What can near-real-time PRRSV surveillance in Ontario reveal?
Diagnostic laboratory data supported rapid PRRSV trend monitoring in Ontario while highlighting the importance of metadata quality and limits of RFLP classification.
Diagnostic laboratory data supported rapid PRRSV trend monitoring in Ontario while highlighting the importance of metadata quality and limits of RFLP classification.
Problem: Diagnostic laboratory data are abundant, but their use for continuous PRRSV surveillance requires appropriate aggregation, standardization, and epidemiological interpretation.
Methods: The authors built daily updated dashboards from University of Guelph Animal Health Laboratory data, including 26,535 usable submissions from 2014 to July 2023 and 13,299 submissions from 1,221 premises between 2018 and July 2023 for farm-level analyses.
1. The average number of farms tested each week increased from 24 in 2018 to 48 in 2023, while the average number testing positive increased from 5 to 11 per week.
2. Among 1,427 typeable submissions from 2019 through July 2023, 80 RFLP patterns were identified, with 1-8-4, 1-1-1, 1-4-2, and 2-5-2 accounting for 59.5% of results.
3. The study shows that diagnostic data can support rapid temporal surveillance while emphasizing that observed trends depend on metadata quality, submission behavior, and the limitations of RFLP as a molecular classification tool.
Original publication
Petukhova T et al. Insights gained through real-time monitoring of porcine reproductive and respiratory syndrome virus and description of temporal trends based on laboratory data in Ontario, Canada. Frontiers in Veterinary Science. 2025;12:1528422.