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Prev Vet Med. 2019 Jan 1;162:84-94. doi: 10.1016/j.prevetmed.2018.11.011. Epub 2018 Nov 26.

Comparison of the dynamic networks of four equine boarding and training facilities.

Author information

1
Department of Population Medicine, University of Guelph, 50 Stone Rd E., Guelph, ON, N1G 2W1, Canada. Electronic address: rmilwid@uoguelph.ca.
2
Department of Population Medicine, University of Guelph, 50 Stone Rd E., Guelph, ON, N1G 2W1, Canada. Electronic address: tosulliv@uoguelph.ca.
3
Department of Population Medicine, University of Guelph, 50 Stone Rd E., Guelph, ON, N1G 2W1, Canada. Electronic address: zpoljak@uoguelph.ca.
4
Department of Population Medicine, University of Guelph, 50 Stone Rd E., Guelph, ON, N1G 2W1, Canada; Department of Mathematics and Statistics, York University, 4700 Keele St., Toronto, ON, M3J 1P3, Canada. Electronic address: mareklaskowski@gmail.com.
5
Department of Population Medicine, University of Guelph, 50 Stone Rd E., Guelph, ON, N1G 2W1, Canada. Electronic address: agreer@uoguelph.ca.

Abstract

Contact networks can be analyzed to assess the potential for disease spread throughout the network. The lack of Canadian facility-level equine contact data makes the characterization of the equine contact structure difficult. Therefore, the purpose of this study was to use empirical contact data to characterize and compare equine network characteristics between equine facilities in Ontario. Contact pattern data from 4 equine facilities were collected using radio-frequency identification tags. The collected data were used to form 7 static contact networks (1 for each study day) for each facility. The assumption of homogenous mixing, where each individual in a population has an equal probability of coming in contact, was assessed for each network, since homogenous mixing is often used to describe mixing patterns in disease transmission models. At the facility level, neither the day-long static networks, nor a combined, week-long network were representative of homogenous mixing. The Jaccard Similarity Index indicated that 11-62% of the contacts were repeated throughout the study period. A network generated with survey-based data enabled the prediction of 8.7-79.6% of the contacts that were recorded with the RFID tags. With respect to the node centrality, the normalized node degree ranged from 0.0 to 0.96, with a mean of 0.31. The node strength ranged from 0 to 1 with a mean of 0.38. For both the node degree and node strength, a node's centrality score relative to the other nodes' centrality scores tended to be consistent throughout the study week. A significant (p < 0.05), weak positive correlation existed between the node degree and strength (0.41 < r < 0.54). The normalized betweenness centrality ranged from 0.00 to 1.00, with a mean of 0.11. Lastly, an exponential random graph model was used to quantify the relationship between the distance between the horses' stalls and edge formation. The distance parameter was not significant for all of the facilities. To conclude, the non-homogenous nature of the contact patterns, coupled with the large range of the centrality measures indicate the importance of using empirical data to understand processes such as disease spread potential within equine populations. Although the collection of a full set of data is optimal, the study results suggest an ability to infer contact networks using observational data in situations where little-to-no data exist. This study serves as a starting point for the characterization of equine contact networks in Ontario.

KEYWORDS:

Contact network; Contact patterns; Equine; Horse; Network analysis; Radio-frequency identification technology

PMID:
30621903
DOI:
10.1016/j.prevetmed.2018.11.011
[Indexed for MEDLINE]
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