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

Descriptive network analysis of Ontario, Canada equine competitions: Implications for disease control | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Descriptive network analysis of Ontario, Canada equine competitions: Implications for disease control Tanya M Rossi, Terri L O’Sullivan, Amy L Greer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6804927/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in BMC Veterinary Research → Version 1 posted 10 You are reading this latest preprint version Abstract Background 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 2016–2018 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 2016–2018 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. equine movements network analysis biosecurity disease prevention and control equine competitions Canada Figures Figure 1 Figure 2 Figure 3 Introduction Competitive events are an important part of horse ownership for many people in the equine industry. In addition to providing entertainment for horse enthusiasts, these competitions often serve as a way of judging equine athleticism, conformation, and breeding potential. While generally benefiting the industry they also introduce the potential for horses to acquire and transmit infectious diseases [ 1 , 2 ]. Performance horses make frequent return trips from their home stables to competitions where they mix with animals travelling regionally, nationally or internationally [ 3 ]. These trips may increase the magnitude of an epidemic as well as widening its spatial spread. Examples of outbreaks associated with horse movements include the 2007 equine influenza epidemic in Australia [ 4 ], and the 2011 equine herpes virus outbreak in Utah [ 5 ]. Lack of knowledge on how horses are connected through competitions presents a challenge for contact tracing and understanding of how diseases may spread through the equine industry. Network analysis can be used to describe contact patterns between individuals in a group and is an important tool to evaluate how pathogens could be propagated and maintained in populations [ 6 , 7 ]. In addition, factors associated with an individual’s risk of acquiring or spreading infections can be identified and aid in the development of targeted disease control programs, thereby increasing the effectiveness of interventions [ 6 , 8 – 11 ]. Network analysis has been applied in veterinary equine research to describe the movement of animals between competitions or farms, and within facilities. For example, several studies have used owner surveys to described horse movements between non-racing facilities in New Zealand, Canada, and Japan, and their potential implications for disease transmission [ 2 , 12 , 13 ]. Network analyses of agricultural competitions and racing events have investigated the possible risk and contribution of these events to the incidence and magnitude of outbreaks, and to their geographical spread [ 14 – 17 ]. Spence et al (2017) found that a single Ontario dressage show resulted in a contact network of 41 equine facilities and 779 horses including just primary and secondary contacts [ 17 ]. Contact patterns within equine facilities were investigated in two studies using radio-frequency identification tags and these data were used to inform mathematical models [ 18 ] and to describe implications for biosecurity and disease control [ 19 ]. Network topography can have significant implications for disease dynamics, including the maximum expected size of an outbreak, speed of transmission, and probability of disease extinction in a population [ 8 , 20 – 22 ]. The concept of a ‘small world network’ was introduced by Watts and Strogatz (1998) to describe networks which have similar pathlengths (degrees of separation) between individuals as those that are observed in a random network, but greater clustering [ 21 ]. Network models simulating the introduction of infectious diseases have demonstrated that transmission occurs more rapidly through networks with ‘small world’ characteristics compared to less complex or random networks [ 14 , 22 , 23 ]. In addition, diseases are more likely to become endemic rather than die out [ 14 ]. These factors have important implications for disease control, including appropriate design of vaccination programs and emergency preparedness protocols [ 22 , 24 ]. In Canada horses are classed as livestock; however, currently owners are not required to register their horses or report movements on or off property. This represents a challenge for describing horse contact networks and precludes estimation of the size of the population at risk. Currently, a description of the Ontario horse population is limited to the Agricultural Census conducted at 5-year intervals [ 25 ], and to survey-based studies using convenience samples [ 26 ]. Equestrian Canada (EC) is the national governing body for equestrian sports in Canada, excluding racing, and regulates many equine events, including Féderation Equestre Internationale (FEI) competitions. The five major disciplines regulated by EC are dressage, eventing, hunter/jumper, endurance, and reining. Beginning in 2012, competition organizers were required to register their events with EC and provide information on the event date, location, discipline, and level. As of 2016, organizers were also required to submit competition results for each class, creating a database of horses participating in each show. Networks of horses, competitions, and venues can be created using these data. The objectives of this study were: i) To describe the 2016–2018 contact networks of Ontario, Canada Equestrian Canada competitions, competing horses, and venues, and ii) to determine if the networks of show venues or competing horses met the criteria for ‘small world networks’. Materials and Methods Data were provided by EC including the registry of all EC sanctioned equine competitions and individual horse results for each show held between 2016 to 2018 in the province of Ontario, Canada. Horses participating in these shows were required to register with EC and obtain a passport number. 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). Study Population The study population consisted of all horses with an EC passport number competing in sanctioned Ontario competitions between 2016-2018. EC regulates hunter/jumper, endurance, dressage, eventing and reining shows occurring at the bronze, silver, gold or platinum levels in Canada. Horse attendance at non-sanctioned shows such as private schooling shows or country fairs were not included in the EC data set and therefore were not included. Network analysis This study used common network terminology, with ‘nodes’ denoting individuals within a network and ‘edges’ denoting the connections between nodes [7]. Networks were described as directed when the relationship between nodes was directional. For example, when horses moved from one competition venue to another. If the relationship was non-directional the network was described as ‘undirected’. For example, when two horses come in contact. The influence of individual nodes on the network was assessed by measures of centrality such as: degree, defined as the number of connections for a given node; betweenness, defined as the frequency a node is on the shortest path between two nodes in the network; and eigenvector centrality, which “measures the importance of a node in the network by assigning its score relative to its connections to others, so that high-scoring neighbours of a node will contribute more to its individual score” [3, 7]. In directed networks, degree was divided into ‘in-degree’, the number of connections where a given node was the destination, and ‘out-degree’, the number of connections where a given node was the source. A node with no associated edges was considered a ‘isolate’. Network density, “the proportion of connections among nodes in the network relative to the total number of possible connections”, was calculated to measure the connectivity of the networks [7]. The level of cohesion in the network was determined by calculating the clustering coefficient ,the proportion of connected nodes who are also connected to one another [7]. Modularity is an additional measure of network cohesion and was calculated using the ‘cluster fast-greedy’ function in igraph which identifies node clusters within the network by adding or removing nodes from a potential cluster to optimise the modularity score or edge density [27]. Important measures of pathlength, defined as the number of edges between two non-adjacent nodes, were calculated, including: mean geodesic, the shortest path between two nodes, and network diameter, the longest geodesic in the network [7]. Networks were created using the igraph package in R Statistical Software (https://www.r-project.org) [28]. Competition networks Directed, 1-mode networks were generated with competitions as nodes and co-attendance of at least one horse between shows as edges for the 2016, 2017, and 2018 competition seasons. Edges were directed by show date to create a temporal sequence of competitions. Edges were weighted by the number of horses co-attending both shows. Network and node metrics calculated for these networks included: density, diameter, modularity, and edge weight, node degree, and betweenness. The calculation of other metrics, such as the clustering coefficient was precluded due to the directed, acyclic structure of the competition networks. Discipline and level specific competition networks The annual competition networks were split into discipline-specific and level-specific subgraphs where only edges between nodes of the same discipline or level were included in the network. In-degree and out-degree were normalized to facilitate comparison between networks, by dividing the degree by ( n – 1), where n is the number of nodes in the network. Density, edge weight and modularity were also compared between subgraphs to investigate the level of connectivity and cohesion among competitions of the same discipline or level. Horse networks Undirected 1-mode networks were created with competing horses as nodes and co-attendance at one or more competitions as edges for the 2016, 2017, and 2018 competition seasons. Edges were weighted by the number of co-attended competitions. Measures of network level connectivity, including network density, diameter, and median geodesic were calculated for the networks. Network cohesion was evaluated by modularity, and clustering coefficient. Node centrality measures included degree, betweenness and eigenvector centrality. Patterns of participation in competitions were described for horses with the highest centrality measures. Venue networks Directed, 1-mode networks were generated with show venues as nodes and co-attendance of at least one horse between venues as edges for the 2016, 2017, and 2018 competition seasons. Self-loops, repeated trips for a given horse to the same venue, were included in the networks. Edges were directed by the direction of horse movements between venues. Two additional measures of network connectivity were calculated for the venue networks, the giant strong and giant weak components. The giant strong component (GSC) is the largest group of venues in the network where all properties can be reached from every other property through directed pathways [7]. The giant weak component (GWC) is the largest group of venues in the network where all properties can be reached from every other property through pathways if edge direction is ignored, [7]. Other network and node metrics calculated for these networks included: density, diameter, modularity, clustering coefficient, median geodesic, node degree, and