SARS-CoV-2 evolution on a dynamic immune landscape

TLDR

Since the pandemic began, many SARS‑CoV‑2 variants have emerged with extensive spike protein evolution, likely driven by antibody‑mediated immune evasion, creating a dynamic immune landscape shaped by local infection history. The study aimed to develop a mechanistic model to predict the variant‑specific relative number of susceptible individuals over time. The model integrates deep mutational scanning data, antibody pharmacokinetics, and regional genomic surveillance. The model accurately reproduced historical variant dynamics, predicted future trends, explained global differences, and indicates that evolving population immunity governs variant transmissibility and fitness, enabling regional risk assessment and informing vaccine design.

Abstract

Abstract Since the onset of the pandemic, many SARS-CoV-2 variants have emerged, exhibiting substantial evolution in the virus’ spike protein 1 , the main target of neutralizing antibodies 2 . A plausible hypothesis proposes that the virus evolves to evade antibody-mediated neutralization (vaccine- or infection-induced) to maximize its ability to infect an immunologically experienced population 1,3 . Because viral infection induces neutralizing antibodies, viral evolution may thus navigate on a dynamic immune landscape that is shaped by local infection history. Here we developed a comprehensive mechanistic model, incorporating deep mutational scanning data 4,5 , antibody pharmacokinetics and regional genomic surveillance data, to predict the variant-specific relative number of susceptible individuals over time. We show that this quantity precisely matched historical variant dynamics, predicted future variant dynamics and explained global differences in variant dynamics. Our work strongly suggests that the ongoing pandemic continues to shape variant-specific population immunity, which determines a variant’s ability to transmit, thus defining variant fitness. The model can be applied to any region by utilizing local genomic surveillance data, allows contextualizing risk assessment of variants and provides information for vaccine design.