Forecasting viral disease outbreaks at the farm-level for commercial sow farms in the U.S.

Igor Adolfo Dexheimer Paploski, Rahul Kumar Bhojwani, Juan Manuel Sanhueza, Cesar Agustín Corzo, Kimberly VanderWaal

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Porcine 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 ∼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.

Original languageEnglish (US)
Article number105449
JournalPreventive Veterinary Medicine
Early online dateJul 29 2021
StatePublished - Nov 2021

Bibliographical note

Funding Information:
This project was supported by the Critical Agricultural Research and Extension program grant no. 2018-68008-27890 from the USDA National Institute of Food and Agriculture and by the joint NIFA-NSF-NIH Ecology and Evolution of Infectious Disease award 2019-67015-29918. The Morrison Sine Health Monitoring Project (MSHMP) is funded by the Swine Health Information Center .

Publisher Copyright:
© 2021 Elsevier B.V.


  • Animal movement
  • Forecasting
  • Machine learning
  • Porcine epidemic diarrhea virus
  • Spatial epidemiology
  • Swine


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