Forecasting viral disease outbreaks at the farm-level for commercial sow farms in the US

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dc.contributor.authorPaploski, Igor Adolfo Dexheimer"Bhojwani, Rahul Kumar"Sanhueza, Juan Manuel"Corzo, Cesar Agustin"VanderWaal, Kimberly
dc.date2021
dc.date.accessioned2021-12-05T18:20:04Z
dc.date.available2021-12-05T18:20:04Z
dc.description.abstractPorcine epidemic diarrhea virus (PEDv) was introduced to the U.S. in 2013 and is now considered to be endemic. Like many endemic diseases, it is challenging for producers to estimate and respond to spatial and temporal variation in risk. Utilizing a regional spatio-temporal dataset containing weekly PEDv infection status for similar to 15 % of the U.S. sow herd, we present a machine learning platform developed to forecast the probability of PEDv infection in sow farms in the U.S. Participating stakeholders (swine production companies) in a swine-dense region of the U.S. shared weekly information on a) PEDv status of farms and b) animal movements for the past week and scheduled movements for the upcoming week. Environmental (average temperature, humidity, among others) and land use characteristics (hog density, proportion of area with different land uses) in a 5 km radius around each farm were summarized. Using the Extreme Gradient Boosting (XGBoost) machine learning model with Synthetic Minority Over-sampling Technique (SMOTE), we developed a near real-time tool that generates weekly PEDv predictions (pertaining to two-weeks in advance) to farms of participating stakeholders. Based on retrospective data collected between 2014 and 2017, the sensitivity, specificity, positive and negative predictive values of our model were 19.9, 99.9, 70.5 and 99.4 %, respectively. Overall accuracy was 99.3 %, although this metric is heavily biased by imbalance in the data (less than 0.7 % of farms had an outbreak each week). This platform has been used to deliver weekly real-time forecasts since December 2019. The forecast platform has a built-in feature to re-train the predictive model in order to remain as relevant as possible to current epidemiological situations, or to expand to a different disease. These dynamic forecasts, which account for recent animal movements, present disease distribution, and environmental factors, will promote data-informed and targeted disease management and prevention within the U.S. swine industry.
dc.identifier.citationPREVENTIVE VETERINARY MEDICINE,Vol.196,,2021
dc.identifier.doi10.1016/j.prevetmed.2021.105449
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/4459
dc.language.isoen
dc.publisherELSEVIER
dc.sourcePREVENTIVE VETERINARY MEDICINE
dc.subject.englishPorcine epidemic diarrhea virus
dc.subject.englishMachine learning
dc.subject.englishForecasting
dc.subject.englishSwine
dc.subject.englishAnimal movement
dc.subject.englishSpatial epidemiology
dc.titleForecasting viral disease outbreaks at the farm-level for commercial sow farms in the US
dc.typeArticle
uct.indizacionSCI
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