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BMC veterinary research2025; 22(1); 43; doi: 10.1186/s12917-025-05248-z

Descriptive network analysis of Ontario, Canada equine competitions: implications for disease control.

Abstract: Competitions are an important source of entertainment and revenue in the horse industry but may contribute to disease introduction and spread. The objectives of this study were to, (i) describe the annual (2016 to 2018) contact networks of Equestrian Canada competitions in Ontario, Canada, and (ii) determine if the networks exhibit characteristics of 'small world' networks. Data on Equestrian Canada registered competitions in the province of Ontario, Canada between 2016 and 2018 were used to create three types of yearly contact networks: competition networks, horse networks, and venue networks. Results: Dressage, hunter/jumper, and eventing competitions were connected through horses co-attending the same competitions; however, endurance and reining shows were isolates in these networks. The median node degrees in the yearly horse networks were between 567 and 619 with wide variation in node centrality scores. Horses competing in multiple disciplines at multiple levels had high node betweenness scores. Horse networks and venue networks had similarly short geodesics as random Erdös-Renyi networks of the same size but exhibited higher levels of clustering indicating that both the horse and venue networks meet the criteria for 'small world' networks. Conclusions: The high connectivity of the networks may provide opportunities for disease transmission to occur between competition levels and disciplines, and potentially increase case counts in an epidemic. The 'small world' topography of the competition and venue networks means disease spread could occur more rapidly in this population and the threshold for disease persistence may be lower.
Publication Date: 2025-12-23 PubMed ID: 41430608PubMed Central: PMC12836759DOI: 10.1186/s12917-025-05248-zGoogle Scholar: Lookup
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  • Journal Article

Cite This Article

APA
Rossi TM, O'Sullivan TL, Greer AL. (2025). Descriptive network analysis of Ontario, Canada equine competitions: implications for disease control. BMC Vet Res, 22(1), 43. https://doi.org/10.1186/s12917-025-05248-z

Publication

ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 22
Issue: 1
Pages: 43
PII: 43

Researcher Affiliations

Rossi, Tanya M
  • Department of Population Medicine, University of Guelph, Guelph, ON, N1G 2W1, Canada.
  • Animal Health Laboratory, University of Guelph, Guelph, ON, N1G 2W1, Canada.
O'Sullivan, Terri L
  • Department of Population Medicine, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Greer, Amy L
  • Department of Population Medicine, University of Guelph, Guelph, ON, N1G 2W1, Canada. amygreer@trentu.ca.
  • Department of Biology, Trent University, Peterborough, ON, K9L 0G2, Canada. amygreer@trentu.ca.

MeSH Terms

  • Animals
  • Horses
  • Ontario / epidemiology
  • Horse Diseases / prevention & control
  • Horse Diseases / epidemiology
  • Horse Diseases / transmission
  • Sports

