Characterizing the Spatiotemporal Heterogeneity of the COVID-19 Vaccination Landscape

  1. Andrew Tiu
  2. Zachary Susswein
  3. Alexes Merritt
  4. Shweta Bansal

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Abstract

As variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged throughout 2021–2022, the need to maximize vaccination coverage across the United States to minimize severe outcomes of coronavirus disease 2019 (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 integrated Centers for Disease Control and Prevention and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We found that COVID-19 vaccination coverage in the United States exhibits significant spatial heterogeneity at the county level, and we statistically identified spatial clusters of undervaccination, all with foci in the southern United States. We also identified vaccination progress at the county level as variable through summer 2021; the progress of vaccination in many counties stalled in June 2021, and few had recovered by July, with transmission of the SARS-CoV-2 delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classified 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 United States, especially with the continued emergence of highly transmissible SARS-CoV-2 variants.

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  1. Version published to 10.1093/aje/kwac080
  2. Version published to 10.1101/2021.10.04.21263345v2 on medRxiv
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    SciScore for 10.1101/2021.10.04.21263345: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  4. Version published to 10.1101/2021.10.04.21263345v1 on medRxiv