By Amy Kinsley, Meggan Craft, Andres Perez and Kim VanderWaal, University of Minnesota Department of Veterinary Population Medicine
The multi-site pig production structure of the U.S. swine industry requires frequent movement of swine, making swine populations vulnerable to disease spread. This scenario becomes even more relevant when there are thousands of pigs on the same site, especially in highly dense regions. For instance, production systems may have pig farms that are multi-sourced or farms that are single-sourced but that send pigs to multiple sites such as gilt development units.
By targeting those sites within a production system that play an important “connectivity” role, we can develop prevention and control strategies for disease containment together with targeted surveillance for early detection of disease.
To understand how movement patterns affect potential disease spread, the Swine Health Information Center funded a project at the University of Minnesota to investigate how the vulnerability of the U.S. swine industry to disease spread can be reduced through targeting surveillance and control efforts toward key farms in swine transport networks.
Swine movement data in three large production systems in the United States were analyzed to measure the influence a specific farm has on potential disease spread throughout the system. The analysis focused on quantifying potential disease spread using several network metrics, including: a) the number of other farms to which a specific farm sent or received pigs, and b) the Mean Infection Potential, which measures potential incoming and outgoing infection chains (i.e. the upstream or downstream flow of pigs). The MIP was calculated based on chains of farms that are connected via movements, which was a result of factors such as pig sourcing, location, etc. For example, if a nursery farm received pigs from several sow farms and then had a movement event to multiple finisher farms, that farm would likely have a high MIP and could be called a “super-spreader” (i.e. a player that contributes to an unusually high number of infections).
The study found that by directing disease interventions toward farms based on their MIP, the potential for infectious disease transmission in the production system can be substantially reduced. Interestingly, production type (sow, nursery, finishing, farrow-finish and wean-to-finish) did not seem to be a key determinant of the MIP.
These findings help us understand how to prioritize resources necessary to control disease spread in the event of an epidemic. Farms with high MIP values could be prioritized for surveillance, prevention and interventions to increase the efficiency of response efforts. Such farms are more likely to break and subsequently spread disease, so targeting surveillance at these locations makes sense. When the next epidemic strikes, network metrics can help identify which key sites should make modifications to their management strategies, which might include depopulation, transport segregation, switching from multiple to single source, or vaccination, which could substantially decrease or diminish disease spread. Furthermore, this approach could help guide risk-based management and monitoring where farms are grouped by their estimated risk of exposure and spreading diseases, including zoning and compartmentalization strategies, which could significantly mitigate the impact of an epidemic.
When we really break it down, it’s all about incoming and outgoing contacts and the impact on risk. For more information about analysis of movement data, identifying super-spreaders farms and implications for disease control for farms in your system, contact Kim VanderWaal.