betweenness. The annual venue networks were split into monthly networks to describe the differences in network density, median geodesic, GSC, and GWC throughout the competition year. All nodes included in the annual networks were retained in the monthly networks and isolates were not removed. Small world networks The median geodesic lengths and clustering coefficients of the horse and annual venue networks were compared with those of 100 randomly generated Erdös-Renyi networks to evaluate if the EC networks met the criteria of ‘small world’ networks. The random networks had the same number of nodes and edges as the observed networks but the possibility of an edge between two nodes occurred with a set constant probability. Random graphs were generated using the ‘erdos.renyi.game’ function in the R igraph package [28]. Results Data cleaning and descriptive statistics Data on registered shows and class results for the 2016-2018 show seasons were cleaned as outlined in the supplementary material (Fig. S1). Entry data were missing for approximately 25% of competitions in each year which resulted in the exclusion of those shows from the networks. No evident pattern in competition level or discipline was found for the missing entry data. Shows hosting classes for multiple levels were registered with EC in two ways: i) as a single competition with multiple levels, or ii) as separate shows of a single level occurring at the same venue on the same date, which resulted in double counting of some competitions. In the latter case, double counted competitions were merged into a single show with multiple levels, as the researchers were interested in the potential for horse-to-horse contact while at the same venue. Descriptive statistics on the number of yearly competitions, competing horses, and venues, and the disciplines and levels of registered shows are shown in Table 1. The number of yearly competitions and venues increased between 2016-2018 from 153 to 160, however, the number of competing horses decreased slightly from 4096 horses in 2016 to 3940 horses in 2018. The most common competition discipline was hunter/jumper, and the most common level was silver. Competition network Network and node metrics for the 2016-2018 competition networks are displayed in Table 2 and an example of the 2017 competition network is shown in Figure 1. The endurance events in the 2016-2018 competition networks, and two dressage shows, one silver and one gold level, in the 2018 network were isolates. The two reining shows in 2017 formed an isolated dyad in the network with no connections to competitions in other disciplines. Regardless of level, all hunter/jumper, dressage, and eventing competitions were connected in the 2016-2017 networks and all, except the 2 dressage isolates were connected in 2018. As isolates were rare and mostly confined to endurance and reining nodes, they were removed from the competition networks before analysis. Clustering by discipline and level was observed using the fast-greedy community technique. Discipline and level specific competition networks Level and discipline specific network and node metrics for 2018 are shown in Tables 3 and 4, respectively (For 2016-2017 please see Tables S1-S4 in the supplementary material). The platinum level competition networks had the highest densities, edge weights, and median normalized out and in-degrees compared to other level specific subnetworks. Similarity among the discipline specific subnetworks, density and median normalized in and out-degrees, and edge weights were highest in the eventing networks compared to other disciplines. Horse network All horses competing in the 2016-2018 EC competition seasons were connected in the networks, apart from horses competing in the endurance event or reining competitions. Network and node metrics for the horse networks are shown in Table 5. Node degrees suggest horses potentially came in contact with a median of 567-619 other horses during the 2016-2018 EC competition seasons. Wide variation in node degree was observed, with some horses in 2017 potentially encountering 2471 other horses. Distribution of all node centrality measures for the horse networks were right skewed with a long right tail suggesting a small number of competing horses had a large influence on the observed network structure. Horses with the highest node degrees were horses competing at both the gold and silver level in a single show season, and horses who showed in hunter/jumper competitions. Betweenness scores were highest in horses competing at multiple levels and in multiple disciplines during the competition season. Similar to node degree, horses who showed in both silver and gold hunter\jumper competitions had the highest eigenvector centrality scores. Venue networks Except for the endurance venue, all properties were connected in the 2016-2018 venue networks. These included venues hosting reining shows in 2017 (Figure 2). The Giant strong components were large and approached the size of the entire yearly venue networks in 2016-2018. Network and node metrics for the yearly networks are shown in Table 6. Node indegree and outdegree distributions for all yearly venue networks were right skewed with a small proportion of properties forming a high number of connections to other venues. Splitting the yearly networks by month revealed that network density, GSC, GWC, and median geodesic were highest during the summer months, May to August (Supplementary material, Table S5). Network metrics for the 2016 and 2017 monthly venue network were similar, however, there was a small decrease in density, and GSC in 2018 (Figure 3). Small world networks Comparisons of median geodesic length, and clustering coefficients between random Erdös-Renyi networks and the observed 2016-2018 horse networks are shown in Table 7. Median geodesic length was similar between random and observed graphs for all years. The clustering coefficient was higher in the observed graphs indicating a greater level of clustering compared to random networks. These findings suggest the EC 2016-2018 horse networks are ‘small world’ networks. Yearly venue networks had a similarly low median geodesic lengths to those of random graphs but larger clustering coefficients (Table 8). Therefore, observed venue networks for 2016-2018 also met the criteria for ‘small world’ networks. Discussion In this study we have described the contact networks of EC competitions, horses, and venues in Ontario, Canada. Except for horses competing in endurance competitions, all horses in our networks had the potential for contact, either directly through mutual show attendance, or indirectly through use of the same competition venues. This interconnectivity provides potential opportunities for disease transmission and spread to occur throughout the EC equine population unless timely interventions or biosecurity protocols are employed. The role of competitions in pathogen dissemination has previously been explored in the equine industry. A study describing off-property movements of non-race horses in Japan suggested the risk of infectious disease transmission was higher in horses attending competitions, as they were responsible for the majority of horse movements [ 2 ]. Mathematical simulations of equine influenza spread through this Japanese network found greater disease dissemination both within and between other equine sectors by horses in the performance/competition sector [ 29 ]. In Ontario, a survey of owner-reported horse movements found that the most common reason for off-property horse movement was to attend competitions and that 97% of movements ended with horses returning to their home barns [ 26 ]. These return trips may provide a route for disease spread both within and between home facilities and may contribute to the magnitude of an outbreak by exposing horses that did not attend competitions [ 30 ]. In addition to high interconnectivity, the EC competition networks were highly clustered by discipline and level. Evaluation of discipline and level-specific subnetworks found variation in node and network metrics between disciplines, and levels. For example, the eventing subnetworks had the highest densities, degrees, and edge weights of the discipline-specific networks, suggesting that competing in eventing shows may carry a higher risk of pathogen transmission than other disciplines. Similarly, the platinum level subnetworks also had the highest densities, degrees, and edge weights of the level-specific subnetworks and may also represent a vulnerable population. However, industry practices may already help mitigate these risks. In Ontario, eventing shows are typical held outside with horses being shipped in daily rather than stabling on site. In addition, horses usually share little to no ring time due to the nature of the events. These behaviors result in fewer opportunities for close contact between horses and minimize opportunities for aerosol and/or droplet transmission or sharing of equipment and air space. Platinum level shows are often international competitions with horses travelling from foreign countries to participate; as a result these competitions are subject to more strict biosecurity guidelines designed to reduce risk of disease transmission [ 31 ]. Examination of the horse networks found that horses have the potential to contact a median of 567–619 other horses per year at EC competitions. For some horses, this number was over 2000 horse contacts. However, these numbers may not reflect the true number of contacts at the individual level. Owner compliance with biosecurity guidelines likely reduces horse to horse contact by limiting physical proximity between horses, practicing appropriate infection control, and not sharing equipment. In addition, factors associated with the competition type and location can impact horse mixing patterns, resulting in greater or fewer opportunities for contact. Our networks only described contact between horses at EC competitions, and did not account for horse contacts at home barns. A study describing the contact network associated with a single Ontario silver-level dressage show found that 710 secondary contacts were identified from 69 horses attending the show, suggesting competition level networks may underestimate the total number of contacts with other horses [ 17 ]. In addition, although EC is the federal governing body for equine sports, horses may participate in non-sanctioned competitions, training clinics, trail rides, and other group events that may increase their contact with other horses [ 13 ]. The distribution of node centrality metrics in the horse networks indicated that a small proportion of individuals had a high influence on network structure and therefore, could play an important role in disease transmission through this population. Previous simulation studies have found that individuals with high network centrality scores have a higher risk of being infected, a shorter time to infection during epidemics, and infect a disproportionately large number of secondary contacts [ 8 , 11 , 32 ]. In the EC network, horses competing at both the gold and silver levels, particularly in the hunter/jumper discipline had the highest node degrees and eigenvector centrality scores suggesting these horses may be more susceptible to acquiring and spreading infectious disease. High betweenness scores were found in horses competing in multiple disciplines and levels during a show season. These horses could act as ‘bridges’ connecting otherwise sparsely connected horse clusters, providing opportunities for disease transmission between horses participating in different levels and disciplines [ 11 ]. Previous livestock studies have demonstrated that