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: All data are publicly available and therefore informed consent was not obtained from show participants. Our study design was reviewed and approved by the University of Guelph Research Ethics Board (REB#19-09-013). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 35 references
  1. Weese JS. Infection control and biosecurity in equine disease control. 2014. 10.1111/evj.12295
    pmc: PMC7163522pubmed: 24802183
  2. Hayama Y, Kobayashi S, Nishida T, Nishiguchi A, Tsutsui T. Risk of equine infectious disease transmission by non-race horse movements in Japan. J Vet Med Sci. 2010;72:839–44. 10.1292/jvms.09-0447.
    doi: 10.1292/jvms.09-0447pubmed: 20179387google scholar: lookup
  3. Spence KL, O’Sullivan TL, Poljak Z, Greer AL. A longitudinal study describing horse demographics and movements during a competition season in Ontario, Canada. Can Vet J. 2018;59:783–90.
    pmc: PMC6005130pubmed: 30026628
  4. Moloney BJ. Overview of the epidemiology of equine influenza in the Australian outbreak. Aust Vet J. 2011;89(Suppl 1):50–6. 10.1111/j.1751-0813.2011.00748.x.
  5. Traub-Dargatz JL, Pelzel-Mccluskey AM, Creekmore LH, Geiser-Novotny S, Kasari TR, Wiedenheft AM, et al. Case-control study of a multistate equine herpesvirus myeloencephalopathy outbreak. J Vet Intern Med. 2013;27:339–46. 10.1111/jvim.12051.
    doi: 10.1111/jvim.12051pubmed: 23398291google scholar: lookup
  6. Martínez-López B, Perez AM, Sánchez-Vizcaíno JM. Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transbound Emerg Dis. 2009;56:109–20. 10.1111/j.1865-1682.2009.01073.x.
  7. Dubé C, Ribble C, Kelton D, McNab B. A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transboundary and Emerging Diseases. Transbound Emerg Dis. 2009;56:73–85. 10.1111/j.1865-1682.2008.01064.x . 
  8. Christley RM, Pinchbeck GL, Bowers RG, Clancy D, French NP, Bennett R, et al. Infection in social networks: using network analysis to identify high-risk individuals. Am J Epidemiol. 2005;162:1024–31. 10.1093/aje/kwi308.
    doi: 10.1093/aje/kwi308pubmed: 16177140google scholar: lookup
  9. Woolhouse MEJ, Dye C, Etard JF, Smith T, Charlwood JD, Garnett GP, et al. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc Natl Acad Sci USA. 1997;94:338–42. 10.1073/pnas.94.1.338.
    doi: 10.1073/pnas.94.1.338pmc: PMC19338pubmed: 8990210google scholar: lookup
  10. Frössling J, Ohlson A, Björkman C, Håkansson N, Nöremark M. Application of network analysis parameters in risk-based surveillance - Examples based on cattle trade data and bovine infections in Sweden. Prev Vet Med. 2012;105:202–8. 10.1016/j.prevetmed.2011.12.011.
  11. Marquetoux N, Stevenson MA, Wilson P, Ridler A, Heuer C. Using social network analysis to inform disease control interventions. Prev Vet Med. 2016;126:94–104. 10.1016/j.prevetmed.2016.01.022.
  12. Rosanowski SM, Cogger N, Rogers CW, Bolwell CF, Benschop J, Stevenson MA. Analysis of horse movements from non-commercial horse properties in new Zealand. N Z Vet J. 2013;61:245–53. 10.1080/00480169.2012.750571.
    doi: 10.1080/00480169.2012.750571pubmed: 23441839google scholar: lookup
  13. Spence KL, O’Sullivan TL, Poljak Z, Greer AL. Descriptive analysis of horse movement networks during the 2015 equestrian season in Ontario, Canada. PLoS ONE. 2019;14:e0219771. 10.1371/journal.pone.0219771.
  14. Christley RM, French NP. Small-world topology of UK racing: the potential for rapid spread of infectious agents. Equine Vet J. 2003;35:586–9.
    doi: 10.2746/042516403775467298pubmed: 14515959google scholar: lookup
  15. Dusan F, Toribio J-A, East IJ. Assessment of the risks of communicable disease transmission through the movement of poultry exhibited at agricultural shows in new South Wales. Aust Vet J. 2010;88:333–41. 10.1111/j.1751-0813.2010.00613.x.
  16. Webb CR. Investigating the potential spread of infectious diseases of sheep via agricultural shows in great Britain. Epidemiol Infect. 2006;134:31–40. 10.1017/S095026880500467X.
