Speaker
Description
Since the onset of the pandemic, many SARS-CoV-2 variants have emerged, exhibiting substantial evolution in the virus' spike protein, the main target of neutralizing antibodies. A plausible hypothesis proposes that the virus evolves to evade antibody-mediated neutralization to maximize its ability to infect an immunologically experienced population. While virus infection induces neutralizing antibodies, viral evolution may thus navigate on a dynamic immune landscape that resulted from the infection history in different regions. Global inequalities in vaccine distribution and differences in infection-prevention measures have shaped this global immunological landscape early on during the pandemic, resulting in uneven geographic distributions of SARS-CoV-2 variants. Consequently, predicting which variant will spread within particular regions has become increasingly challenging.
To tackle this challenge, we developed a comprehensive mechanistic model of the dynamic immunological landscape of SARS-CoV-2. We utilized deep-mutational scanning data and antibody pharmacokinetics to compute time-dependent cross-neutralization between arbitrary variants. Combined with infection history and molecular surveillance data, we could predict the variant-specific relative number of susceptibles over time. This quantity precisely matched historical variant dynamics, predicted future variant dynamics in all investigated countries, and could explain their global differences. While the model contains only one global calibration parameter; adaptation of the model to different geographical contexts solely required to adapt the molecular surveillance data to the region of interest. Our work strongly supports the hypothesis that SARS-CoV-2 evolution is driven by escape from humoral immunity, allows contextualizing risk assessment of variants without the use of laborious neutralisation data, and provides important clues for vaccine design.
