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

> **NIH NIH R35** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $401,525

## Abstract

Project Summary
Viral pathogens are an enduring threat to global public health. This project aims 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. These
methods will result in a real-time analysis platform, realized via the website nextstrain.org, that provides
constantly up-to-date analyses of a variety of viruses, including influenza virus, Ebola virus and Middle East
respiratory syndrome coronavirus (MERS-CoV). This website would provide public health officials and other
stakeholders an intuitive view of ongoing viral evolution and help to pinpoint targeted interventions.
In the case of influenza, monitoring antigenic 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 develop
tools to characterize circulating antigenic phenotypes from genetic and serological assay data and to develop
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. All predictions will be made in a public
fashion on the website nextstrain.org, allowing wide distribution and rapid dissemination to public health
officials.
In an outbreak scenario such as the 2014–2015 West African Ebola epidemic or the 2013–2015 MERS-CoV
outbreak, the focus of public health interventions shifts from vaccination to early diagnosis, contact tracing,
isolation and treatment. 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 inferences will be
deployed on the website nextstrain.org in real-time, allowing field epidemiologists to put samples into the
great epidemic context and understand the transmission history leading to the case at hand. Such a system
stands to make a real contribution to global public health and outbreak respo...

## Key facts

- **NIH application ID:** 9932391
- **Project number:** 5R35GM119774-05
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Trevor BC Bedford
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $401,525
- **Award type:** 5
- **Project period:** 2016-08-23 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9932391

## Citation

> US National Institutes of Health, RePORTER application 9932391, Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation (5R35GM119774-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9932391. Licensed CC0.

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