targeting these highly influential individuals may increase the effectiveness of disease surveillance, control and prevention programs [ 10 , 11 , 33 – 35 ]. Analysis of competition venue networks revealed that, except the endurance venue, all EC venues were connected through horses attending sanctioned shows at the same properties. In contrast to the competition networks, reining shows in 2017 were linked to other EC disciplines by use of common host venues. These findings suggest opportunities for indirect disease transmission, such as contact with improperly stored waste, contaminated surfaces, or staff, may facilitate disease spread between reining horses and horses competing in other disciplines [ 36 ]. Due to the high connectivity of these networks the GSCs included almost all venues in the yearly venue networks suggesting pathogens could spread to the majority of venues through directed edges. These findings have important implications for disease dynamics as GSC size represents the lower bound of predicted epidemic size [ 37 , 38 ]. However, calculation of GSC in static networks assumes edges connecting nodes are constant, which rarely occurs in real world networks [ 38 ]. Our venue networks depicted horse movements between properties over the nine-month competition season; these movements happen infrequently and over short time periods, therefore the size of GSCs in these networks were likely overestimated. To increase accuracy of GSC estimates, and to compare GSC sizes and median node degrees over the competition season, the yearly venue networks were partitioned into monthly networks. GSC size decreased in these networks compared with the yearly networks, and the highest estimates occurred between May-August. These findings, and the comparison of median node degree between months, suggest the risk of disease transmission and the size of a potential epidemic would be highest during the summer months. Our results are in agreement with a study by Spence et al , 2018, which found horse movements between properties, including those hosting competitions, were highest in May and August [ 3 ]. The distributions of indegree and outdegree for the venue networks were right skewed with a long right tail, suggesting a small number of popular venues had a high number of connections with other properties in the network. These findings are expected as, typically, a few large commercial horse venues host the majority of competitions during the EC show season in Ontario, while a higher number of small, private venues host one or two shows a year. The creation and adherence to strict biosecurity protocols are therefore, especially important at these large venues to prevent disease introduction and spread through a large proportion of the venue network [ 39 ]. Networks characterised by short pathlengths between nodes and high levels of clustering are termed ‘small world networks’ and have important implications for disease spread [ 21 , 22 ]. For example, mathematical models of disease transmission have found disease propagation occurs more rapidly in these networks than in random networks, suggesting a shorter window for intervention in the face of an outbreak [ 14 , 22 ]. When median geodesics and clustering coefficients were compared with random networks, both the horse and the yearly venue networks met the criteria for small world networks. Horses and venues not directly connected in the networks were separated by a median of only 2 edges (1 horse or property) suggesting the transmission chain between individuals not in direct contact is short. These findings highlight the need for outbreak preparedness and response plans to be established well in advance of competitions to rapidly prevent disease spread should an outbreak be identified. Limitations, benefits, and future work The EC competition data provided information on the majority of competition attendance and horse movements between shows, permitting the creation of contact networks. However, a proportion (~ 25%) of show results were missing from the dataset to due to missing report data. As a result, competitions without these data were excluded from the networks as horse attendance could not be ascertained. In addition, this study described competition contact networks and did not include information on horse contacts at home barns or other locations. Therefore, the networks in these studies likely underestimate the size of the susceptible population, and node degrees for some individuals may be higher than reported here. However, Dawson et al 2015 demonstrated that even incomplete networks can provide useful information for helping design disease control initiatives and informing models of disease transmission [ 40 ]. In this study, co-attendance of horses at competitions was used as a proxy for direct or indirect contact as actual contact patterns of horses at competitions were not observed. Therefore, network connections in this study should be viewed as ‘potential’ contacts and may not reflect true contact rates. Future work directly observing contacts between horses, people, and common surfaces at equine competitions should be performed to better gain insights into contact patterns while at the venue. Despite these limitations our network analyses provide valuable information which could be used to inform simulations of infectious disease introduction events at EC competitions in Ontario and may also aid in identifying highly influential individuals or settings that should be targeted for disease surveillance and control programs. 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 Availability of data and materials The datasets used and/or analysed during the current study are comprised of publicly available data (sport licence number, and competition results) that can be extracted from the Equestrian Canada website (www.equestrian.ca). Competing interests The authors declare that they have no competing interests Funding This work was supported by funding from Agriculture Canada (Agri-risk Program). Authors' contributions TR analyzed and interpreted the Equestrian Canada (EC) dataset and prepared the written manuscript for submission. TOS was involved in the conceptualization and design of the study and provided feedback on multiple manuscript revisions. ALG was involved in the conceptualization and design of the study, obtaining funding to support the study, data extraction, decision to publish, and provided feedback on multiple manuscript revisions. All authors read and approved the final manuscript Acknowledgements The authors would like to thank Equestrian Canada for providing the data for this project and for their insights on the equine industry in Ontario. References Weese JS (2014) Infection control and biosecurity in equine disease control. Equine Veterinary Journal. 46:654–60. 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Tables Table 1: Descriptive statistics for registered Equestrian Canada competitions held in Ontario from 2016-2018. 2016 2017 2018 Shows (n) 153 155 160 Venues (n) 58 56 63 Unique horse IDs 4096 4036 3940 Level: * Bronze Silver Gold Platinum 69 (45.1%) 114 (74.5%) 61 (39.9%) 19 (12.4%) 70 (40.6%) 109 (74.8%) 58 (41.3%) 16 (9.0%) 63 (39.4%) 116 (72.5%) 64 (40.0%) 14 (8.8%) Mixed level shows 87 (56.9%) 80 (51.6%) 78 (48.8%) Discipline: Hunter/Jumper Dressage Eventing Endurance Reining 87 (56.9%) 45 (29.4%) 20 (13.1%) 1 (0.6%) 0 (0.0%) 84 (54.2%) 45 (29.0%) 23 (10.7%) 1 (0.6%) 2 (1.3%) 90 (56.2%) 46 (28.8%) 23 (14.4%) 1 (0.6%) 0 (0%) * Multi-level shows were included in the show count for each competition level included in the show registration. Table 2: Network and node metrics for the 2016-2018 Equestrian Canada competition contact network in Ontario. Competitions were considered connected if at least one horse co-attended both events. 2016 2017 2018 Nodes (n) 153 155 160 Edges (n) 2794 2598 2523 Density 12.2% 11.3% 10.3% Diameter 6 6 6 Indegree * 14 (6 - 21) 12 (5 - 21) 11 (5 - 21) Outdegree * 14 (8 - 26) 14 (7 - 22) 13 (7 - 24) Betweenness * 22.4 (2.9 - 65.5) 20 (3 - 65) 20 (2.7 - 75.9) Edge Weight *,** 3 (1 - 15) 3 (1 - 11) 4 (1 - 15) Clusters 9 9 10 Modularity 0.55 0.5 0.58 * Results are presented as median (IQR) ** Edge weight = the number of horses co-attending both competitions Table 3: Network and node metrics for level-specific subnetworks of the 2016-2018 Equestrian Canada competition contact networks in Ontario. Bronze Silver Gold Platinum Nodes (n) 62 117 62 13 Edges 424 1006 591 44 Density 11.2% 7.4% 15.6% 28.2% Standardized Indegree * 0.1 (0.03 - 0.2) 0.05 (0.02 - 0.09) 0.1 (0.06 - 0.2) 0.2 (0.08 – 0.4) Standardized Outdegree * 0.1 (0.05 - 0.2) 0.06 (0.03 - 0.1) 0.1 (0.06 - 0.2) 0.2 (0.2 – 0.4) Edge Weight *,** 4 (2 - 15) 7 (2 - 47) 8 (2 - 36) 142 (16 - 250) Clusters 6 9 4 2 Modularity 0.71 0.69 0.17 0.04 * Results presented as median (IQR) ** Edge weight = the number of horses co-attending both competitions Table 4: Network and node metrics for discipline-specific sub-networks of the 2016-2018 Equestrian Canada competition contact networks in Ontario. Hunter/Jumper Dressage Eventing Nodes (n) 90 44 23 Edges 1665 317 206 Density 20.7% 16.8% 40.7% Standardized Indegree * 0.1 (0.07 - 0.3) 0.1 (0.07 - 0.2) 0.3 (0.2 - 0.6) Standardized Outdegree * 0.2 (0.1 - 0.3) 0.1 (0.04 - 0.2) 0.4 (0.2 - 0.6) Edge Weight *,** 6 (2 - 40) 3 (1 - 8) 7 (3 - 11) Clusters 8 3 3 Modularity 0.54 0.49 0.12 * Results presented as median (IQR) ** Edge weight = the number of horses co-attending both competitions Table 5: Network and node metrics for the 2016-2018 Equestrian Canada contact network of horses competing in Ontario. Horses were considered connected if they co-attended at least one event during the show season. 2016 2017 2018 Nodes (n) 4096 4035 3939 Edges 1,370,015 1,263,272 1,236,748 Density 16.3% 15.5% 15.9% Diameter 4 4 4 Geodesic * 2 (1 - 4) 2 (1 - 4) 2 (1 - 5) Degree * 619 (184 - 1097) 567 (186 – 965) 586 (191 - 988) Betweenness * 272 (22 - 1700) 265 (16 - 701) 302 (17 - 1694) Eigenvector Centrality * 0.005 (0.001 – 0.01) 0.005 (0.001 – 0.02) 0.005 (0.001 – 0.01) Edge Weight ** 1 (1 - 3) 1 (1 - 3) 1 (1 - 3) Clusters 4 5 7 Modularity 0.3 0.3 0.4 Clustering Coefficient 0.7 0.68 0.69 * Results presented as median (IQR) ** Edge Weight = the number of competitions co-attended by connected horses Table 6: Network and node metrics for the 2016-2018 Equestrian Canada contact network of competition venues in Ontario. Venues were considered connected if at least one horse attended a competition at both venues. 