Predicting influenza H3N2 vaccine efficacy from evolution of the dominant epitope

TL;DR

This paper introduces a model that predicts influenza H3N2 vaccine efficacy by measuring antigenic distance through amino acid changes in dominant epitopes, aiding vaccine strain selection.

Contribution

The study presents a novel antigenic distance-based model for predicting vaccine efficacy, improving candidate vaccine selection for influenza H3N2.

Findings

Predicted 2016-2017 vaccine efficacy was 19%, close to the observed 20%.

The model provides a useful tool for predicting human protection against circulating strains.

It offers a quantitative approach to guide vaccine strain selection.

Abstract

We predict vaccine efficacy with a measure of antigenic distance between influenza A(H3N2) and candidate vaccine viruses based on amino acid substitutions in the dominant epitopes. In 2016-2017, our model predicts 19% efficacy compared to 20% observed. This tool assists candidate vaccine selection by predicting human protection against circulating strains.