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Home / Equine Research Bank / PLoS One / Validation of modified radio-frequency identification tag fi...
PloS one2019; 14(1); e0210148; doi: 10.1371/journal.pone.0210148

Validation of modified radio-frequency identification tag firmware, using an equine population case study.

Abstract: Contact networks can be used to assess disease spread potential within a population. However, the data required to generate the networks can be challenging to collect. One method of collecting this type of data is by using radio-frequency identification (RFID) tags. The OpenBeacon RFID system generally consists of tags and readers. Communicating tags should be within 10m of the readers, which are powered by an external power source. The readers are challenging to implement in agricultural settings due to the lack of a power source and the large area needed to be covered. OpenBeacon firmware was modified to use the tag's onboard flash memory for data storage. The tags were deployed within an equine facility for a 7-day period. Tags were attached to the horses' halters, worn by facility staff, and placed in strategic locations around the facility to monitor which participants had contact with the specified locations during the study period. When the tags came within 2m of each other, they recorded the contact event participant IDs, and start and end times. At the end of the study period, the data were downloaded to a computer and analyzed using network analysis methods. The resulting networks were plausible given the facility schedule as described in a survey completed by the facility manager. Furthermore, changes in the daily facility operations as described in the survey were reflected in the tag-collected data. In terms of the battery life, 88% of batteries maintained a charge for at least 6 days. Lastly, no consistent trends were evident in the horses' centrality metrics. This study demonstrates the utility of RFID tags for the collection of equine contact data. Future work should include the collection of contact data from multiple equine facilities to better characterize equine disease spread potential in Ontario.
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Publication Date: 2019-01-09 PubMed ID: 30625195PubMed Central: PMC6326514DOI: 10.1371/journal.pone.0210148Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't
  • Validation Study

Summary

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

The researchers have successfully used modified OpenBeacon RFID tags to map out equine contact networks over a week-long period. This technology offers a promising solution to tracking disease spread within equine populations.

Radio-Frequency Identification (RFID) Tags and Disease Spread

  • Contact networks are used to predict disease spread among a population. In order to construct these networks, data about individual contact events must be collected. While this can be challenging, radio-frequency identification (RFID) tags offer one solution.
  • The OpenBeacon RFID system typically consists of tags that communicate with readers when within 10 meters of them. However, in agricultural settings, these readers are difficult to implement due to the lack of a continuous power source and the large area to be covered.

Modifying OpenBeacon Firmware

  • To overcome these challenges, the researchers modified the OpenBeacon firmware to store data in the tag’s onboard flash memory.
  • These modified tags were then used in a case study at an equine facility. The tags were attached to horses’ halters, worn by staff members, and placed around the facility.
  • Whenever two tags came within 2 meters of each other, the IDs and the start and end times of the contact event were recorded.

Results & Analysis

  • The collected data was analyzed using network analysis methods. The resulting networks made sense given the facility schedule and daily operations reported by the facility manager.
  • Any changes in the facility’s schedule were also reflected in the data recorded by the tags.
  • Regarding battery life, most (88%) of the tag batteries lasted for at least 6 days.
  • No clear patterns were found in the horses’ centrality metrics, suggesting no consistent contact trends among the horses.

Implications & Future Research

  • This study shows that RFID tags can be useful tools for collecting data on equine interactions, providing insights into possible disease spread paths.
  • In the future, researchers plan to gather contact data from multiple equine facilities to better understand the potential for disease spread among these populations in Ontario.

Cite This Article

APA
Milwid RM, O'Sullivan TL, Poljak Z, Laskowski M, Greer AL. (2019). Validation of modified radio-frequency identification tag firmware, using an equine population case study. PLoS One, 14(1), e0210148. https://doi.org/10.1371/journal.pone.0210148

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 14
Issue: 1
Pages: e0210148
PII: e0210148

Researcher Affiliations

Milwid, Rachael M
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
O'Sullivan, Terri L
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
Poljak, Zvonimir
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
Laskowski, Marek
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
  • Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
Greer, Amy L
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.

MeSH Terms

  • Animal Husbandry / instrumentation
  • Animal Husbandry / methods
  • Animals
  • Contact Tracing / instrumentation
  • Contact Tracing / methods
  • Horse Diseases / prevention & control
  • Horse Diseases / transmission
  • Horses
  • Ontario
  • Radio Frequency Identification Device

