ArticleJournal of the American Medical Informatics Association : JAMIA2022
Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.Nature communications · 2026Article
- AI-driven synthetic data generation for accelerating hepatology research: A study of the United Network for Organ Sharing (UNOS) database.Hepatology (Baltimore, Md.) · 2026Article
- Steps toward developing an algorithm to facilitate recognition of translational science.Journal of clinical and translational science · 2026Article
- Generating Synthetic Multi-national Longitudinal Cohorts for Clinically Grounded HIV Research.medRxiv : the preprint server for health sciences · 2025Article
- Treatment disparities and prognostic implications in octogenarians versus non-octogenarians with high-gradient severe aortic stenosis.Open heart · 2025Article
- [Re-identification potential of structured health data].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics.medRxiv : the preprint server for health sciences · 2023Article
- Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.Journal of clinical medicine · 2023Article
- Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- The imperative of applying ethical perspectives to biomedical and health informatics.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Predicting 5-year dementia conversion in veterans with mild cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND
methodsUsing an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 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.
resultsIn 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 = 5819), 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 datasets. Analyses of sparsely tested populations were less similar and had more data suppression.
conclusionIn 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.