VaxSeer: Selecting influenza vaccine strains through evolutionary and antigenicity models

Current vaccines provide limited protection against rapidly evolving viruses. For example, the influenza vaccine's effectiveness has averaged below 40% for the past ten years. Today, clinical outcomes of vaccine effectiveness can only be assessed retrospectively. Prospective estimation of their effectiveness is crucial but remains under-explored. In this paper, we propose an in-silico method named VaxSeer that predicts expected vaccine effectiveness by considering both the future dominance of circulating viruses and antigenic profiles of vaccine candidates. Based on ten years of historical WHO data, our approach consistently selects superior strains than the annual recommendations. Finally, the prospective score we propose exhibits a strong correlation with retrospective vaccine effectiveness and reduced disease burden, highlighting the promise of this framework in driving the vaccine selection process. Predictions from our model for the 2024 and future winter seasons are available at https://wxsh1213.github.io/vaxseer.github.io/.