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Effects of data quality in an animal trade network and their impact on centrality parameters

K. Büttner,J. Salau,J. Krieter

Published 2018 in Soc. Networks

ABSTRACT

Abstract 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 (rs) 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 rs 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.

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