Forecasting herd-level porcine epidemic diarrhea (PED) frequency trends in Ontario (Canada)

Abstract

Porcine Epidemic Diarrhea Virus (PEDV) emerged in North America in 2013. The first case of PEDV in Canada was identified on an Ontario farm in January 2014. Surveillance was instrumental in identifying the initial case and in minimizing the spread of the virus to other farms. With recent advances in predictive analytics showing promise for health and disease forecasting, the primary objective of this study was to apply machine learning predictive methods (random forest, artificial neural networks, and classification and regression trees) to provincial PEDV incidence data, and in so doing determine their accuracy for predicting future PEDV trends. Trend was defined as the cumulative number of new cases over a four-week interval, and consisted of four levels (zero, low, medium and high). Provincial PEDV incidence and prevalence estimates from an industry database, as well as temperature, humidity, and precipitation data, were combined to create the forecast dataset. With 10-fold cross validation performed on the entire dataset, the overall accuracy was 0.68 (95% CI: 0.60 – 0.75), 0.57 (95% CI: 0.49 – 0.64), and 0.55 (0.47 – 0.63) for the random forest, artificial neural network, and classification and regression tree models, respectively. Based on the cross-validation approach to evaluating predictive accuracy, the random forest model provided the best prediction.