Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.

Journal: American journal of veterinary research
PMID: 39787711

Abstract

OBJECTIVE: To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRRS) virus and the basic demographic data on the severity of the impact on selected production parameters during clinical PRRS outbreaks in Ontario sow herds.

Authors

  • Dylan John Melmer
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
  • Terri L O'Sullivan
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
  • Amy Greer
    Biology Department, Trent University, Peterborough, ON, Canada.
  • Davor Ojkic
    Animal Health Laboratory, University of Guelph, Guelph, ON, Canada.
  • Robert Friendship
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
  • Zvonimir Poljak
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada; Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada. Electronic address: zpoljak@uoguelph.ca.

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