Characterizing the spatiotemporal heterogeneity of the COVID-19 vaccination landscape

Abstract

As variants of SARS-CoV-2 have emerged through 2021-2022, the need to maximize vaccination coverage across the United States to minimize severe outcomes of COVID-19 has been critical. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrate CDC and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We find that COVID-19 vaccination coverage in the US exhibits significant spatial heterogeneity at the county level and we statistically identify spatial clusters of undervaccination, all withfoci in the southern US. We also identify vaccination progress at the county level as variable through summer 2021; many counties stalled in vaccination into June 2021 and few recovered by July, with transmission of the Delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classify vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the US, especially with the continued emergence of highly transmissible variants.