Evaluation of surveillance protocols for detecting porcine reproductive and respiratory syndrome virus infection in boar studs by simulation modeling

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Abstract

Because porcine reproductive and respiratory syndrome virus (PRRSV) can be transmitted through semen, PRRSV-free boar studs need to be routinely monitored to rapidly detect any potential PRRSV introduction. However, current protocols for monitoring PRRSV in boar studs are diverse, sometimes very costly, and their effectiveness has not been quantified. The objective of this study was to evaluate the ability of different monitoring protocols to detect PRRSV introduction into a negative boar stud by using a simulation modeling approach. A stochastic transmission model was constructed to simulate the spread of PRRSV in a typical negative boar stud in the USA (herd size of 200 boars, 60% annual replacement) and the performance of monitoring protocols by using different sample sizes (10, 30, and 60 samples), sampling frequency (3 times a week, weekly, and biweekly), and diagnostic procedures (PCR on semen, PCR on serum, ELISA on serum, and both PCR and ELISA on serum). The monitoring protocols were evaluated in terms of the time from PRRSV introduction into the boar stud to PRRSV detection. Protocols that used PCR on serum detected the PRRSV introduction earlier than protocols that used PCR on semen, and these were earlier than those that used ELISA on serum. The most intensive protocol evaluated (testing 60 boars 3 times a week by PCR on serum) would need 13 days to detect 95% of the PRRSV introductions. These results support field observations, suggesting that an intensive monitoring protocol needs to be in place in a boar stud to quickly detect a PRRSV introduction.

Original languageEnglish (US)
Pages (from-to)492-501
Number of pages10
JournalJournal of Veterinary Diagnostic Investigation
Volume19
Issue number5
DOIs
StatePublished - Sep 2007

Keywords

  • Boars
  • Diagnostic
  • Monitoring
  • Porcine reproductive and respiratory syndrome
  • Sampling
  • Simulation model

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