2016 2017 2018 Nodes (n) 58 56 63 Edges 703 626 658 Density 20.9% 20.0% 16.6% Diameter 3 4 4 Geodesic * 1.8 (1 - 3) 1.9 (1 - 4) 2 (1 - 4) Indegree * 10 (6 - 14) 9 (6 - 16) 9 (5 - 14) Outdegree * 10 (7 - 15) 10 (6 - 13) 9 (5 - 13) Betweenness * 3.6 (0.04 - 22.5) 3.8 (0.6 – 27.5) 4.7 (0.7 - 20.7) Giant Strong Component 53 54 59 Giant Weak Component 58 56 61 Clusters 4 4 6 Modularity 0.27 0.34 0.34 Clustering Coefficient 0.53 0.54 0.52 * Results presented as median (IQR) Table 7: A comparison of ‘small world’ network characteristics between the observed the 2016-2018 Equestrian Canada contact networks of horses competing in Ontario, and random networks with the same number of nodes and edges. Random Network Observed Network 2016: Median Geodesic * Clustering Coefficient * 1.8 (1.8 - 1.8) 0.16 (0.16 - 0.16) 2 (1 - 5) 0.7 2017: Median Geodesic * Clustering Coefficient * 1.8 (1.8 - 1.8) 0.16 (0.16 - 0.16) 2 (1 - 4) 0.68 2018: Median Geodesic * Clustering Coefficient * 1.8 (1.8 - 1.8) 0.16 (0.16 - 0.16) 2 (1 - 5) 0.69 * Results presented as median (IQR) Table 8: A comparison of ‘small world’ network characteristics between the observed 2016-2018 Equestrian Canada contact networks of competitions in Ontario, and random networks with the same number of nodes and edges. Random Network Observed Network 2016: Median Geodesic * Clustering Coefficient * 1.6 (1.6 - 1.6) 0.37 (0.37 - 0.38) 1.8 (1 - 3) 0.53 2017: Median Geodesic * Clustering Coefficient * 1.8 (1.8 -1.8) 0.36 (0.35 - 0.36) 1.9 (1 - 4) 0.54 2018: Median Geodesic * Clustering Coefficient * 1.7 (1.7 - 1.7) 0.3 (0.3 - 0.31) 2 (1 - 4) 0.52 * Results presented as median (IQR) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6804927","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486528353,"identity":"97fa1f7b-1d56-440d-b4aa-d2dd71b5fb5e","order_by":0,"name":"Tanya M Rossi","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"M","lastName":"Rossi","suffix":""},{"id":486528354,"identity":"5e559b26-befb-469f-bf5d-656faf7384fe","order_by":1,"name":"Terri L O’Sullivan","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Terri","middleName":"L","lastName":"O’Sullivan","suffix":""},{"id":486528355,"identity":"af2da098-0399-4e14-bab7-b95ec8536945","order_by":2,"name":"Amy L Greer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDADftK1SLaByARStBgcI1YL/7Qzxq8rKmzyje/3Pnxc+OMwA3/7AfxaJG7nmFmeOZNmue0Yu7HxjITDDBJnCFkF1GLY2HbYwOwYG5s0D1CLASHXyYO1/PtvYNzGxv4brIX/AX4tBrdzjB82NhwwMGBjY2MGa5EgYIvh7bQyxoZjyQYSx9KYpXnS0nkkbhCwRe528uaPDTV2BvzNxxg/89hYy/H3E7CFgYHDTAKZy0NIPRCwP/5AhKpRMApGwSgYyQAAKb8+d0Kg248AAAAASUVORK5CYII=","orcid":"","institution":"Trent University","correspondingAuthor":true,"prefix":"","firstName":"Amy","middleName":"L","lastName":"Greer","suffix":""}],"badges":[],"createdAt":"2025-06-02 19:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6804927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6804927/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-05248-z","type":"published","date":"2025-12-23T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87573603,"identity":"d1a408d9-1b54-43c9-a988-56a4b1511ab5","added_by":"auto","created_at":"2025-07-25 11:21:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":514543,"visible":true,"origin":"","legend":"\u003cp\u003eA directed, 1-mode contact network of 2017 Equestrian Canada competitions in Ontario, excluding endurance and reining shows. Edges represent co-attendance of at least one horse at both competitions and are directed by the relative timing of the events.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6804927/v1/54a2edb57bb012cd6b73eb35.png"},{"id":87574459,"identity":"16646a53-01b6-4225-99c9-04942f701be1","added_by":"auto","created_at":"2025-07-25 11:29:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500581,"visible":true,"origin":"","legend":"\u003cp\u003eA directed, 1-mode contact network of 2017 Equestrian Canada competition venues in Ontario. Edges represent co-attendance of at least one horse at both venues and are directed by the temporal order of horse movements between venues. The endurance competition venue was excluded from the figure.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6804927/v1/7e11784d019f841746c0a185.png"},{"id":87573604,"identity":"1621d9aa-a834-4ea2-93e7-a5e9339caf81","added_by":"auto","created_at":"2025-07-25 11:21:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267996,"visible":true,"origin":"","legend":"\u003cp\u003eA comparison of monthly network and node metrics for the 2016-2018 Equestrian Canada competition venue networks in Ontario.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6804927/v1/6b9bf96599467f92ebfcfe19.png"},{"id":99172263,"identity":"bde46735-d567-4309-b499-5b1c37dd536d","added_by":"auto","created_at":"2025-12-29 16:06:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1646353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6804927/v1/8f0c3691-9374-4248-9020-51770b1fb356.pdf"},{"id":87573601,"identity":"a25a4d30-d2fa-4801-bd9c-8397fe4b74c2","added_by":"auto","created_at":"2025-07-25 11:21:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26990,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6804927/v1/016fa3d5e91cc97b8740ea5c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Descriptive network analysis of Ontario, Canada equine competitions: Implications for disease control","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCompetitive events are an important part of horse ownership for many people in the equine industry. In addition to providing entertainment for horse enthusiasts, these competitions often serve as a way of judging equine athleticism, conformation, and breeding potential. While generally benefiting the industry they also introduce the potential for horses to acquire and transmit infectious diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Performance horses make frequent return trips from their home stables to competitions where they mix with animals travelling regionally, nationally or internationally [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These trips may increase the magnitude of an epidemic as well as widening its spatial spread. Examples of outbreaks associated with horse movements include the 2007 equine influenza epidemic in Australia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and the 2011 equine herpes virus outbreak in Utah [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Lack of knowledge on how horses are connected through competitions presents a challenge for contact tracing and understanding of how diseases may spread through the equine industry.\u003c/p\u003e \u003cp\u003eNetwork analysis can be used to describe contact patterns between individuals in a group and is an important tool to evaluate how pathogens could be propagated and maintained in populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, factors associated with an individual\u0026rsquo;s risk of acquiring or spreading infections can be identified and aid in the development of targeted disease control programs, thereby increasing the effectiveness of interventions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Network analysis has been applied in veterinary equine research to describe the movement of animals between competitions or farms, and within facilities. For example, several studies have used owner surveys to described horse movements between non-racing facilities in New Zealand, Canada, and Japan, and their potential implications for disease transmission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Network analyses of agricultural competitions and racing events have investigated the possible risk and contribution of these events to the incidence and magnitude of outbreaks, and to their geographical spread [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Spence \u003cem\u003eet al\u003c/em\u003e (2017) found that a single Ontario dressage show resulted in a contact network of 41 equine facilities and 779 horses including just primary and secondary contacts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Contact patterns within equine facilities were investigated in two studies using radio-frequency identification tags and these data were used to inform mathematical models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and to describe implications for biosecurity and disease control [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork topography can have significant implications for disease dynamics, including the maximum expected size of an outbreak, speed of transmission, and probability of disease extinction in a population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The concept of a \u0026lsquo;small world network\u0026rsquo; was introduced by Watts and Strogatz (1998) to describe networks which have similar pathlengths (degrees of separation) between individuals as those that are observed in a random network, but greater clustering [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Network models simulating the introduction of infectious diseases have demonstrated that transmission occurs more rapidly through networks with \u0026lsquo;small world\u0026rsquo; characteristics compared to less complex or random networks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, diseases are more likely to become endemic rather than die out [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These factors have important implications for disease control, including appropriate design of vaccination programs and emergency preparedness protocols [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Canada horses are classed as livestock; however, currently owners are not required to register their horses or report movements on or off property. This represents a challenge for describing horse contact networks and precludes estimation of the size of the population at risk. Currently, a description of the Ontario horse population is limited to the Agricultural Census conducted at 5-year intervals [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and to survey-based studies using convenience samples [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Equestrian Canada (EC) is the national governing body for equestrian sports in Canada, excluding racing, and regulates many equine events, including F\u0026eacute;deration Equestre Internationale (FEI) competitions. The five major disciplines regulated by EC are dressage, eventing, hunter/jumper, endurance, and reining. Beginning in 2012, competition organizers were required to register their events with EC and provide information on the event date, location, discipline, and level. As of 2016, organizers were also required to submit competition results for each class, creating a database of horses participating in each show. Networks of horses, competitions, and venues can be created using these data.\u003c/p\u003e \u003cp\u003eThe objectives of this study were: i) To describe the 2016\u0026ndash;2018 contact networks of Ontario, Canada Equestrian Canada competitions, competing horses, and venues, and ii) to determine if the networks of show venues or competing horses met the criteria for \u0026lsquo;small world networks\u0026rsquo;.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData were provided by EC including the registry of all EC sanctioned equine competitions and individual horse results for each show held between 2016 to 2018 in the province of Ontario, Canada. Horses participating in these shows were required to register with EC and obtain a passport number. 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).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study population consisted of all horses with an EC passport number competing in sanctioned Ontario competitions between 2016-2018. EC regulates hunter/jumper, endurance, dressage, eventing and reining shows occurring at the bronze, silver, gold or platinum levels in Canada. Horse attendance at non-sanctioned shows such as private schooling shows or country fairs were not included in the EC data set and therefore were not included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNetwork analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study used common network terminology, with \u0026lsquo;nodes\u0026rsquo; denoting individuals within a network and \u0026lsquo;edges\u0026rsquo; denoting the connections between nodes [7]. Networks were described as directed when the relationship between nodes was directional. For example, when horses moved from one competition venue to another. If the relationship was non-directional the network was described as \u0026lsquo;undirected\u0026rsquo;. For example, when two horses come in contact.