-based backbones of beef subnetworks seem to be more stable over time than those of other subnetworks. At a regional scale, node reachability accounting for time-respecting paths, as proxy of epidemic burden, is greater for a dairy region than for a beef region. This highlights the importance of considering local specificities and temporal dynamics of animal trade networks when evaluating control measures of pathogen spread.
深入了解动物贸易网络的特性对于在大区域到跨国范围内更好地控制病原体传播具有极高的价值。由于其具有时间动态性,特别需要研究多年数据集,以调查结构模式多年来是否稳定。在本研究中,我们分析了2005年至2009年法国不同空间粒度和时间窗口下的牛移动网络,目的有三:探索主要网络特征的时间变化;计算该网络上病原体传播的代理指标,该指标考虑了其随时间变化的特性;识别与主要动物类型和农场(奶牛场与肉牛场)相关的特性。不同时间和空间粒度下的网络特性在性质上没有差异。约40%的养殖场和80%的市镇直接相互连接。聚合时间窗口的宽度对指标的归一化分布几乎没有影响。8至16周的时间段足以对其主要趋势进行稳健估计,而更长的时间段将提供关于尾部的更多细节。通过2005 - 2009年间连续网络之间的小重叠可以看出网络的动态性质,连续网络中只有3%的共同链接有65%的共同活跃节点。要控制病原体在这样一个网络上的传播,通过将最大强连通分量减少80%以上,应防止上一年网络中中心性最高的1%至5%的养殖场的动物移动。对按品种和畜群划分的子网(奶牛、肉牛和混合子网)的分析揭示了平均指标及其分布的时间变化的相似趋势。肉牛子网基于链接的骨干似乎比其他子网随时间更稳定。在区域尺度上,作为疫情负担代理指标的考虑时间相关路径的节点可达性,奶牛养殖区域比肉牛养殖区域更大。这突出了在评估病原体传播控制措施时考虑动物贸易网络的局部特性和时间动态的重要性。