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Social Networks Contribute to PRRS Spread Among Swine

10 July 2012, at 8:14am

CANADA - Research conducted by the University of Guelph indicates the networks that connect pigs, particularly ownership and trucking networks, play a major role in the spread of PRRS, writes Bruce Cochrane.

Researchers with the University of Guelph, with funding provided by the Canadian Swine Health Board, are using advanced techniques including the development of computer models to identify the major factors that contribute to the spread of Porcine Reproductive and Respiratory Syndrome.

Dr. Zvonimir Poljiak, a swine epidemiologist in the Department of Population Medicine at the Ontario Veterinary College at the University of Guelph, says scientists are attempting to determine the contribution of factors such as spatial location, trucking, animal sources, feed sources and semen sources to the transmission of the PRRS virus.

Dr Zvonimir Poljiak-University of Guelph

I think the key finding is, what are the most important networks that are contributing to the spread of disease. What I mean by network is, herds today are somehow grouped into ownerships.

Maybe some of them do not cluster spatially together so we have to take into account this clustering of ownerships in space when we are evaluating some of the ways that virus is spreading spatially.

The other important factor beside the ownership is really the trucking.

The trucking networks could be identified as important for disease spread, depending on whether we are talking about all herds or only sow herds. The other factor that is also very important is the gilt sources.

We always knew that these are important networks for spread of disease anyway but it was nice to actually see that something that is biologically plausible is actually coming from these models as something that is really contributing to the spread of this particular genotype.

Dr Poljiak says, by identifying those networks that seem most important for disease spread, scientists can track the most important variables which in the long run will allow them to design better intervention strategies and better surveillance strategies.

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