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 36 references
  1. Government of Ontario. Animal Health Act, 2009 [Internet]. 2018.
  2. Craft ME, Caillaud D. Network models: an underutilized tool in wildlife epidemiology?. Interdiscip Perspect Infect Dis 2011;2011:676949.
    doi: 10.1155/2011/676949pmc: PMC3063006pubmed: 21527981google scholar: lookup
  3. Craft ME. Infectious disease transmission and contact networks in wildlife and livestock.. Philos Trans R Soc Lond B Biol Sci 2015 May 26;370(1669).
    doi: 10.1098/rstb.2014.0107pmc: PMC4410373pubmed: 25870393google scholar: lookup
  4. Mastrandrea R, Fournet J, Barrat A. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys.. PLoS One 2015;10(9):e0136497.
    doi: 10.1371/journal.pone.0136497pmc: PMC4556655pubmed: 26325289google scholar: lookup
  5. Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences.. Science 2009 Feb 13;323(5916):892-5.
    doi: 10.1126/science.1165821pubmed: 19213908google scholar: lookup
  6. Barrat A, Cattuto C, Tozzi AE, Vanhems P, Voirin N. Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases.. Clin Microbiol Infect 2014 Jan;20(1):10-6.
    doi: 10.1111/1469-0691.12472pubmed: 24267942google scholar: lookup
  7. Salathé M, Kazandjieva M, Lee JW, Levis P, Feldman MW, Jones JH. A high-resolution human contact network for infectious disease transmission.. Proc Natl Acad Sci U S A 2010 Dec 21;107(51):22020-5.
    doi: 10.1073/pnas.1009094108pmc: PMC3009790pubmed: 21149721google scholar: lookup
  8. Cattuto C, Van den Broeck W, Barrat A, Colizza V, Pinton JF, Vespignani A. Dynamics of person-to-person interactions from distributed RFID sensor networks.. PLoS One 2010 Jul 15;5(7):e11596.
    doi: 10.1371/journal.pone.0011596pmc: PMC2904704pubmed: 20657651google scholar: lookup
  9. Ruiz-Garcia L, Lundadei L. The role of RFID in agriculture-applications, limitations and challenges. Comput Electron Agric 2011;79: 42–50.
  10. Domdouzis K, Kumar B, Anumba C. Radio-frequency identification (RFID) applications: A brief introduction. Adv Eng Informatics 2007;21: 350–355.
  11. Adrion F, Kapun A, Eckert F, Holland EM, Staiger M, Götz S. Monitoring trough visits of growing-finishing pigs with UHF-RFID. Comput Electron Agric 2018;144: 144–153.
    doi: 10.1016/j.compag.2017.11.036google scholar: lookup
  12. Schaefer AL, Cook NJ, Bench C, Chabot JB, Colyn J, Liu T, Okine EK, Stewart M, Webster JR. The non-invasive and automated detection of bovine respiratory disease onset in receiver calves using infrared thermography.. Res Vet Sci 2012 Oct;93(2):928-35.
    doi: 10.1016/j.rvsc.2011.09.021pmc: PMC7111736pubmed: 22055252google scholar: lookup
  13. Taylor PS, Hemsworth PH, Groves PJ, Gebhardt-Henrich SG, Rault JL. Ranging Behaviour of Commercial Free-Range Broiler Chickens 1: Factors Related to Flock Variability.. Animals (Basel) 2017 Jul 20;7(7).
    doi: 10.3390/ani7070054pmc: PMC5532569pubmed: 28726734google scholar: lookup
  14. Feng J, Fu Z, Wang Z, Xu M, Zhang X. Development and evaluation on a RFID-based traceability system for cattle/beef quality safety in China. Food Control 2013;31: 314–325.
    doi: 10.1016/j.foodcont.2012.10.016google scholar: lookup
  15. Weinstein R. RFID: A technical overview and its application to enterprise. IT professioinal 2005;7: 27–33.
  16. Roberts CM. Radio frequency identification (RFID). Comput Secur 2006;25: 18–26.
    doi: 10.1016/j.cose.2005.12.003google scholar: lookup
  17. Barrat A, Cattuto C, Colizza V, Pinton JF, Van den Broeck W, Vespignani A. High resolution dynamical mapping of social interactions with active RFID. Arxiv Prepr arXiv08114170 2008; 1–16.
  18. Isella L, Stehlé J, Barrat A, Cattuto C, Pinton JF, Van den Broeck W. What's in a crowd? Analysis of face-to-face behavioral networks.. J Theor Biol 2011 Feb 21;271(1):166-80.
    doi: 10.1016/j.jtbi.2010.11.033pubmed: 21130777google scholar: lookup
  19. Bitmanufactory. Open hardware for open beacons [Internet]. [cited 5 Apr 2017]. Available: https://www.bitmanufactory.com/
  20. Myers C, Wilson WD. Equine influenza virus. Clin Tech Equine Pract 2006;5: 187–196.
    doi: 10.1053/j.ctep.2006.03.013google scholar: lookup
  21. Davis J, Garner MG, East IJ. Analysis of local spread of equine influenza in the Park Ridge region of Queensland.. Transbound Emerg Dis 2009 Mar;56(1-2):31-8.