\u003c/p\u003e\n\u003cp\u003eThe influence of individual nodes on the network was assessed by measures of centrality such as: degree, defined as the number of connections for a given node; \u0026nbsp;betweenness, defined as the frequency a node is on the shortest path between two nodes in the network; and eigenvector centrality, which \u0026ldquo;measures the importance of a node in the network by assigning its score relative to its connections to others, so that high-scoring neighbours of a node will contribute more to its individual score\u0026rdquo; [3, 7]. In directed networks, degree was divided into \u0026lsquo;in-degree\u0026rsquo;, the number of connections where a given node was the destination, and \u0026lsquo;out-degree\u0026rsquo;, the number of connections where a given node was the source. A node with no associated edges was considered a \u0026lsquo;isolate\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eNetwork density,\u0026nbsp;\u0026ldquo;the proportion of connections among nodes in the network relative to the total number of possible connections\u0026rdquo;, was calculated to measure the connectivity of the networks [7]. The level of cohesion in the network was determined by calculating the clustering coefficient ,the proportion of connected nodes who are also connected to one another [7]. Modularity is an additional measure of network cohesion and was calculated using the \u0026lsquo;cluster fast-greedy\u0026rsquo; function in igraph which identifies node clusters within the network by adding or removing nodes from a potential cluster to optimise the modularity score or edge density [27].\u0026nbsp;Important measures of pathlength, defined as the number of edges between two non-adjacent nodes, were calculated, including: mean geodesic, the shortest path between two nodes, and network diameter, the longest geodesic in the network\u0026nbsp;[7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNetworks were created using the igraph package in R Statistical Software (https://www.r-project.org) [28].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompetition networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDirected, 1-mode networks were generated with competitions as nodes and co-attendance of at least one horse between shows as edges for the 2016, 2017, and 2018 competition seasons. Edges were directed by show date to create a temporal sequence of competitions. Edges were weighted by the number of horses co-attending both shows. Network and node metrics calculated for these networks included: density, diameter, modularity, and edge weight, node degree, and betweenness. The calculation of other metrics, such as the clustering coefficient was precluded due to the directed, acyclic structure of the competition networks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiscipline and level specific competition networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe annual competition networks were split into discipline-specific and level-specific subgraphs where only edges between nodes of the same discipline or level were included in the network. In-degree and out-degree were normalized to facilitate comparison between networks, by dividing the degree by (\u003cem\u003en\u003c/em\u003e \u0026ndash; 1), where \u003cem\u003en\u0026nbsp;\u003c/em\u003eis the number of nodes in the network. Density, edge weight and modularity were also compared between subgraphs to investigate the level of connectivity and cohesion among competitions of the same discipline or level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHorse networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUndirected 1-mode networks were created with competing horses as nodes and co-attendance at one or more competitions as edges for the 2016, 2017, and 2018 competition seasons. Edges were weighted by the number of co-attended competitions. Measures of network level connectivity, including network density, diameter, and median geodesic were calculated for the networks. Network cohesion was evaluated by modularity, and clustering coefficient. Node centrality measures included degree, betweenness and eigenvector centrality. Patterns of participation in competitions were described for horses with the highest centrality measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVenue networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDirected, 1-mode networks were generated with show venues as nodes and co-attendance of at least one horse between venues as edges for the 2016, 2017, and 2018 competition seasons. Self-loops, repeated trips for a given horse to the same venue, were included in the networks. Edges were directed by the direction of horse movements between venues. Two additional measures of network connectivity were calculated for the venue networks, the giant strong and giant weak components. The giant strong component (GSC) is the largest group of venues in the network where all properties can be reached from every other property through directed pathways [7]. The giant weak component (GWC) is the largest group of venues in the network where all properties can be reached from every other property through pathways if edge direction is ignored, [7]. Other network and node metrics calculated for these networks included: density, diameter, modularity, clustering coefficient, median geodesic, node degree, and betweenness.\u003c/p\u003e\n\u003cp\u003eThe annual venue networks were split into monthly networks to describe the differences in network density, median geodesic, GSC, and GWC throughout the competition year. All nodes included in the annual networks were retained in the monthly networks and isolates were not removed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSmall world networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe median geodesic lengths and clustering coefficients of the horse and annual venue networks were compared with those of 100 randomly generated Erd\u0026ouml;s-Renyi networks to evaluate if the EC networks met the criteria of \u0026lsquo;small world\u0026rsquo; networks. The random networks had the same number of nodes and edges as the observed networks but the possibility of an edge between two nodes occurred with a set constant probability. Random graphs were generated using the \u0026lsquo;erdos.renyi.game\u0026rsquo; function in the R igraph package [28].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eData cleaning and descriptive statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData on registered shows and class results for the 2016-2018 show seasons were cleaned as outlined in the supplementary material (Fig. S1). Entry data were missing for approximately 25% of competitions in each year which resulted in the exclusion of those shows from the networks. No evident pattern in competition level or discipline was found for the missing entry data. Shows hosting classes for multiple levels were registered with EC in two ways: i) as a single competition with multiple levels, or ii) as separate shows of a single level occurring at the same venue on the same date, which resulted in double counting of some competitions. In the latter case, double counted competitions were merged into a single show with multiple levels, as the researchers were interested in the potential for horse-to-horse contact while at the same venue.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics on the number of yearly competitions, competing horses, and venues, and the disciplines and levels of registered shows are shown in Table 1. The number of yearly competitions and venues increased between 2016-2018 from 153 to 160, however, the number of competing horses decreased slightly from 4096 horses in 2016 to 3940 horses in 2018. The most common competition discipline was hunter/jumper, and the most common level was silver.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompetition network\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNetwork and node metrics for the 2016-2018 competition networks are displayed in Table 2 and an example of the 2017 competition network is shown in Figure 1. The endurance events in the 2016-2018 competition networks, and two dressage shows, one silver and one gold level, in the 2018 network were isolates. The two reining shows in 2017 formed an isolated dyad in the network with no connections to competitions in other disciplines. Regardless of level, all hunter/jumper, dressage, and eventing competitions were connected in the 2016-2017 networks and all, except the 2 dressage isolates were connected in 2018.\u0026nbsp;\u0026nbsp;As isolates were rare and mostly confined to endurance and reining nodes, they were removed from the competition networks before analysis. Clustering by discipline and level was observed using the fast-greedy community technique.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiscipline and level specific competition networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLevel and discipline specific network and node metrics for 2018 are shown in Tables 3 and 4, respectively (For 2016-2017 please see Tables S1-S4 in the supplementary material). The platinum level competition networks had the highest densities, edge weights, and median normalized out and in-degrees compared to other level specific subnetworks. Similarity among the discipline specific subnetworks, density and median normalized in and out-degrees, and edge weights were highest in the eventing networks compared to other disciplines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHorse network\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll horses competing in the 2016-2018 EC competition seasons were connected in the networks, apart from horses competing in the endurance event or reining competitions. Network and node metrics for the horse networks are shown in Table 5. Node degrees suggest horses potentially came in contact with a median of 567-619 other horses during the 2016-2018 EC competition seasons. Wide variation in node degree was observed, with some horses in 2017 potentially encountering 2471 other horses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDistribution of all node centrality measures for the horse networks were right skewed with a long right tail suggesting a small number of competing horses had a large influence on the observed network structure. Horses with the highest node degrees were horses competing at both the gold and silver level in a single show season, and horses who showed in hunter/jumper competitions. Betweenness scores were highest in horses competing at multiple levels and in multiple disciplines during the competition season. Similar to node degree, horses who showed in both silver and gold hunter\\jumper competitions had the highest eigenvector centrality scores.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVenue networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExcept for the endurance venue, all properties were connected in the 2016-2018 venue networks. These included venues hosting reining shows in 2017 (Figure 2). \u0026nbsp;The Giant strong components were large and approached the size of the entire yearly venue networks in 2016-2018. Network and node metrics for the yearly networks are shown in Table 6. Node indegree and outdegree distributions for all yearly venue networks were right skewed with a small proportion of properties forming a high number of connections to other venues.