ReviewTrends in microbiology2018
Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.
Review in Trends in microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
89 citing papers in PubMed.
- Nextstrain automates real-time phylogenetic analysis of open data for endemic and emerging pathogens.Bioinformatics (Oxford, England) · 2026Article
- Strain-specific differences in the response to egg-derived versus recombinant protein influenza vaccines.Journal of virology · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Practical phylogenetic usage of theoretical advances in distance-based tree learning.BMC bioinformatics · 2026Article
- V- and VNature communications · 2026Article
- Strain-specific differences in the response to egg-derived versus recombinant protein influenza vaccines.bioRxiv : the preprint server for biology · 2026Article
- Predicting the antigenic evolution of seasonal influenza viruses using phylogenetic convergence.bioRxiv : the preprint server for biology · 2026Article
- Structural vaccinology expedites rational design of next-generation vaccines for influenza and respiratory syncytial virus.Virologica Sinica · 2026Review
- A CNN-Based Model of Cross-Immunity to Influenza A(H3N2) Virus: Testing Under "Real-World" Conditions.Viruses · 2026Article
- CAPYBARA: A generalizable framework for predicting serological measurements across human cohorts.PLoS computational biology · 2026Article
- Article
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- Article
- High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains.bioRxiv : the preprint server for biology · 2025Article
- Influenza vaccine strain selection with an AI-based evolutionary and antigenicity model.Nature medicine · 2025Article
- Article
- Article
- The role of artificial intelligence in detecting avian influenza virus outbreaks: A review.Open veterinary journal · 2025Review
- How immunity shapes the long-term dynamics of influenza H3N2.PLoS computational biology · 2025Article
- Concepts and Methods for Predicting Viral Evolution.Methods in molecular biology (Clifton, N.J.) · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
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Registered trials
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.