# Integrative prediction of seasonal influenza evolution by genotype, phenotype, and geography

> **NIH NIH F31** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $33,989

## Abstract

Project Summary/Abstract
The rapid evolution of seasonal influenza requires the development of a new influenza vaccine by the World
Health Organization (WHO) every one to two years. This evolution occurs through a process of antigenic drift
where amino acid mutations in the hemagglutinin (HA) surface protein allow currently circulating viruses to
evade adaptive immunity against previous vaccine viruses. Therefore, globally successful seasonal influenza
viruses are often antigenically distinct from previous lineages. High-quality experimental assays for antigenic
drift are laborious and low-throughput, leading researchers to develop computational models that can predict the
success of influenza viruses from HA sequence data alone. Since the publication of these original sequence-
only models in 2014, there have been significant advances in influenza virology and computational methods
that could benefit influenza predictive models. Specifically, there are now computational methods to measure
antigenic drift by accurately inferring missing measurements in HI assays, high-throughput mutagenesis assays
to measure functional constraints on mutations in HA, research supporting the importance of proteins other than
HA for influenza's fitness, and detailed analysis of influenza's variable geographic circulation. I propose to create
a new predictive model of influenza evolution that integrates these modern, biologically-informed fitness metrics
into a single framework. These new metrics will build on dense, high-quality HI assays from collaborators at
the Centers for Disease Control and Prevention (CDC), deep mutational scanning assays of seasonal influenza
from collaborators in Dr. Jesse Bloom's lab, a curated database of whole genome sequences for influenza, and
empirical estimates of influenza's global migration rates. This new predictive model will improve the accuracy
of predictions about which viruses are most likely to succeed in future influenza seasons. These improved
predictions will inform recommendations by Dr. Bedford to the WHO at annual vaccine design meetings and,
thereby, effect improvements in vaccine efficacy and reduce influenza-related morbidity and mortality in human
populations.

## Key facts

- **NIH application ID:** 9882872
- **Project number:** 5F31AI140714-02
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** JOHN HUDDLESTON
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $33,989
- **Award type:** 5
- **Project period:** 2019-04-01 → 2020-12-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9882872

## Citation

> US National Institutes of Health, RePORTER application 9882872, Integrative prediction of seasonal influenza evolution by genotype, phenotype, and geography (5F31AI140714-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9882872. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*