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Researchers create algorithm to predict PEDv outbreaks

The high-tech approach already has an 80 per cent accuracy rate

Researchers from North Carolina State University have developed an algorithm that could give pig producers advance notice of porcine epidemic diarrhea virus (PEDv) outbreaks.

The proof-of-concept algorithm has potential for use in real-time prediction of other disease outbreaks in food animals.

PEDv is a virus that causes high mortality rates in preweaned piglets. The virus emerged in the U.S. in 2013 and by 2014 had infected approximately 50 per cent of breeding herds. PEDv is transmitted by contact with contaminated fecal matter.

Gustavo Machado, assistant professor of population health and pathobiology at NC State and corresponding author of a paper describing the work, used machine-learning techniques to create an algorithm capable of predicting PEDv outbreaks in space and time.

Machado, with colleagues from the University of Minnesota and Brazil’s Universidade Federal do Rio Grande do Sul, used weekly farm-level incidence data from sow farms to create the model. The data included all pig movement types, hog density, and environmental and weather factors such as vegetation, wind speed, temperature and precipitation.

The researchers looked at “neighbourhoods” that were defined as a 10-kilometre 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 per cent accuracy.

The most important risk factor for predicting PEDv spread was pig movement into and through the 10-km neighbourhood.

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