Trevor Bedford, PhD
Professor, Biostatistics, Bioinformatics and Epidemiology Program
Vaccine and Infectious Disease Division, Fred Hutch
Professor, Herbold Computational Biology Program
Public Health Sciences Division, Fred Hutch
Member
Pathogen-Associated Malignancies Integrated Research Center (PAM IRC), Fred Hutch
Dr. Trevor Bedford specializes in using viral genomics and computational modeling to track, predict and combat infectious disease outbreaks. His work spans a wide range of pathogens — including influenza, Ebola, Zika, dengue, mpox, and SARS-CoV-2 — with a focus on translating data into real-world public health action. As co-founder of Nextstrain, an open-source platform for real-time viral surveillance, he helped revolutionize how viral genomic data is used to respond to outbreaks. These new approaches have improved influenza vaccine selection, helped contain Ebola epidemics and mapped SARS-CoV-2 evolution. He also led the Seattle Flu Study, which discovered community transmission of COVID-19 in February 2020, reshaping pandemic response. Through open science and data-driven approaches, he champions strategies to better anticipate and manage global health threats.
For Media Relations inquiries please email media@fredhutch.org
Other Appointments & Affiliations
Member, Translational Data Science Integrated Research Center (TDS IRC), Fred HutchMember
Translational Data Science Integrated Research Center (TDS IRC), Fred Hutch
Affiliate Investigator
Human Biology Division, Fred Hutch
Howard Hughes Medical Institute Investigator
Howard Hughes Medical Institute
Affiliate Professor, Department of Genome Sciences and Department of Epidemiology
University of Washington
Education
Harvard University, 2008, PhD (Biology)
University of Chicago, 2002, BA (Biological Sciences)
Research Interests
Computational molecular evolution
Phylogenetics
Infectious disease epidemiology
Antigenic evolution and immune dynamics
Bayesian statistics
Current Projects
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
Teaching/Mentoring Interests
Sequence analysis
Modeling infectious disease dynamics
Computational Bayesian inference