\u003c/p\u003e\n\u003cp\u003eSplitting the yearly networks by month revealed that network density, GSC, GWC, and median geodesic were highest during the summer months, May to August (Supplementary material, Table S5). Network metrics for the 2016 and 2017 monthly venue network were similar, however, there was a small decrease in density, and GSC in 2018 (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSmall world networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eComparisons of median geodesic length, and clustering coefficients between random\u0026nbsp;Erd\u0026ouml;s-Renyi networks and the observed 2016-2018 horse networks are shown in Table 7. Median geodesic length was similar between random and observed graphs for all years. The clustering coefficient was higher in the observed graphs indicating a greater level of clustering compared to random networks. These findings suggest the EC 2016-2018 horse networks are \u0026lsquo;small world\u0026rsquo; networks.\u003c/p\u003e\n\u003cp\u003eYearly venue networks had a similarly low median geodesic lengths to those of random graphs but larger clustering coefficients (Table 8). Therefore, observed venue networks for 2016-2018 also met the criteria for \u0026lsquo;small world\u0026rsquo; networks.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we have described the contact networks of EC competitions, horses, and venues in Ontario, Canada. Except for horses competing in endurance competitions, all horses in our networks had the potential for contact, either directly through mutual show attendance, or indirectly through use of the same competition venues. This interconnectivity provides potential opportunities for disease transmission and spread to occur throughout the EC equine population unless timely interventions or biosecurity protocols are employed. The role of competitions in pathogen dissemination has previously been explored in the equine industry. A study describing off-property movements of non-race horses in Japan suggested the risk of infectious disease transmission was higher in horses attending competitions, as they were responsible for the majority of horse movements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mathematical simulations of equine influenza spread through this Japanese network found greater disease dissemination both within and between other equine sectors by horses in the performance/competition sector [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In Ontario, a survey of owner-reported horse movements found that the most common reason for off-property horse movement was to attend competitions and that 97% of movements ended with horses returning to their home barns [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These return trips may provide a route for disease spread both within and between home facilities and may contribute to the magnitude of an outbreak by exposing horses that did not attend competitions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to high interconnectivity, the EC competition networks were highly clustered by discipline and level. Evaluation of discipline and level-specific subnetworks found variation in node and network metrics between disciplines, and levels. For example, the eventing subnetworks had the highest densities, degrees, and edge weights of the discipline-specific networks, suggesting that competing in eventing shows may carry a higher risk of pathogen transmission than other disciplines. Similarly, the platinum level subnetworks also had the highest densities, degrees, and edge weights of the level-specific subnetworks and may also represent a vulnerable population. However, industry practices may already help mitigate these risks. In Ontario, eventing shows are typical held outside with horses being shipped in daily rather than stabling on site. In addition, horses usually share little to no ring time due to the nature of the events. These behaviors result in fewer opportunities for close contact between horses and minimize opportunities for aerosol and/or droplet transmission or sharing of equipment and air space. Platinum level shows are often international competitions with horses travelling from foreign countries to participate; as a result these competitions are subject to more strict biosecurity guidelines designed to reduce risk of disease transmission [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExamination of the horse networks found that horses have the potential to contact a median of 567\u0026ndash;619 other horses per year at EC competitions. For some horses, this number was over 2000 horse contacts. However, these numbers may not reflect the true number of contacts at the individual level. Owner compliance with biosecurity guidelines likely reduces horse to horse contact by limiting physical proximity between horses, practicing appropriate infection control, and not sharing equipment. In addition, factors associated with the competition type and location can impact horse mixing patterns, resulting in greater or fewer opportunities for contact. Our networks only described contact between horses at EC competitions, and did not account for horse contacts at home barns. A study describing the contact network associated with a single Ontario silver-level dressage show found that 710 secondary contacts were identified from 69 horses attending the show, suggesting competition level networks may underestimate the total number of contacts with other horses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, although EC is the federal governing body for equine sports, horses may participate in non-sanctioned competitions, training clinics, trail rides, and other group events that may increase their contact with other horses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distribution of node centrality metrics in the horse networks indicated that a small proportion of individuals had a high influence on network structure and therefore, could play an important role in disease transmission through this population. Previous simulation studies have found that individuals with high network centrality scores have a higher risk of being infected, a shorter time to infection during epidemics, and infect a disproportionately large number of secondary contacts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the EC network, horses competing at both the gold and silver levels, particularly in the hunter/jumper discipline had the highest node degrees and eigenvector centrality scores suggesting these horses may be more susceptible to acquiring and spreading infectious disease. High betweenness scores were found in horses competing in multiple disciplines and levels during a show season. These horses could act as \u0026lsquo;bridges\u0026rsquo; connecting otherwise sparsely connected horse clusters, providing opportunities for disease transmission between horses participating in different levels and disciplines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous livestock studies have demonstrated that targeting these highly influential individuals may increase the effectiveness of disease surveillance, control and prevention programs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis of competition venue networks revealed that, except the endurance venue, all EC venues were connected through horses attending sanctioned shows at the same properties. In contrast to the competition networks, reining shows in 2017 were linked to other EC disciplines by use of common host venues. These findings suggest opportunities for indirect disease transmission, such as contact with improperly stored waste, contaminated surfaces, or staff, may facilitate disease spread between reining horses and horses competing in other disciplines [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Due to the high connectivity of these networks the GSCs included almost all venues in the yearly venue networks suggesting pathogens could spread to the majority of venues through directed edges. These findings have important implications for disease dynamics as GSC size represents the lower bound of predicted epidemic size [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, calculation of GSC in static networks assumes edges connecting nodes are constant, which rarely occurs in real world networks [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our venue networks depicted horse movements between properties over the nine-month competition season; these movements happen infrequently and over short time periods, therefore the size of GSCs in these networks were likely overestimated. To increase accuracy of GSC estimates, and to compare GSC sizes and median node degrees over the competition season, the yearly venue networks were partitioned into monthly networks. GSC size decreased in these networks compared with the yearly networks, and the highest estimates occurred between May-August. These findings, and the comparison of median node degree between months, suggest the risk of disease transmission and the size of a potential epidemic would be highest during the summer months. Our results are in agreement with a study by Spence \u003cem\u003eet al\u003c/em\u003e, 2018, which found horse movements between properties, including those hosting competitions, were highest in May and August [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distributions of indegree and outdegree for the venue networks were right skewed with a long right tail, suggesting a small number of popular venues had a high number of connections with other properties in the network. These findings are expected as, typically, a few large commercial horse venues host the majority of competitions during the EC show season in Ontario, while a higher number of small, private venues host one or two shows a year. The creation and adherence to strict biosecurity protocols are therefore, especially important at these large venues to prevent disease introduction and spread through a large proportion of the venue network [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetworks characterised by short pathlengths between nodes and high levels of clustering are termed \u0026lsquo;small world networks\u0026rsquo; and have important implications for disease spread [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, mathematical models of disease transmission have found disease propagation occurs more rapidly in these networks than in random networks, suggesting a shorter window for intervention in the face of an outbreak [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When median geodesics and clustering coefficients were compared with random networks, both the horse and the yearly venue networks met the criteria for small world networks. Horses and venues not directly connected in the networks were separated by a median of only 2 edges (1 horse or property) suggesting the transmission chain between individuals not in direct contact is short. These findings highlight the need for outbreak preparedness and response plans to be established well in advance of competitions to rapidly prevent disease spread should an outbreak be identified.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations, benefits, and future work\u003c/h2\u003e \u003cp\u003eThe EC competition data provided information on the majority of competition attendance and horse movements between shows, permitting the creation of contact networks. However, a proportion (~\u0026thinsp;25%) of show results were missing from the dataset to due to missing report data. As a result, competitions without these data were excluded from the networks as horse attendance could not be ascertained. In addition, this study described competition contact networks and did not include information on horse contacts at home barns or other locations. Therefore, the networks in these studies likely underestimate the size of the susceptible population, and node degrees for some individuals may be higher than reported here. However, Dawson \u003cem\u003eet al\u003c/em\u003e 2015 demonstrated that even incomplete networks can provide useful information for helping design disease control initiatives and informing models of disease transmission [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, co-attendance of horses at competitions was used as a proxy for direct or indirect contact as actual contact patterns of horses at competitions were not observed. Therefore, network connections in this study should be viewed as \u0026lsquo;potential\u0026rsquo; contacts and may not reflect true contact rates. Future work directly observing contacts between horses, people, and common surfaces at equine competitions should be performed to better gain insights into contact patterns while at the venue.