A national growth mixture modeling analysis of county-level COVID-19 incidence rate trajectories and health inequities during three successive pandemic waves in 2020

We applied Growth Mixture Modeling (GMM) to characterize county-level COVID-19 incidence rate (IR) trajectories across three distinct waves in the United States from March 15 to November 2, 2020. GMM enabled the identification of latent subpopulations with shared temporal patterns of disease spread, offering a flexible analytic framework for uncovering both known and evolving disparities. Across the three periods, up to five trajectory groups were identified, revealing substantial geographic and temporal heterogeneity. To support interpretation and reduce multiple comparisons, we developed and validated a 17-item Social Determinants of Health (SDOH) index representing county-level economic and resource access factors. Higher-incidence rate trajectories consistently aligned with lower SDOH scores and greater proportions of Black or African American residents and younger populations. These disparities shifted over time, with patterns of high-incidence rates emerging in resource-limited counties across the South and Midwest in later waves. By further assessing well-documented inequities and expanding understanding of their dynamic expression across space and time, this study demonstrates the utility of GMM for public health surveillance, planning, and equitable response strategies in future outbreaks.