Uncovering Breeding Herd Vulnerabilities to PRRS

Michelle Sprague, DVM, is with AMVC in Audubon, Iowa.

Source: Swine Health Information Center

How vulnerable are your herds to porcine reproductive and respiratory syndrome (PRRS)? Want to know where to focus biosecurity? 

Gustavo Silva at Iowa State University, and his team, have devised a way to give farms a Biosecurity Vulnerability Score (BVS) based on surveys from Iowa State University’s PRRS Outbreak Investigation Program and data from the Morrison Swine Health Monitoring Project. This Swine Health Information Center-funded research shows that farms with higher BVS scores have broken more frequently with PRRS, adding validity to the scoring system.

The most important categories of risk events judged by the experts were those relating to swine movements, pickup/deliveries from/to premises and people movement. The five most important events that occur in breeding herds related to PRRS introduction, as judged by the experts, were breeding replacement animals, semen delivery, air transmission, weaned pigs transported from premises and dead animal removal, respectively.

To validate the BVS, Silva and team surveyed biosecurity practices and PRRS outbreak histories in 125 breed-to-wean herds in the US swine industry. Data on the frequency of PRRS outbreaks was used to test the hypothesis that biosecurity vulnerability scores were different between farms with a low incidence of PRRS outbreaks, compared to farms with a high incidence. In the two databases used, scores consistently showed that farms with higher scores have a higher frequency of PRRS outbreaks. Farms that had never had an outbreak investigation before had a significantly lower BVS score when compared to farms that had two or more outbreaks.

The BVS shows promise for assessing vulnerabilities on biosecurity protocols in order to reduce the frequency of PRRS outbreaks. And it may help producers and veterinarians prioritize investments in improving biosecurity practices over time. It can also be used to predict relative vulnerability of different farms within a production system and/or region based on frequencies of risk events since the probability of introduction of pathogens increases as the frequency of risk events increases.
 

 
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