\u003c/p\u003e \u003cp\u003eDespite these limitations our network analyses provide valuable information which could be used to inform simulations of infectious disease introduction events at EC competitions in Ontario and may also aid in identifying highly influential individuals or settings that should be targeted for disease surveillance and control programs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll 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).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are comprised of publicly available data (sport licence number, and competition results) that can be extracted from the Equestrian Canada website (www.equestrian.ca). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from Agriculture Canada (Agri-risk Program).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTR analyzed and interpreted the Equestrian Canada (EC) dataset and prepared the written manuscript for submission. TOS was involved in the conceptualization and design of the study and provided feedback on multiple manuscript revisions. ALG was involved in the conceptualization and design of the study, obtaining funding to support the study, data extraction, decision to publish, and provided feedback on multiple manuscript revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Equestrian Canada for providing the data for this project and for their insights on the equine industry in Ontario.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeese JS (2014) Infection control and biosecurity in equine disease control. 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Proceedings of the Royal Society B: Biological Sciences 270:699\u0026ndash;708.\u003c/li\u003e\n\u003cli\u003eWatts DJ, Strogatz SH (1998) Collective dynamics of \u0026ldquo;small-world\u0026rdquo; networks. Nature 393 June:440\u0026ndash;2.\u003c/li\u003e\n\u003cli\u003eLloyd AL, May RM (2001) How viruses spread among computers and people. Science 292:1316\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eMoore C, Newman MEJ (2000) Epidemics and percolation in small-world networks. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 61:5678\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eZanette DH, Kuperman M (2002) Effects of immunization in small-world epidemics. Physica A: Statistical Mechanics and its Applications 309:445\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eCensus of Agriculture. Statistics Canada (2016) https://www.statcan.gc.ca/eng/ca2016. Accessed 16 Nov 2020.\u003c/li\u003e\n\u003cli\u003eSpence KL, O\u0026rsquo;Sullivan TL, Poljak Z, Greer AL (2018) A longitudinal study describing horse demographics and movements during a competition season in Ontario, Canada. Canadian Veterinary Journal 59:783\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eNewman MEJ (2004) Fast algorithm for detecting community structure in networks. Physical Review E 69:1\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eCs\u0026aacute;rdi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695.\u003c/li\u003e\n\u003cli\u003eHayama Y, Kobayashi S, Nishida T, Muroga N, Tsutsui T (2012) Network simulation modeling of equine infectious anemia in the non-racehorse population in Japan. Preventive Veterinary Medicine 103:38\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eSpence KL, O\u0026rsquo;Sullivan TL, Poljak Z, Greer AL (2018) Estimating the potential for disease spread in horses associated with an equestrian show in Ontario, Canada using an agent-based model. Preventive Veterinary Medicine 151 December 2017:21\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eSlater J (2013) Biosecurity at equestrian competitions: Olympic legacy? Equine Veterinary Journal 45:396\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eStein RA (2011) Super-spreaders in infectious diseases. International Journal of Infectious Diseases 15:e510\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eHellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. (2020) Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts. medRxiv February:2020.02.08.20021162.\u003c/li\u003e\n\u003cli\u003eGuinat C, Relun A, Wall B, Morris A, Dixon L, Pfeiffer DU (2016) Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies. Sci Rep 6: 28429.\u003c/li\u003e\n\u003cli\u003eSanchez-Matamoros A, Martinez-Lopez B, Sanchez-Vizcaino F, Sanchez-Vizcaino JM (2013) Social network analysis of equidae movements and its application to risk-based surveillance and to control of spread of potential equidae diseases. Transboundary and Emerging Diseases 60:448\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eMyers C, Wilson WD (2006) Equine Influenza Virus. Clinical Techniques in Equine Practice 5:187\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eKao RR, Danon L, Green DM, Kiss IZ (2006) Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain. Proceedings of the Royal Society B: Biological Sciences 273:1999\u0026ndash;2007.\u003c/li\u003e\n\u003cli\u003eChristley RM, Robinson SE, Lysons R, French NP (2005) Network analysis of cattle movement in Great Britain. Proc Annu Conf Soc Vet Epidemiol Prev Med.\u003c/li\u003e\n\u003cli\u003eWoolhouse MEJ, Shaw DJ, Matthews L (2005) Epidemiological implications of the contact network structure for cattle farms and the 20 \u0026ndash; 80 rule. Biology Letters 1(3):350\u0026ndash;2.\u003c/li\u003e\n\u003cli\u003eDawson PM, Werkman M, Brooks-Pollock E, Tildesley MJ (2015) Epidemic predictions in an imperfect world: Modelling disease spread with partial data. Proceedings of the Royal Society B: Biological Sciences 282:1\u0026ndash;9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Descriptive statistics for registered Equestrian Canada competitions held in Ontario from 2016-2018.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eShows (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eVenues (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUnique horse IDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e4096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e4036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cu\u003eLevel:\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eBronze\u003c/p\u003e\n \u003cp\u003eSilver\u003c/p\u003e\n \u003cp\u003eGold\u003c/p\u003e\n \u003cp\u003ePlatinum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 (45.1%)\u003c/p\u003e\n \u003cp\u003e114 (74.5%)\u003c/p\u003e\n \u003cp\u003e61 (39.9%)\u003c/p\u003e\n \u003cp\u003e19 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70 (40.6%)\u003c/p\u003e\n \u003cp\u003e109 (74.8%)\u003c/p\u003e\n \u003cp\u003e58 (41.3%)\u003c/p\u003e\n \u003cp\u003e16 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63 (39.4%)\u003c/p\u003e\n \u003cp\u003e116 (72.5%)\u003c/p\u003e\n \u003cp\u003e64 (40.0%)\u003c/p\u003e\n \u003cp\u003e14 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMixed level shows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e87 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e80 (51.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e78 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cu\u003eDiscipline:\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eHunter/Jumper\u003c/p\u003e\n \u003cp\u003eDressage\u003c/p\u003e\n \u003cp\u003eEventing\u003c/p\u003e\n \u003cp\u003eEndurance\u003c/p\u003e\n \u003cp\u003eReining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87 (56.9%)\u003c/p\u003e\n \u003cp\u003e45 (29.4%)\u003c/p\u003e\n \u003cp\u003e20 (13.1%)\u003c/p\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84 (54.2%)\u003c/p\u003e\n \u003cp\u003e45 (29.0%)\u003c/p\u003e\n \u003cp\u003e23 (10.7%)\u003c/p\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003cp\u003e2 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (56.2%)\u003c/p\u003e\n \u003cp\u003e46 (28.8%)\u003c/p\u003e\n \u003cp\u003e23 (14.4%)\u003c/p\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eMulti-level shows were included in the show count for each competition level included in the show registration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Network and node metrics for the 2016-2018 Equestrian Canada competition contact network in Ontario. Competitions were considered connected if at least one horse co-attended both events.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNodes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eEdges (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eIndegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e14 (6 - 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e12 (5 - 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e11 (5 - 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eOutdegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e14 (8 - 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e14 (7 - 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e13 (7 - 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBetweenness\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e22.4 (2.9 - 65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20 (3 - 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20 (2.7 - 75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eEdge Weight\u003csup\u003e*,**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3 (1 - 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3 (1 - 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e4 (1 - 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eClusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults are presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003eEdge weight = the number of horses co-attending both competitions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Network and node metrics for level-specific subnetworks of the 2016-2018 Equestrian Canada competition contact networks in Ontario.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003eBronze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003eSilver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003eGold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003ePlatinum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eNodes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eEdges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e15.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e28.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eStandardized Indegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.1 (0.03 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e0.05 (0.02 - 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.1 (0.06 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e0.2 (0.08 \u0026ndash; 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eStandardized Outdegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.1 (0.05 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e0.06 (0.03 - 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.1 (0.06 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e0.2 (0.2 \u0026ndash; 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eEdge Weight\u003csup\u003e*,**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e4 (2 - 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e7 (2 - 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e8 (2 - 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e142 (16 - 250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eClusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4317%;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0617%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5932%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e Edge weight = the number of horses co-attending both competitions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eNetwork and node metrics for discipline-specific sub-networks of the 2016-2018 Equestrian Canada competition contact networks in Ontario.