Effects of data quality in an animal trade network and their impact on centrality parameters
Effects of data quality in an animal trade network and their impact on centrality parameters
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DOI：
10.1016/j.socnet.2018.01.001
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发表时间：
2018-07-01
期刊：
影响因子：
3.1
通讯作者：
Krieter, Joachim
中科院分区：
文献类型：
--
作者：
Buettner, Kathrin;Salau, Jennifer;Krieter, Joachim
处理动物贸易网络的分析始终面临着数据集不完善的挑战，这主要是由于国家边界或不同生产者社区造成的。在本研究中，对德国北部生产者社区的猪肉供应链进行了网络鲁棒性分析，即假阳性节点或边缘可能影响网络结构和中心性参数结果的点。对动物贸易网络的分析主要集中在疾病传播以及基于中心性参数的有针对性的预防和干预策略的制定和实施。在这里，纳入标准可能会影响疾病传播的预测以及所采取的控制措施的结果。因此，建立了四种不同的移除场景，均基于边界规范问题（根据弧的出现频率移除弧、根据其一般出现频率移除节点以及根据其作为供应商或购买者的出现频率移除节点）来分析网络鲁棒性。为了评估节点排序的变化，计算原始网络和每个删除步骤之间的 Spearman 等级相关系数 (r(s))。根据节点出现频率删除节点显示出最稳健的结果。对于至少 80% 移除弧的一小部分，r(s) 值保持在阈值 0.70 以上。对于其他移除场景，所调查的中心性参数显示了有关节点排名的各种稳健结果。因此，排除网络中不经常交易的农场不会与网络结构和中心性参数的显着变化相关。对于基于中心性参数的有针对性的疾病预防和干预策略，能够评估纳入标准对网络结构的影响，从而对可能的疾病传播的速度和程度具有重要意义。 (C) 2018 Elsevier B.V. 保留所有权利。
Dealing with the analysis of animal trade networks always faces the challenge of imperfect data sets mainly due to country borders or different producer communities. In the present study, the network robustness, i.e. the point at which false positive nodes or edges may influence the network structure and the results of the centrality parameters, were analysed for a pork supply chain of a producer community in Northern Germany. The analysis of animal trade networks mainly focusses on disease transmission and the development and implementation of targeted prevention and intervention strategies based on centrality parameters. Here, the inclusion criteria may impact the prediction of disease transmission as well as the outcome of the applied control measures. Thus, four different removal scenarios all based on the boundary specification problem (removal of arcs according to their frequency of appearance, removal of nodes according to their general frequency of appearance and according to their frequency of appearance as supplier or purchaser) were established to analyse the network robustness. In order to evaluate the changes in the rank order of the nodes a Spearman Rank Correlation Coefficient (r(s)) was calculated between the original network and each removal step. The removal of nodes according to their frequency of appearance showed the most robust results. The values of r(s) stayed above the threshold of 0.70 for at least a fraction of 80% removed arcs. For the other removal scenarios the centrality parameters under investigation showed various robust results concerning the ranking of the nodes.Therefore, the exclusion of farms that trade infrequently in the network would not be associated with significant change in network structure and centrality parameters. For targeted disease prevention and intervention strategies based on centrality parameters, it is of great relevance to be able to evaluate the influence of inclusion criteria on the network structure and thus on the speed and the extent of possible disease transmission. (C) 2018 Elsevier B.V. All rights reserved.