SHIC-Funded Study Offers to Predict PEDV Outbreaks

From PED research, the Swine Health Information Center (SHIC) is also learning how the disease spreads and creating ideas for managing risk. ( PORK )

The Swine Health Information Center (SHIC) collaborated with the Morrison Swine Health Information Project to enable a study applying machine-learning to predict porcine epidemic diarrhea virus (PED) outbreaks on sow farms. The researchers were able determine it is possible to predict the probability of an outbreak when considering animal movements and environmental conditions. Another goal was to see if shared producer data could be used to develop critical tools for the prevention of disease spread and implementation of risk mitigation. Further, this work serves as a model for near real-time disease forecasting. The authors hope it will advance disease surveillance and control for endemic swine pathogens in the U.S.

Many mechanisms play an apparent role in the spread of viruses: movement of infectious animals, airborne spread of aerosols, wildlife, contaminated fomites, feed, and personnel. Understanding the complexity of animal movements as a whole – routes, volumes, and frequency – is essential. The broader view used in the study, rather than focusing solely on a specific farm, helps to better understand PED epidemiology and spread by analyzing the cumulative effect of animal movement and environment on infection risk.

Analyzing data from PED outbreaks during 2015 as well as a large, retrospective dataset, the study was able to correctly predict when an outbreak occurred during one-week periods with greater than 80% accuracy. Because they used a neighborhood-based approach, researchers were able to simultaneously capture disease risks associated with long-distance animal movement as well as local neighborhood effects. They defined a neighborhood as the area 10 kilometers around a farm. They evaluated the relative importance of neighborhood effects in determining infection risk. This included animal movements to farms nearby, hog density in the area, environmental factors, and the landscape.

The team used a machine-learning technique to analyze the data and found their model accuracy ranged from 78.2 to 83.3%. In the process, researchers learned the most important predictor for PED outbreaks was the overall number of pigs moved into the neighborhood followed by season of the year. Hog density was also confirmed as a significant factor.

The next steps will be using the model to apply to real-time data so PED outbreak risk can be predicted and interventions can be put into place to prevent the outbreak. The study was done in one region of the country and needs to be repeated in other regions, with different movement, neighborhood and environmental factors. And testing the model to be able to predict outbreaks of other diseases, for example PRRS, will be done.

Authors of the study are Gustavo Machado, Carles Vilalta, Mariana Recamonde-Mendoza, Cesar Corzo, Montserrat Torremorell, Andres Perez and Kimberly VanderWaal. Read the entire study with detail here: https://www.nature.com/articles/s41598-018-36934-8

 

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