Work being conducted by Gustavo Machado and colleagues at North Carolina State University seeks to determine between-farm contact networks formed by different vehicle movements, according to a recent Swine Health Information Center newsletter. The team has developed a novel model to reconstruct vehicle movement networks. Ongoing work will identify the vehicles that create more connections among the farms and consequently may play a role as disease super-spreaders in the network.

Using a new methodology, the frequency, ratio of visit, and cumulative time of vehicles visiting farm units and clean stations is being evaluated resulting in risk stratification by farm production type and region. A risk ratio of visits is calculated from of the number times each vehicle visited a farm divided by the number times each vehicle visited a clean station, which facilitates the comparison among these two variables.

Using this new methodology along with other measurement techniques, the overall objectives of the study include determining the average, maximum, and minimum distance of potential disease spread driven by vehicles coming on and off farms. In addition, they are evaluating the impact of cleaning and disinfection effectiveness in reducing the number of between-farm contacts. Their work will help identify likely pathways for disease to spread between farms.

So far, the research team has collected GPS movements from 567 different vehicles from three commercial swine companies from two different regions in the US. Each vehicle was labeled according to its main transportation role. In addition, for 6393 farms used in this study, they collected Secure Pork Supply maps from the biosecurity plans available to identify distance between vehicles and perimeter buffer areas, thus enabling them to define when a vehicle is contacting a farm. They are in the process of creating the network structure to evaluate the connection among farms.

As part of the study objectives, the team is modeling potential spread of African swine fever by creating temporal ranges for the pathogen’s stability and potential introduction into susceptible farms. Using published literature about ASF stability in the environment, the model also evaluates probable vehicle cleaning effectiveness within the vehicle movement network.