Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-13T09:26:32.070Z Has data issue: false hasContentIssue false
  • English
  • Français

Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Published online by Cambridge University Press:  04 January 2012

S. Rautureau ,
B. Dufour  and
B. Durand
S. Rautureau*
Affiliation:
Epidemiology unit (EPI), French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 97 700 Maisons-Alfort, France
B. Dufour
Affiliation:
Epidemiology Unit (EPIMAI), Alfort National Veterinary School (ENVA), 97 700 Maisons-Alfort, France
B. Durand
Affiliation:
Epidemiology unit (EPI), French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 97 700 Maisons-Alfort, France
*
†E-mail: [email protected]; [email protected]
Get access

Abstract

The networks generated by live animal movements are the principal vector for the propagation of infectious agents between farms, and their topology strongly affects how fast a disease may spread. The structural characteristics of networks may thus provide indicators of network vulnerability to the spread of infectious disease. This study applied social network analysis methods to describe the French swine trade network. Initial analysis involved calculating several parameters to characterize networks and then identifying high-risk subgroups of holdings for different time scales. Holding-specific centrality measurements (‘degree’, ‘betweenness’ and ‘ingoing infection chain’), which summarize the place and the role of holdings in the network, were compared according to the production type. In addition, network components and communities, areas where connectedness is particularly high and could influence the speed and the extent of a disease, were identified and analysed. Dealer holdings stood out because of their high centrality values suggesting that these holdings may control the flow of animals in part of the network. Herds with growing units had higher values for degree and betweenness centrality, representing central positions for both spreading and receiving disease, whereas herds with finishing units had higher values for in-degree and ingoing infection chain centrality values and appeared more vulnerable with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases. This reflects the dynamics of the swine trade with downward movements along the production chain. But, the significant heterogeneity of farms with several production units did not reveal any particular type of production for targeting disease surveillance or control. Besides, no giant strong connected component was observed, the network being rather organized according to communities of small or medium size (<20% of network size). Because of this fragmentation, the swine trade network appeared less structurally vulnerable than ruminant trade networks. This fragmentation is explained by the hierarchical structure, which thus limits the structural vulnerability of the global trade network. However, inside communities, the hierarchical structure of the swine production system would favour the spread of an infectious agent (especially if introduced in breeding herds).

