Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing &gt;1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)
Authors: Thomas Jason A , Foraker Randi E , Zamstein Noa , Morrow Jon D , Payne Philip R O , Wilcox Adam B ,   the N3C Consortium, Haendel Melissa A , Chute Christopher G , Gersing Kenneth R , Walden Anita , Haendel Melissa A , Bennett Tellen D , Chute Christopher G , Eichmann David A , Guinney Justin , Kibbe Warren A , Liu Hongfang , Payne Philip R O , Pfaff Emily R , Robinson Peter N , Saltz Joel H , Spratt Heidi , Starren Justin , Suver Christine , Wilcox Adam B , Williams Andrew E , Wu Chunlei , Chute Christopher G , Pfaff Emily R , Gabriel Davera , Hong Stephanie S , Kostka Kristin , Lehmann Harold P , Moffitt Richard A , Morris Michele , Palchuk Matvey B , Zhang Xiaohan Tanner , Zhu Richard L , Pfaff Emily R , Amor Benjamin , Bissell Mark M , Clark Marshall , Girvin Andrew T , Hong Stephanie S , Kostka Kristin , Lee Adam M , Miller Robert T , Morris Michele , Palchuk Matvey B , Walters Kellie M , Walden Anita , Chae Yooree , Cook Connor , Dest Alexandra , Dietz Racquel R , Dillon Thomas , Francis Patricia A , Fuentes Rafael , Graves Alexis , McMurry Julie A , Neumann Andrew J , O'Neil Shawn T , Sheikh Usman , Volz Andréa M , Zampino Elizabeth , Austin Christopher P , Gersing Kenneth R , Bozzette Samuel , Deacy Mariam , Garbarini Nicole , Kurilla Michael G , Michael Sam G , Rutter Joni L , Temple-O'Connor Meredith , Amor Benjamin , Bissell Mark M , Bradwell Katie Rebecca , Girvin Andrew T , Manna Amin , Qureshi Nabeel , Saltz Mary Morrison , Suver Christine , Chute Christopher G , Haendel Melissa A , McMurry Julie A , Volz Andréa M , Walden Anita , Bramante Carolyn , Harper Jeremy Richard , Hernandez Wenndy , Koraishy Farrukh M , Mariona Federico , Mattapally Saidulu , Saha Amit , Vedula Satyanarayana , Fu Yujuan , Mathews Nisha , Mendelevitch Ofer 
Published: March 2022

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.