ArticleNature2014
A predictive fitness model for influenza.
Article in Nature, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 264 papers.
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Who cites it
264 citing papers in PubMed.
- Predictive evolutionary genomics: principles, validation, and practice.The New phytologist · 2026Review
- Near real-time data on the human neutralizing antibody landscape to influenza virus in summer of 2026 shows antigenic advance of H3N2 subclade K region D mutants and H1N1 D.3.1.1 Sa mutants.bioRxiv : the preprint server for biology · 2026Article
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Article
- Reply to: Limited evidence of AI superiority in seasonal influenza vaccine strain selection.Nature medicine · 2026Article
- Limited evidence of AI superiority in seasonal influenza vaccine strain selection.Nature medicine · 2026Article
- Predicting the antigenic evolution of seasonal influenza viruses using phylogenetic convergence.bioRxiv : the preprint server for biology · 2026Article
- Forecasting Multitrait Resistance Evolution under Antibiotic Stress.Molecular biology and evolution · 2026Article
- Mapping the mutational landscape of an avian retrovirus envelope protein across its evolutionary trajectory.PLoS pathogens · 2026Article
- Article
- Near real-time data on the human neutralizing antibody landscape to influenza virus as of early 2026 to inform vaccine-strain selection.bioRxiv : the preprint server for biology · 2026Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.bioRxiv : the preprint server for biology · 2026Article
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.Virus evolution · 2026Article
- Near real-time data on the human neutralizing antibody landscape to influenza virus as of early 2026 to inform vaccine-strain selection.Virus evolution · 2026Article
- Article
- High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains.bioRxiv : the preprint server for biology · 2025Article
- Quantifying plasmid movement in drug-resistant Shigella species using phylodynamic inference.PLoS pathogens · 2025Article
204 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.