Porcine epidemic diarrhoea virus (PEDV) and porcine deltacoronavirus (PDCoV) were first identified in Canada in 2014. Surveillance efforts have been instrumental in controlling both diseases. In this study, we provide an overview of surveillance components for the two diseases in Ontario (Canada), as well as PEDV and PDCoV incidence and prevalence measures. Swine herds located in the Province of Ontario, of any type, whose owners agreed to participate in a voluntary industry-led disease control programme (DCP) and with associated diagnostic or epidemiological information about the two swine coronaviruses, were eligible to be included for calculation of disease frequency at the provincial level. PEDV and PDCoV data stored in the industry DCP database were imported into the R statistical software and analysed to produce weekly frequency of incidence counts and prevalence counts, in addition to yearly herd-level incidence risk and prevalence between 2014 and 2016. The yearly herd-level incidence risk of PEDV, based on industry data, was 13.5%, 3.0% and 1.4% (95% CI: 11.1-16.2, 2.0-4.2, 0.8-2.3), while the yearly herd-level incidence risk of PDCoV was 1.1%, 0.3%, and 0.1% (95% CI: 0.5-2.2, 0.1-0.9, 0.0-0.5), for 2014, 2015 and 2016, respectively. Herd-level prevalence estimates for PEDV in the last week of 2014, 2015 and 2016 were 4.4%, 2.3% and 1.4%, respectively (95% CI: 3.1-6.0, 1.5-3.3, 0.8-2.2), while herd-level prevalence estimates for PDCoV in the last week of 2014, 2015 and 2016 were 0.5%, 0.2% and 0.2%, respectively (95% CI: 0.1-1.2, 0.0-0.6, 0.0-0.6). Collectively, our results point to low and decreasing incidence risk and prevalence for PEDV and PDCoV in Ontario, making both diseases possible candidates for disease elimination at the provincial level.

A B S T R A C TPorcine 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.

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