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

Fred Hutchinson Cancer Research Center, Seattle WA

Investigators

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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 in?uenza 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 in?uenza 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 re?ne methods for forecasting strain dynamics of in?uenza virus. Monitoring and forecasting evolution of viral strains is of paramount importance. New antigenic variants of in?uenza 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 in?uenza vaccine. This project aims to re?ne methods to forecast strain dynamics and predict the makeup of the future in?uenza population. This forecasting is especially relevant to in?uenza 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 in?uenza viruses and result in improved vaccine ef?cacy. 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 ef?cient 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. Nextstrain has been instrumental to SARS-CoV-2 genomic epidemiology in the United States and world. Improvements to the accuracy and capabilities of the platform would be well placed.