Abstract Background Concerns about patient privacy have limited access to COVID-19 datasets Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner Objectives Evaluate the utility of synthetic data by comparing analysis results between real and synthetic data Methods A gradient boosted classification tree was built to predict death using Ontario’s 90 514 COVID-19 case records linked with community comorbidity, demographic, and socioeconomic characteristics Model accuracy and relationships were evaluated, as well as privacy risks The same model was developed on a synthesized dataset and compared to one from the original data Results The AUROC and AUPRC for the real data model were 0 945 [95% confidence interval (CI), 0 941–0 948] and 0 34 (95% CI, 0 313–0 368), respectively The synthetic data model had AUROC and AUPRC of 0 94 (95% CI, 0 936–0 944) and 0 313 (95% CI, 0 286–0 342) with confidence interval overlap of 45 05% and 52 02% when compared with the real data The most important predictors of death for the real and synthetic models were in descending order: age, days since January 1, 2020, type of exposure, and gender The functional relationships were similar between the two data sets Attribute disclosure risks were 0 0585, and membership disclosure risk was low Conclusions This synthetic dataset could be used as a proxy for the real dataset
Evaluating the utility of synthetic COVID-19 case data