A research team has used a computer algorithm to discover key factors in the spread of diseases and found that moving hogs from one site to another is the most important avenue.
It seems to me this is the use of relatively high technology to come to an obvious conclusion.
Gustavo Machada, assistant professor of population health and pathobioloty at North Carolina State University led the team that included colleagues at the University of Minnesota and Universidada Federal do Rio Grande do Sul in Brazil.
They prepared their machine-learning techniques using Porcine Epidemic Diarrhea virus as the model.
The data included all pig movement types, hog density, and environmental and weather factors such as vegetation, wind speed, temperature and precipitation for sow herds.
They examined “neighbourhoods” that were defined as a 10-kilometer radius around sow farms. They fed the model information about outbreaks, animal movements into each neighbourhood and the environmental characteristics inside each neighbourhood.
Ultimately, their model was able to predict PEDV outbreaks with approximately 80 percent accuracy.
Besides pig movements, other important factors turned up by the analysis of the data were terrain slope and vegetation.
The research was based on data from sow herds in North Carolina.
Machado said “as we get more data from other farm sites across the U.S., we expect the model’s accuracy to increase. Our end goal is to have near real-time risk predictions so that farmers and veterinarians can provide preventative care to high-risk areas and make decisions based on data.”
Next steps for the researchers include improving the model to predict a wider range of diseases and expanding it to include other industries, such as poultry.
The work appears in Scientific Reports, and is supported by the National Pork Board and the Swine Health Information Center.
