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A predictive fitness model for influenza
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A predictive fitness model for influenza
A predictive fitness model for influenza
Journal Article

A predictive fitness model for influenza

2014
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Overview
The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage. A computational approach for predicting the future evolution of the human influenza virus, based on population-genetic data of previous strains, is presented; this model holds promise for improving vaccine strain selection for seasonal influenza. Keeping tabs on influenza virus evolution Marta Łuksza and Michael Lässig present a computational approach for predicting the future evolution of influenza virus. The authors develop a fitness model for the influenza haemagglutinin protein, based on population-genetic data from all previous strains, that allows them to predict the future evolution of currently existing clades. This computational model holds promise for improving vaccine strain selection.

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