SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity

Lauren J Beesley; Kelly R Moran; Kshitij Wagh; Lauren A Castro; James Theiler; Hyejin Yoon; Will Fischer; Nick W Hengartner; Bette Korber; Sara Y Del Valle

doi:10.1016/j.ebiom.2023.104534

SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity

EBioMedicine. 2023 May:91:104534. doi: 10.1016/j.ebiom.2023.104534. Epub 2023 Mar 31.

Authors

Affiliations

¹ Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA. Electronic address: lvandervort@lanl.gov.
² Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA.
³ Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁴ Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁵ Space Data Science and Systems, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁶ Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁷ Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA; The New Mexico Consortium, Los Alamos, NM, USA.

Abstract

Background: Throughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread.

Methods: In this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 16 SARS-CoV-2 variant waves/transitions for 230 countries and sub-country regions, between October 2020 and January 2023. We then clustered geographic locations in terms of their variant behavior across several Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition.

Findings: This work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions.

Interpretation: These results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date.

Funding: Laboratory Directed Research and Development (LDRD), Los Alamos National Laboratory.

Keywords: COVID-19; GISAID; SARS-CoV-2; Variant transition.

MeSH terms

COVID-19* / epidemiology
COVID-19* / prevention & control
Humans
Pandemics
Retrospective Studies
SARS-CoV-2* / genetics

Supplementary concepts

SARS-CoV-2 variants

Grants and funding

R37 AI150590/AI/NIAID NIH HHS/United States