The pEpitope Model Quantifies a Novel Antigenic Distance to Predict Influenza Vaccine Effectiveness

Though influenza affects millions of individuals worldwide each year, vaccination has been shown to prevent hospitalizations, lessen severe illness outcomes, and reduce the public health burden. Minimizing the antigenic differences between the vaccine virus and the circulating strains ensures the vaccine will adequately prime the immune system against infection. We developed a theory of the human antibody response to influenza infection following vaccination that produced a robust measure of antigenic distance, called the pEpitope model. We calibrated this model with A(H3N2) epidemiology data over the past 47 years, the past decade, and in recent studies from the US Centers for Disease Control and Prevention Influenza Vaccine Effectiveness Network; the coefficients of determination with vaccine effectiveness were 0.75, 0.78, and 0.92, respectively. Our analysis of A(H3N2) strains circulating between September 2017-May 2018 identified the emergence of a new quasispecies cluster that is sufficiently distant from the 2018-2019 vaccine. The pEpitope model is a valuable tool that enhances influenza vaccine virus selection and development.