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)

Abstract

Objective: This 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 methods: Using 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.

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 = 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.

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.

Keywords: COVID-19; data sharing; data utility; electronic health records; synthetic data.

Figure 1.

Aggregate epidemic curves with counts (vertical bars) and 7-day moving averages (smoothed line) for (A) tests, (B) cases, (C) percent positive, (D) admissions, and (E) deaths during admission. Color encodings include original data (light blue) and synthetic data (light red), with their overlap (purple). As counts get smaller from tests to deaths, the epidemic curves visually appear less similar.

Figure 2.

Distributions of total tests by zip code shown by original data (light blue) and synthetic data (light red), and their overlap (purple). (A) All data binned by 100. (B) Filtered data with a bin size of 10 to only show the distribution of tests by zip code in zip codes with <100 tests. Both y-axes use a log scale. As seen in panel A, the vast majority of tests are conducted in a minority of zip codes. As seen in panels A and B, the distribution of the synthetic data closely matches the original data at >10 tests per zip code.

Figure 3.

Zip code-level epidemic curves with counts (vertical bars) and 7-day moving averages (smoothed line). Color encodings include original data (light blue) and synthetic data (light red), with their overlap (purple). Each row (A–E) corresponds to a different randomly sampled zip code visualizing cases (left column) and admissions (right column). Synthetic data are more similar to original data when indicator density is higher. Overall, synthetic data closely match overall trends and closely match start and end dates.

Figure 4.

Zip code-level epidemic curves with counts (vertical bars) and 7-day moving averages (smoothed line). Color encodings include original data (light blue) and synthetic data (light red), with their overlap (purple). Each row (A–E) corresponds to a different randomly sampled zip code visualizing cases (left column) and admissions (right column). Synthetic data are more similar to original data when indicator density is higher. Overall, synthetic data closely match overall trends and closely match start and end dates.

Figure 5.

Workflow of synthetic error experiment showing synthetic data on the left, original data on the right which are then merged to allow the calculation of synthetic error to be made.

Figure 6.

Synthetic error distributions per zip code stratified by month for tests (top row), cases (middle row), and admissions (bottom row) shown both at original scale (left column) and zoomed in to the peak of each row’s middle bin (legend showing bin ranges and color encodings seen on the far right of each row). Original data value denotes the monthly count in the original data for the key indicator of interest. Box plots of synthetic error are shown in the top 30% of each sub-plot (A–F), with a histogram of synthetic error shown in the bottom 70%. Within each sub-plot, the box plot and histogram have a shared x-axis corresponding to synthetic error and shared bins corresponding to the original data value. The y-axis shows the number of zip codes stratified by month (eg, zip code month pairs). Boxes in the box plots span from Q1 to Q3, with median marked inside the box. Fences span ±1.5 times the IQR. Error increased as the size (count) of the original data increased, which allows users to estimate the level of error in their data of interest. The synthetic data systematically underestimate the monthly count of key indicators in zip codes with the most tests, cases, and deaths, and overestimate them in zip codes with the least.