ArticlePLoS computational biology2020
Early prediction of antigenic transitions for influenza A/H3N2.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- VirPLM: antigenic prediction of influenza A/H3N2 viruses with a fine-tuned protein language model.Bioinformatics (Oxford, England) · 2026Article
- Leveraging synthetic and genetic data to improve epidemic forecasting.PLoS computational biology · 2026Article
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- Modeling Influenza Antiviral Strategies: Reducing Burden and Preventing Resistance.The Journal of infectious diseases · 2025Article
- Synthetic method of analogues for emerging infectious disease forecasting.PLoS computational biology · 2025Article
- Influenza A genomic diversity during human infections underscores the strength of genetic drift and the existence of tight transmission bottlenecks.Virus evolution · 2024Article
- Predicting host-based, synthetic lethal antiviral targets from omics data.NAR molecular medicine · 2024Article
- Predicting host-based, synthetic lethal antiviral targets from omics data.bioRxiv : the preprint server for biology · 2023Article
- SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity.EBioMedicine · 2023Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Forecasting emergence of COVID-19 variants of concern.PloS one · 2022Article
- A changing trend in diagnostic methods of Influenza A (H3N2) virus in human: a review.3 Biotech · 2021Review
- Evaluation of Next-Generation H3 Influenza Vaccines in Ferrets Pre-Immune to Historical H3N2 Viruses.Frontiers in immunology · 2021Article
- Modeling the selective advantage of new amino acids on the hemagglutinin of H1N1 influenza viruses using their patient age distributions.Virus evolution · 2021Article
- Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality.The Journal of cell biology · 2020Article
- Article
- The pandemic COVID-19: a tale of viremia, cellular oxidation and immune dysfunction.The Pan African medical journal · 2020Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate--which is a function of mutational load and population susceptibility to the cluster--along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.
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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.