Keywords

network analysisswinetradeFrance
Type
Full Paper
Information
animal , Volume 6 , Issue 7 , 28 May 2012 , pp. 1152 - 1162
Copyright
Copyright © The Animal Consortium 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ammendrup, S, Barcos, LO 2006. The implementation of traceability systems. Revue Scientifique et Technique 25, 763–773.CrossRefGoogle ScholarPubMed
Anonymous 2009. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Bell, DC, Atkinson, JS, Carlson, JW 1999. Centrality measures for disease transmission networks. Social Networks 21, 1–21.CrossRefGoogle Scholar
Bigras-Poulin, M, Barfod, K, Mortensen, S, Greiner, M 2007. Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. Preventive Veterinary Medicine 80, 143–165.CrossRefGoogle ScholarPubMed
Bigras-Poulin, M, Thompson, RA, Chriel, M, Mortensen, S, Greiner, M 2006. Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. Preventive Veterinary Medicine 76, 11–39.CrossRefGoogle ScholarPubMed
Borgatti, SP, Everett, MG 1997. Network analysis of 2-mode data. Social Networks 19, 243–269.CrossRefGoogle Scholar
Christley, RM, Robinson, SE, Lysons, R, French, N 2005. Network analysis of cattle movement in Great Britain. In Proceedings of Society for Veterinary Epidemiology and Preventive Medecine, Nairn, Scotland, pp. 234–243.Google Scholar
Clauset, A, Newman, ME, Moore, C 2004. Finding community structure in very large networks. Physical Review E 70, 066111.Google ScholarPubMed
Clauset, A, Shalizi, CR, Newman, MEJ 2009. Power-law distributions in empirical data. SIAM Review 51, 661–703.CrossRefGoogle Scholar
Dube, C, Ribble, C, Kelton, D, McNab, B 2009. A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transboundary and Emerging Diseases 56, 73–85.CrossRefGoogle ScholarPubMed
Fèvre, EM, Bronsvoort, BMdC, Hamilton, KA, Cleaveland, S 2006. Animal movements and the spread of infectious diseases. Trends in Microbiology 14, 125–131.CrossRefGoogle ScholarPubMed
Freeman, LC 1978/1979. Centrality in social networks, conceptual clarification. Social Networks 1, 215–239.CrossRefGoogle Scholar
Good, BH, de Montjoye, Y-A, Clauset, A 2010. Performance of modularity maximization in practical contexts. Physical Review E 81, 046106.Google ScholarPubMed
Karrer, B, Levina, E, Newman, ME 2008. Robustness of community structure in networks. Physical Review E 77, 046119.Google ScholarPubMed
Kiss, IZ, Green, DM, Kao, RR 2006a. Infectious disease control using contact tracing in random and scale-free networks. Journal of the Royal Society, Interface 3, 55–62.CrossRefGoogle ScholarPubMed
Kiss, IZ, Green, DM, Kao, RR 2006b. The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread. Journal of the Royal Society, Interface 3, 669–677.CrossRefGoogle Scholar
Lentz, HH, Konschake, M, Teske, K, Kasper, M, Rother, B, Carmanns, R, Petersen, B, Conraths, FJ, Selhorst, T 2011. Trade communities and their spatial patterns in the German pork production network. Preventive Veterinary Medicine 98, 176–181.CrossRefGoogle ScholarPubMed
Martinez-Lopez, B, Perez, AM, Sanchez-Vizcaino, JM 2009a. Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain. Preventive Veterinary Medicine 91, 29–38.CrossRefGoogle ScholarPubMed
Martinez-Lopez, B, Perez, AM, Sanchez-Vizcaino, JM 2009b. Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and Emerging Diseases 56, 109–120.CrossRefGoogle ScholarPubMed
Natale, F, Giovannini, A, Savini, L, Palma, D, Possenti, L, Fiore, G, Calistri, P 2009. Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. Preventive Veterinary Medicine 92, 341–350.CrossRefGoogle ScholarPubMed
Newman, ME 2002. Assortative mixing in networks. Physical Review Letters 89, 208701.CrossRefGoogle ScholarPubMed
Newman, ME 2004. Fast algorithm for detecting community structure in networks. Physical Review E 69, 066133.Google ScholarPubMed
Newman, ME 2005. Random graphs as models of networks. In Handbook of graphs and networks (ed. S Bornholdt and HG Schuster), pp. 35–68. Wiley-VCH Verlag GmbH & Co. KGaA, Germany.Google Scholar
Newman, ME 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the USA, 103(23), pp. 8577–8582.CrossRefGoogle Scholar
Nöremark, M, Hakansson, N, Lewerin, SS, Lindberg, A, Jonsson, A 2011. Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. Preventive Veterinary Medicine 99, 78–90.CrossRefGoogle ScholarPubMed
Pastor-Satorras, R, Vespignani, A 2001. Epidemic spreading in scale-free networks. Physical Review Letters 86, 3200–3203.CrossRefGoogle ScholarPubMed
Rautureau, S, Dufour, B, Durand, B 2011. Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005. Transboundary and Emerging Diseases 58, 110–120.CrossRefGoogle Scholar
Robinson, SE, Christley, RM 2007. Exploring the role of auction markets in cattle movements within Great Britain. Preventive Veterinary Medicine 81, 21–37.CrossRefGoogle ScholarPubMed
Volkova, VV, Howey, R, Savill, NJ, Woolhouse, ME 2010a. Sheep movement networks and the transmission of infectious diseases. PLoS One 5, e11185.CrossRefGoogle ScholarPubMed
Volkova, VV, Howey, R, Savill, NJ, Woolhouse, ME 2010b. Potential for transmission of infections in networks of cattle farms. Epidemics 2, 116–122.CrossRefGoogle ScholarPubMed
Wasserman, S, Faust, K 1994. Social network analysis: methods and applications. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Watts, DJ, Strogatz, SH 1998. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442.CrossRefGoogle ScholarPubMed