FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
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.
© 2025. The Author(s).
Get Full Text
Publication Date: 2025-12-23 PubMed ID: 41430608PubMed Central: PMC12836759DOI: 10.1186/s12917-025-05248-zGoogle Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study analyzed the contact networks formed through equine competitions in Ontario, Canada, from 2016 to 2018 to understand how these interactions might facilitate the spread of diseases among horses.
  • The research also examined whether these networks displayed ‘small world’ characteristics, which would suggest faster and wider disease transmission potential.

Objectives of the Study

  • To describe annual contact networks based on equestrian competitions registered with Equestrian Canada in Ontario between 2016 and 2018.
  • To determine if the networks exhibited ‘small world’ properties that could influence disease spread dynamics.

Types of Networks Created

  • Competition networks: Connections between different types of competitions based on shared horse participation.
  • Horse networks: Networks formed by horses attending multiple competitions or venues, indicating direct or indirect contacts between animals.
  • Venue networks: Networks connecting competition locations based on horses visiting the same venues across events.

Key Findings

  • Connectivity among disciplines:
    • Dressage, hunter/jumper, and eventing competitions were interconnected through horses that attended multiple events in these disciplines.
    • Endurance and reining competitions tended to be isolated within the networks, showing less overlap with other disciplines.
  • Node degree and centrality:
    • The median number of connections per horse (node degree) ranged from 567 to 619 annually, highlighting broad connectivity.
    • Horses participating in multiple disciplines and levels had higher betweenness centrality, meaning they acted as critical bridges within the network.
  • Network topology:
    • The horse and venue networks had short average path lengths (“geodesics”) similar to random Erdös-Renyi networks, which means any two nodes were only a few connections apart.
    • Both networks showed higher clustering than random networks, meaning nodes tended to form tightly knit groups.
    • This combination of short paths and high clustering fits the definition of ‘small world’ networks.

Implications for Disease Transmission

  • The high level of connectivity suggests diseases can spread across different competition disciplines and levels quickly.
  • ‘Small world’ characteristics imply infectious agents can transmit rapidly and efficiently throughout the network, potentially leading to large outbreaks.
  • The threshold for disease persistence (i.e., the point at which an infection can sustain itself within the population) may be lowered due to these network properties.
  • Horses with high betweenness centrality could be targeted for increased surveillance or control measures because of their pivotal role in disease spread.

Conclusion

  • Equine competitions in Ontario form highly connected networks that facilitate substantial horse-to-horse contacts across multiple events and venues.
  • The ‘small world’ nature of these networks increases the risk and speed of infectious disease spread among competing horses.
  • Disease control strategies in equine populations should account for these network structures to effectively mitigate transmission during outbreaks.

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

BMC veterinary research
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.
    pmc: PMC7163522pubmed: 24802183doi: 10.1111/evj.12295google scholar: lookup
  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.
    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.
    doi: 10.1111/j.1751-0813.2011.00748.xpubmed: 21711290google scholar: lookup
  5. Traub-Dargatz JL, Pelzel-Mccluskey AM, Creekmore LH, Geiser-Novotny S, Kasari TR, Wiedenheft AM. Case-control study of a multistate equine herpesvirus myeloencephalopathy outbreak. J Vet Intern Med 2013;27:339–46.
    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.
    doi: 10.1111/j.1865-1682.2009.01073.xpubmed: 19341388google scholar: lookup
  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. Transbound Emerg Dis 2009;56:73–85.
    doi: 10.1111/j.1865-1682.2008.01064.xpubmed: 19267879google scholar: lookup
  8. Christley RM, Pinchbeck GL, Bowers RG, Clancy D, French NP, Bennett R. Infection in social networks: using network analysis to identify high-risk individuals. Am J Epidemiol 2005;162:1024–31.
    doi: 10.1093/aje/kwi308pubmed: 16177140google scholar: lookup
  9. Woolhouse MEJ, Dye C, Etard JF, Smith T, Charlwood JD, Garnett GP. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc Natl Acad Sci USA 1997;94:338–42.
    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.
    doi: 10.1016/j.prevetmed.2011.12.011pmc: PMC7114171pubmed: 22265643google scholar: lookup
  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.
    doi: 10.1016/j.prevetmed.2016.01.022pubmed: 26883965google scholar: lookup
  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.
    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.
    doi: 10.1371/journal.pone.0219771pmc: PMC6622551pubmed: 31295312google scholar: lookup
  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.
    doi: 10.1111/j.1751-0813.2010.00613.xpubmed: 20726966google scholar: lookup
  16. Webb CR. Investigating the potential spread of infectious diseases of sheep via agricultural shows in great Britain.. Epidemiol Infect 2006;134:31–40.
    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.
    pmc: PMC5480143pubmed: 28637457doi: 10.1186/s12917-017-1103-7google scholar: lookup
  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.
    doi: 10.1016/j.prevetmed.2018.11.011pubmed: 30621903google scholar: lookup
  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.
    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.
    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.
    doi: 10.1016/S0378-4371(02)00618-0google scholar: lookup
  25. Government of Canada. 2016 Census of Agriculture.. 2016.
  26. Csárdi G, Nepusz T. The Igraph software package for complex network research.. InterJournal Complex Syst 2006;1695.
    doi: 10.3724/sp.j.1087.2009.02191google scholar: lookup
  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.
    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.
    doi: 10.1016/j.prevetmed.2011.09.011pubmed: 21963256google scholar: lookup
  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.
    doi: 10.1016/j.prevetmed.2017.12.013pubmed: 29496102google scholar: lookup
  31. Slater J. Biosecurity at equestrian competitions: olympic legacy?. Equine Vet J 2013;45:396–7.
    doi: 10.1111/evj.12055pubmed: 23738876google scholar: lookup
  32. Stein RA. Super-spreaders in infectious diseases.. Int J Infect Dis 2011;15:e510–3.
    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. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.. Lancet Global Health 2020;8:e488–96.
    doi: 10.1016/S2214-109X(20)30074-7pmc: PMC7097845pubmed: 32119825google scholar: lookup
  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.
    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. 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.
    doi: 10.1111/j.1865-1682.2012.01365.xpubmed: 22830597google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,970 horse owners like you who receive our email updates on horse health and nutrition.