# Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation

> **NIH NIH R35** · FRED HUTCHINSON CANCER CENTER · 2022 · $418,074

## Abstract

Project Summary
Viral pathogens are an enduring threat to global public health. This project aims to use viral genomic data
to improve understanding of ongoing virus evolution and to make actionable inferences to reduce the global
burden of viral infectious disease. In order to be relevant for public health interventions, analyses of viral
sequence data need to be incredibly rapid, both in terms of computation and in terms of dissemination. To
accomplish these goals, this project will create novel methodological tools to analyze evolutionary dynamics
from influenza genetic sequence data and to analyze transmission patterns from outbreak sequence data.
Over the current project period (2016-2021), we developed a real-time analysis platform called Nextstrain,
which provides up-to-date analyses for a variety of pathogens including influenza virus, Ebola virus, Zika
virus, dengue virus, mumps virus, tuberculosis and SARS-CoV-2. Bioinformatic pipelines developed through
Nextstrain are reusable by academic groups and public health labs and resulting analyses are shareable via the
website nextstrain.org.
In the upcoming project period (2021-2026), we will refine methods for forecasting strain dynamics of influenza
virus. Monitoring and forecasting evolution of viral strains is of paramount importance. New antigenic variants
of influenza that partially escape from prior human immunity emerge and rapidly sweep through the viral
population. Such strains are less susceptible to vaccine-derived immunity and so antigenic evolution results in
the need to frequently update the seasonal influenza vaccine. This project aims to refine methods to forecast
strain dynamics and predict the makeup of the future influenza population. This forecasting is especially
relevant to influenza vaccine strain selection, as a vaccine strain is chosen for the Northern Hemisphere in
February for deployment the following winter. Accurate projections will aid in vaccine match for seasonal
influenza viruses and result in improved vaccine efficacy. Technical innovations focus on extending models to
work across different viruses, different gene segments and to incorporate spatial dynamics.
In an outbreak scenario such as the West African Ebola epidemic, the American Zika epidemic or the SARS-
CoV-2 pandemic, the focus of public health interventions focus on early diagnosis, contact tracing, isolation and
treatment. Epidemiological understanding of transmission dynamics is of paramount importance to outbreak
response. Viral genomic data can reveal otherwise hidden transmission patterns and aid in efficient contact
tracing. Geographic spread is especially amenable to genomic inferences. This project will develop tools to
make epidemiological inferences from outbreak sequence data. These methods will continue to be deployed
via the Nextstrain platform, allowing epidemiologists throughout the world to analyze their own datasets.
Genomic epidemiology has the potential to truly inform outbreak response. N...

## Key facts

- **NIH application ID:** 10397121
- **Project number:** 5R35GM119774-08
- **Recipient organization:** FRED HUTCHINSON CANCER CENTER
- **Principal Investigator:** Trevor BC Bedford
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $418,074
- **Award type:** 5
- **Project period:** 2016-08-23 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10397121

## Citation

> US National Institutes of Health, RePORTER application 10397121, Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation (5R35GM119774-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10397121. Licensed CC0.

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