Vaccine and Infectious Disease Division, Fred Hutch
Associate Professor, Herbold Computational Biology Program
Public Health Sciences Division, Fred Hutch
Human Biology Division, Fred Hutch
Dr. Trevor Bedford uses powerful computers and complex statistical methods to study the rapid spread and evolution of viruses, including those that cause COVID-19, influenza, Ebola and Zika. Data gathered from these processes help researchers develop successful strategies for monitoring and controlling infectious diseases. His visual representations of viral family trees are used to show how the fate of dangerous outbreaks is often determined by the genetics of the infectious agent, human behavior and geography. Dr. Bedford has applied these techniques to document the worldwide spread of COVID-19 and of seasonal flu viruses. He is developing models to predict which strains of influenza are likely to be most challenging to humans — data that help inform the crucial early decisions about which strains to include in annual flu shots. He specializes in tracking the evolutionary changes of viruses such as HIV and influenza that use RNA, rather than DNA, to carry their genetic information. RNA viruses are much more prone to rapid mutation, which makes many of them particularly nimble at escaping the human immune system and difficult to stop with vaccines. He co-developed an open-source platform called Nextstrain that provides a continually updated view of publicly available viral genomic data alongside powerful analytic and visualization tools. He is a leading advocate for the immediate release of research analyzing viral evolution during epidemics, fresh information that can make a lifesaving difference.
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Affiliate Associate Professor, Department of Genome Sciences and Department of Epidemiology
University of Washington
Harvard University, 2008, Ph.D. (Biology)
University of Chicago, 2002, B.A. (Biological Sciences)
Computational molecular evolution
Infectious disease epidemiology
Antigenic evolution and immune dynamics
Antigenic cartography to characterize virus diversity and evolution
Phylogeographic methods to quantify virus circulation patterns
Inference of epidemiological parameters from viral sequence data
Methods to assess fitness and predict evolutionary growth or decay across strains
Modeling infectious disease dynamics
Computational Bayesian inference
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