Trajectory Modeling of Spatio-Temporal Trends in COVID-19 Incidence in Flint and Genesee County, Michigan

PURPOSE: The establishment of community-academic partnerships to digest data and create actionable policy and advocacy steps is of continuing importance. In this paper, we document COVID-19 racial and geographic disparities uncovered via a collaboration between a local health department and university research center. METHODS: We leverage individual level data for all COVID-19 cases aggregated to the census block group level, where group-based trajectory modeling was employed to identify latent patterns of change and continuity in COVID-19 diagnoses. RESULTS: Linking with socioeconomic data from the census, we identified the types of communities most heavily affected by each of Michigan's two waves (in spring and fall of 2020). This includes a geographic and racial gap in COVID-19 cases during the first wave, which is largely eliminated during the second wave. CONCLUSIONS: Our work has been extremely valuable for community partners, informing community-level response toward testing, treatment, and vaccination. In particular, identifying and conducting advocacy on the sizeable racial disparity in COVID-19 cases during the first wave in spring 2020 helped our community nearly eliminate disparities throughout the second wave in fall 2020.