UMMS Affiliation

UMass Center for Clinical and Translational Science

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Article Preprint


Bioinformatics | Data Science | Epidemiology | Health Information Technology | Statistics and Probability | Translational Medical Research


Objective To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses.

Materials and Methods Using an original data set (n=1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated.

Results In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean=2.9±2.4; max=16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n=171) and for all unsuppressed zip codes (n=5,819), respectively. In small sample sizes, synthetic data utility was notably decreased.

Discussion Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression.

Conclusion In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression -an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.


Health Informatics, COVID-19, SARS-CoV-2, community spread, synthetic data, epidemiology, data utility, data sharing, synthetic data, electronic health records

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The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

DOI of Published Version



medRxiv 2021.07.06.21259051; doi: Link to preprint on medRxiv.

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This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.

The UMass Center for Clinical and Translational Science (UMCCTS), UL1TR001453, helped fund this study.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.