    doi: 10.1017/S095026880500467Xpmc: PMC2870361pubmed: 16409648google scholar: lookup
  17. Spence KL, O’Sullivan TL, Poljak Z, Greer AL. Descriptive and network analyses of the equine contact network at an equestrian show in Ontario, Canada and implications for disease spread. BMC Vet Res. 2017;13. 10.1186/s12917-017-1103-7.
    pmc: PMC5480143pubmed: 28637457
  18. Milwid RM, O’Sullivan TL, Poljak Z, Laskowski M, Greer AL. Comparison of the dynamic networks of four equine boarding and training facilities. Prev Vet Med. 2019;162:84–94. 10.1016/j.prevetmed.2018.11.011.
  19. Rossi TM, Milwid RM, Moore A, O’Sullivan TL, Greer A L. Descriptive network analysis of a standardbred training facility contact network: implications for disease transmission. Canadian Veterinary Journal. 2020;61:853-859.
    pmc: PMC7350062pubmed: 32741991
  20. Read JM, Keeling MJ. Disease evolution on networks: the role of contact structure. Proc Royal Soc B-Biological Sci. 2003;270:699–708. 10.1098/rspb.2002.2305.
    doi: 10.1098/rspb.2002.2305pmc: PMC1691304pubmed: 12713743google scholar: lookup
  21. Watts DJ, Strogatz SH. Collective dynamics of small-world networks. Nature. 1998;393:440–2.
    doi: 10.1038/30918pubmed: 9623998google scholar: lookup
  22. Lloyd AL, May RM. How viruses spread among computers and people. Science. 2001;292:1316–7.
    doi: 10.1126/science.1061076pubmed: 11360990google scholar: lookup
  23. Moore C, Newman MEJ. Epidemics and percolation in small-world networks. Phys Rev E. 2000;61:5678–82. 10.1103/PhysRevE.61.5678.
    doi: 10.1103/PhysRevE.61.5678pubmed: 11031626google scholar: lookup
  24. Zanette DH, Kuperman M. Effects of immunization in small-world epidemics. Physica A. 2002;309:445–52. 10.1016/S0378-4371(02)00618-0.
  25. Government of Canada. 2016 Census of Agriculture. 2016. https://www.statcan.gc.ca/en/ca2016. Accessed 28 Oct 2025.
  26. Csárdi G, Nepusz T. The Igraph software package for complex network research. InterJournal Complex Syst. 2006;1695. 10.3724/SP.J.1087.2009.02191.
  27. R Core Team. R: A language and environment for statistical computing. 2024.
  28. Newman MEJ. Fast algorithm for detecting community structure in networks. Phys Rev E. 2004;69:066133. 10.1103/PhysRevE.69.066133.
    doi: 10.1103/PhysRevE.69.066133pubmed: 15244693google scholar: lookup
  29. Hayama Y, Kobayashi S, Nishida T, Muroga N, Tsutsui T. Network simulation modeling of equine infectious anemia in the non-racehorse population in Japan. Prev Vet Med. 2012;103:38–48. 10.1016/j.prevetmed.2011.09.011.
  30. Spence KL, O’Sullivan TL, Poljak Z, Greer AL. Estimating the potential for disease spread in horses associated with an equestrian show in Ontario, Canada using an agent-based model. Prev Vet Med. 2018;151:21–8. 10.1016/j.prevetmed.2017.12.013.
  31. Slater J. Biosecurity at equestrian competitions: olympic legacy? Equine Vet J. 2013;45:396–7. 10.1111/evj.12055.
    doi: 10.1111/evj.12055pubmed: 23738876google scholar: lookup
  32. Stein RA. Super-spreaders in infectious diseases. Int J Infect Dis. 2011;15:e510–3. 10.1016/j.ijid.2010.06.020.
    doi: 10.1016/j.ijid.2010.06.020pmc: PMC7110524pubmed: 21737332google scholar: lookup
  33. Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health. 2020;8:e488–96. 10.1016/S2214-109X(20)30074-7.
  34. Guinat C, Relun A, Wall B, Morris A, Dixon L, Pfeiffer DU. Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies. Sci Rep. 2016;6:28429. 10.1038/srep28429.
    doi: 10.1038/srep28429pmc: PMC4928095pubmed: 27357836google scholar: lookup
  35. Sánchez-Matamoros a, Martínez-López B, Sánchez-Vizcaíno F, Sánchez-Vizcaíno JM, Sanchez-Matamoros A, Martinez-Lopez B, et al. Social network analysis of equidae movements and its application to Risk-Based surveillance and to control of spread of potential equidae diseases. Transbound Emerg Dis. 2013;60:448–59. 10.1111/j.1865-1682.2012.01365.x.

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