Wherever the date of symptom onset was missing, we estimated it from the date of specimen collection using a multiple imputation approach. Firstly, we modelled the lag time from date of symptoms onset to date of specimens collection from cases with complete data via a Poisson regression model using predictors significant at p,0.05. The covariates assessed in the model were patient age, gender, province, date of specimen collection, and collection of a specimen on a weekend day (i.e. Saturday or Sunday). Secondly we obtained an estimated lag-time for each observation with missing date of symptoms onset using a random sampling process from a Poisson distribution centred on the predicted value from the Poisson regression model. A Poisson distribution was selected to model count data. Thirdly we imputed missing dates of symptoms onset by subtracting the estimated lag-time from the date of specimen collection. The imputation process was repeated 100 times for each missing value, creating 100 datasets with information on the onset date (imputed or observed) for 12,630 laboratory-confirmed cases.