Prediction of Influenza B Vaccine Effectiveness from Sequence Data

TL;DR

This paper presents a method to predict influenza B vaccine effectiveness by measuring antigenic distance from viral sequence data, aiding in timely vaccine updates.

Contribution

A novel sequence-based antigenic distance measure that correlates with vaccine effectiveness over multiple years.

Findings

The measure correlates well with historical vaccine effectiveness (1979-2014).

It can inform vaccine strain selection and update timing.

Potential to improve influenza B vaccine design.

Abstract

Influenza is a contagious respiratory illness that causes significant human morbidity and mortality, affecting 5-15% of the population in a typical epidemic season. Human influenza epidemics are caused by types A and B, with roughly 25% of human cases due to influenza B. Influenza B is a single-stranded RNA virus with a high mutation rate, and both prior immune history and vaccination put significant pressure on the virus to evolve. Due to the high rate of viral evolution, the influenza B vaccine component of the annual influenza vaccine is updated, roughly every other year in recent years. To predict when an update to the vaccine is needed, an estimate of expected vaccine effectiveness against a range of viral strains is required. We here introduce a method to measure antigenic distance between the influenza B vaccine and circulating viral strains. The measure correlates well with effectiveness of the influenza B component of the annual vaccine in humans between 1979 and 2014. We discuss how this measure of antigenic distance may be used in the context of annual influenza vaccine design and prediction of vaccine effectiveness.