Influenza vaccine strain selection with an AI-based evolutionary and antigenicity model
Wenxian Shi, Jeremy Wohlwend, Menghua Wu, Regina Barzilay
Abstract
Current vaccines provide limited protection against rapidly evolving viruses. For example, Centers for Disease Control and Prevention estimates show that the overall influenza vaccine effectiveness against outpatient illness in the United States averaged below 40% between 2012 and 2021. Moreover, the clinical outcomes of a vaccine can be assessed only retrospectively. Here we propose an in silico method named VaxSeer that predicts the antigenic match of vaccine candidates with circulating viruses, in the context of the viruses’ relative dominance in the future influenza season. Based on 10 years of retrospective evaluation using sequencing and antigenicity data, our approach consistently selects strains with better empirical antigenic matches to circulating viruses than annual recommendations. Finally, our predicted estimate of antigenic match exhibits a strong correlation with influenza vaccine effectiveness and reduction in disease burden, highlighting the promise of this framework to drive the vaccine selection process. By matching antigenicity prediction with a forecast of circulating strains in the next season, a model is shown to outperform standard recommendations for vaccine design in terms of vaccine effectiveness and disease burden in multiple evaluations throughout different influenza seasons.