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003eHunter/Jumper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003eDressage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003eEventing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eNodes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEdges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e40.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eStandardized Indegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.1 (0.07 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.1 (0.07 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.3 (0.2 - 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eStandardized Outdegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.2 (0.1 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.1 (0.04 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.4 (0.2 - 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEdge Weight\u003csup\u003e*,**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e6 (2 - 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e3 (1 - 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e7 (3 - 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eClusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e Edge weight = the number of horses co-attending both competitions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Network and node metrics for the 2016-2018 Equestrian Canada contact network of horses competing in Ontario. Horses were considered connected if they co-attended at least one event during the show season.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eNodes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e4035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e3939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEdges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1,370,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e1,263,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1,236,748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e15.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e15.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eGeodesic\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2 (1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e2 (1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2 (1 - 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e619 (184 - 1097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e567 (186 \u0026ndash; 965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e586 (191 - 988)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eBetweenness\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e272 (22 - 1700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e265 (16 - 701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e302 (17 - 1694)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEigenvector Centrality\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.005 (0.001 \u0026ndash; 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.005 (0.001 \u0026ndash; 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.005 (0.001 \u0026ndash; 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEdge Weight\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1 (1 - 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e1 (1 - 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1 (1 - 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eClusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eClustering Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003eEdge Weight = the number of competitions co-attended by connected horses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eNetwork and node metrics for the 2016-2018 Equestrian Canada contact network of competition venues in Ontario. Venues were considered connected if at least one horse attended a competition at both venues.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eNodes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eEdges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e16.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eGeodesic\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e1.8 (1 - 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e1.9 (1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e2 (1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eIndegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e10 (6 - 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e9 (6 - 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e9 (5 - 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eOutdegree\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e10 (7 - 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e10 (6 - 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e9 (5 - 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eBetweenness\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e3.6 (0.04 - 22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e3.8 (0.6 \u0026ndash; 27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4.7 (0.7 - 20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eGiant Strong Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eGiant Weak Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eClusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.9985%;\"\u003e\n \u003cp\u003eClustering Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.994%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5037%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u003c/strong\u003e A comparison of \u0026lsquo;small world\u0026rsquo; network characteristics between the observed the 2016-2018 Equestrian Canada contact networks of horses competing in Ontario, and random networks with the same number of nodes and edges.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003eRandom Network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003eObserved Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2016:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1.8 - 1.8)\u003c/p\u003e\n \u003cp\u003e0.16 (0.16 - 0.16)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (1 - 5)\u003c/p\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2017:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1.8 - 1.8)\u003c/p\u003e\n \u003cp\u003e0.16 (0.16 - 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2 (1 - 4)\u003c/p\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2018:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1.8 - 1.8)\u003c/p\u003e\n \u003cp\u003e0.16 (0.16 - 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2 (1 - 5)\u003c/p\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u0026nbsp;\u003c/strong\u003eA comparison of \u0026lsquo;small world\u0026rsquo; network characteristics between the observed 2016-2018 Equestrian Canada contact networks of competitions in Ontario, and random networks with the same number of nodes and edges.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003eRandom Network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003eObserved Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2016:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.6 (1.6 - 1.6)\u003c/p\u003e\n \u003cp\u003e0.37 (0.37 - 0.38)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1 - 3)\u003c/p\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2017:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1.8 -1.8)\u003c/p\u003e\n \u003cp\u003e0.36 (0.35 - 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.9 (1 - 4)\u003c/p\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.8315%;\"\u003e\n \u003cp\u003e2018:\u003c/p\u003e\n \u003cp\u003eMedian Geodesic\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eClustering Coefficient\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.7 (1.7 - 1.7)\u003c/p\u003e\n \u003cp\u003e0.3 (0.3 - 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2 (1 - 4)\u003c/p\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResults presented as median (IQR)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"equine movements, network analysis, biosecurity, disease prevention and control, equine competitions, Canada","lastPublishedDoi":"10.21203/rs.3.rs-6804927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6804927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCompetitions 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 2016\u0026ndash;2018 contact networks of Equestrian Canada competitions in Ontario, Canada, and ii) determine if the networks exhibit characteristics of \u0026lsquo;small world networks\u0026rsquo;. Data on Equestrian Canada registered competitions in the province of Ontario, Canada between 2016\u0026ndash;2018 were used to create three types of yearly contact networks: competition networks, horse networks, and venue networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDressage, 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 Erd\u0026ouml;s-Renyi networks of the same size but exhibited higher levels of clustering indicating that both the horse and venue networks meet the criteria for \u0026lsquo;small world networks\u0026rsquo;.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe 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 \u0026lsquo;small world\u0026rsquo; 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.\u003c/p\u003e","manuscriptTitle":"Descriptive network analysis of Ontario, Canada equine competitions: Implications for disease control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 11:13:11","doi":"10.21203/rs.3.rs-6804927/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-28T02:56:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T20:35:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T17:14:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109395914628786507131591333915657215715","date":"2025-06-17T10:12:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301914440119700153488226593224905199586","date":"2025-06-16T14:36:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T22:24:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-05T08:18:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T04:46:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T04:44:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-06-02T19:41:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38c39eef-fb25-4f6e-966d-4552adc4631c","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:00:12+00:00","versionOfRecord":{"articleIdentity":"rs-6804927","link":"https://doi.org/10.1186/s12917-025-05248-z","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2025-12-23 15:57:04","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-07-25 11:13:11","video":"","vorDoi":"10.1186/s12917-025-05248-z","vorDoiUrl":"https://doi.org/10.1186/s12917-025-05248-z","workflowStages":[]},"version":"v1","identity":"rs-6804927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6804927","identity":"rs-6804927","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}