    doi: 10.1111/j.1865-1682.2008.01060.xpubmed: 19200296google scholar: lookup
  22. Waller AS, Sellon DC, Sweeney CR, Timoney PJ, Newton JR, Hines MT. Streptococcal infections Equine Infectious Diseases. Second Edi Elsevier Inc.; 2014. pp. 265–277.
  23. Barnett C, Brumbaugh G, Holland R, Lunn P, Vaala W, Voss E. Guidelines for vaccination of horses. American Association of Equine Practitioners; Lexington, Kentucky, USA; 2001.
  24. Sweeney CR, Timoney JF, Newton JR, Hines MT. Streptococcus equi infections in horses: guidelines for treatment, control, and prevention of strangles.. J Vet Intern Med 2005 Jan-Feb;19(1):123-34.
    pubmed: 15715061
  25. Government of Canada. Daily data report for November 2016 [Internet]. [cited 27 Nov 2017]. Available: http://climate.weather.gc.ca
  26. Lloyd AS, Martin JE, Bornett-Gauci HLI, Wilkinson RG. Horse personality: Variation between breeds. Appl Anim Behav Sci 2008;112: 369–383.
    doi: 10.1016/j.applanim.2007.08.010google scholar: lookup
  27. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2016.
  28. Gabor C, Tamas N. The igraph software package for complex network research. InterJournal 2006;Complex Sy: 1695.
  29. Martínez-López B, Perez AM, Sánchez-Vizcaíno JM. Social network analysis. Review of general concepts and use in preventive veterinary medicine.. Transbound Emerg Dis 2009 May;56(4):109-20.
    doi: 10.1111/j.1865-1682.2009.01073.xpubmed: 19341388google scholar: lookup
  30. Opsahl T, Agneessens F, Skvoretz J. Article node centrality in weighted networks: Generalizing degree and shortest paths. Soc Networks 2010;32: 245–251.
  31. Borgatti SP, Everett M, Johnson JC. Analyzing social networks. SAGE Publications Limited; 2013.
  32. Mccombie W, Welt BA. Radio Frequency Identification Technology Kirk-Othmer Encyclopedia of Chemical Technology. Wiley Online Library 2011.
    doi: 10.1007/978-3-642-41332-2google scholar: lookup
  33. Voirin N, Payet C, Barrat A, Cattuto C, Khanafer N, Régis C, Kim BA, Comte B, Casalegno JS, Lina B, Vanhems P. Combining high-resolution contact data with virological data to investigate influenza transmission in a tertiary care hospital.. Infect Control Hosp Epidemiol 2015 Mar;36(3):254-60.
    doi: 10.1017/ice.2014.53pubmed: 25695165google scholar: lookup
  34. Machens A, Gesualdo F, Rizzo C, Tozzi AE, Barrat A, Cattuto C. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices.. BMC Infect Dis 2013 Apr 23;13:185.
    doi: 10.1186/1471-2334-13-185pmc: PMC3640968pubmed: 23618005google scholar: lookup
  35. Stehlé J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton JF, Quaggiotto M, Van den Broeck W, Régis C, Lina B, Vanhems P. High-resolution measurements of face-to-face contact patterns in a primary school.. PLoS One 2011;6(8):e23176.
    doi: 10.1371/journal.pone.0023176pmc: PMC3156713pubmed: 21858018google scholar: lookup
  36. Eubank S, Guclu H, Kumar VS, Marathe MV, Srinivasan A, Toroczkai Z, Wang N. Modelling disease outbreaks in realistic urban social networks.. Nature 2004 May 13;429(6988):180-4.
    doi: 10.1038/nature02541pubmed: 15141212google scholar: lookup

Citations

This article has been cited 4 times.
  1. Fielding HR, Silk MJ, McKinley TJ, Delahay RJ, Wilson-Aggarwal JK, Gauvin L, Ozella L, Cattuto C, McDonald RA. Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain. Prev Vet Med 2021 Sep;194:105443.
    doi: 10.1016/j.prevetmed.2021.105443pubmed: 34352518google scholar: lookup
  2. Rossi TM, Milwid RM, Moore A, O'Sullivan TL, Greer AL. Descriptive network analysis of a Standardbred horse training facility contact network: Implications for disease transmission. Can Vet J 2020 Aug;61(8):853-859.
    pubmed: 32741991
  3. Gelardi V, Godard J, Paleressompoulle D, Claidiere N, Barrat A. Measuring social networks in primates: wearable sensors versus direct observations. Proc Math Phys Eng Sci 2020 Apr;476(2236):20190737.
    doi: 10.1098/rspa.2019.0737pubmed: 32398933google scholar: lookup
  4. Camargo VA, Pajor EA, Pearson JM. Validation of proximity loggers to record proximity events among beef bulls. Transl Anim Sci 2025;9:txaf011.
    doi: 10.1093/tas/txaf011pubmed: 39959561google scholar: lookup
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