Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution
- University of Washington, United States
- Centers for Disease Control and Prevention (CDC), United States
- The Francis Crick Institute, United Kingdom
- The Francis Crick Insitute, United Kingdom
- National Institute of Infectious Diseases, Japan
- National Instituite of Infectious Diseases, Japan
- Peter Doherty Institute for Infection and Immunity, United States
- Peter Doherty Institute for Infection and Immunity, Australia
- University of Basel, Switzerland
- Fred Hutchinson Cancer Research Center, United States
Abstract
Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence- only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.
Data availability
Sequence data are available from GISAID using accession ids provided in Supplemental File S1.Source code, derived data from serological measurements, fitness metric annotations, and resulting fitness model performance data are available in the project's GitHub repository (https://github.com/blab/flu-forecasting).Raw serological measurements are restricted from public distribution by previous data sharing agreements.
Article and author information
Author details
Pierre Barrat-Charlaix
Funding
Cancer Research UK (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
National Institute of Allergy and Infectious Diseases (U19AI117891-01)
- Trevor Bedford
National Institute of Allergy and Infectious Diseases (R01AI127893-01)
- Pierre Barrat-Charlaix
- Richard A Neher
- Trevor Bedford
Medical Research Council (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
Wellcome (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
Ministry of Health, Labour and Welfare (10110400)
- Seiichiro Fujisaki
- Kazuya Nakamura
- Noriko Kishida
- Shinji Watanabe
- Hideki Hasegawa
Japan Agency for Medical Research and Development (JPfk0108118)
- Shinji Watanabe
Australian Government Department of Health
- Ian Barr
- Kanta Subbarao
National Institute of Allergy and Infectious Diseases (F31AI140714)
- John Huddleston
National Institute of General Medical Sciences (R35GM119774-01)
- Trevor Bedford
Pew Charitable Trusts
- Trevor Bedford
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Vaughn S Cooper, University of Pittsburgh, United States
Publication history
- Received: June 17, 2020
- Accepted: August 24, 2020
- Accepted Manuscript published: September 2, 2020 (version 1)
- Version of Record published: October 13, 2020 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution
eLife 9:e60067.
https://doi.org/10.7554/eLife.60067
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