Patterns of contact within the New Zealand poultry industry.

Members of the Poultry Industry Association and the Egg Producers Federation of New Zealand (n=420) were sent a questionnaire asking them to describe the type and frequency of on- and off-enterprise movements relating to feed, live birds and hatching eggs, table eggs and poultry product, and manure and waste litter. Social network analyses were used to describe patterns of contact among poultry enterprises and their associates for these four movement types. The response rate to the survey was 58% (244 out of 420). Network structures for enterprise-to-enterprise movements of feed, live birds and hatching eggs, and table egg and poultry product were characterised by 'hub and spoke' type structures with small-world characteristics. Small worlds were created by network hubs (e.g. feed suppliers and hatcheries) providing goods and services to larger numbers of client farms. In addition to hubs acting as the predominant source of material moving onto farms we identified enterprises acting as bridges between identified small worlds. The presence of these bridges is a concern, since their presence has the potential to facilitate the spread of hazards (e.g. feed contaminants, infectious agents carried within feed) more readily throughout the population. An ability to predict enterprises with these network characteristics on the basis of factors such as shed capacity, enterprise type, geographic location would be useful for developing risk-based approaches to disease prevention, surveillance, detection, response and control activities.

[1]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[2]  I. Onorato,et al.  A preventable outbreak of tuberculosis investigated through an intricate social network. , 2001, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[3]  B. Martínez-López,et al.  Social network analysis. Review of general concepts and use in preventive veterinary medicine. , 2009, Transboundary and emerging diseases.

[4]  R S Morris,et al.  Decision support systems for monitoring and maintaining health in food animal populations , 2007, New Zealand veterinary journal.

[5]  Jennifer E. Dent,et al.  Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic , 2008, BMC veterinary research.

[6]  R. Christley,et al.  Exploring the role of auction markets in cattle movements within Great Britain. , 2007, Preventive veterinary medicine.

[7]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[8]  B. Bollobás The evolution of random graphs , 1984 .

[9]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[10]  R. Christley,et al.  Direct and indirect contacts between cattle farms in north-west England. , 2008, Preventive veterinary medicine.

[11]  M. Bigras-Poulin,et al.  Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. , 2006, Preventive veterinary medicine.

[12]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[13]  R. Christley,et al.  Network analysis of cattle movement in Great Britain. , 2005 .

[14]  R S Morris,et al.  Social-network analysis of Mycobacterium bovis transmission among captive brushtail possums (Trichosurus vulpecula). , 2003, Preventive veterinary medicine.

[15]  P. Bearman,et al.  Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks1 , 2004, American Journal of Sociology.

[16]  Walter Willinger,et al.  Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications , 2005, Internet Math..

[17]  L. Amaral,et al.  The web of human sexual contacts , 2001, Nature.

[18]  A. Covaci,et al.  Food contamination with polychlorinated biphenyls and dioxins in Belgium. Effects on the body burden , 2002, Journal of epidemiology and community health.

[19]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[20]  Kathleen M. Carley,et al.  On the robustness of centrality measures under conditions of imperfect data , 2006, Soc. Networks.

[21]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[22]  C. Dubé,et al.  A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. , 2009, Transboundary and emerging diseases.

[23]  R. Christley,et al.  Infection in social networks: using network analysis to identify high-risk individuals. , 2005, American journal of epidemiology.

[24]  Adrian Baddeley,et al.  spatstat: An R Package for Analyzing Spatial Point Patterns , 2005 .

[25]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[26]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[27]  R L Sanson,et al.  A comparison of predictions made by three simulation models of foot-and-mouth disease , 2007, New Zealand veterinary journal.

[28]  P. Roeder,et al.  Manual on the preparation of national animal disease emergency preparedness plans , 1999 .

[29]  D. Pfeiffer,et al.  Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot and mouth disease (FMD) epidemic in the UK. , 2006, Preventive veterinary medicine.

[30]  N P French,et al.  Small-world topology of UK racing: the potential for rapid spread of infectious agents. , 2010, Equine veterinary journal.

[31]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.