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The Biostatistics, Bioinformatics and Epidemiology Program (BBE) pursues innovative and advanced quantitative science in collaboration with investigators in the Vaccine and Infectious Disease Division and around the globe to accomplish our ambitious objective of eliminating disease and death attributable to infection. We drive scientific discovery in statistical, computational and mathematical modeling, along with epidemiologic methods, to confront the burden of HIV, tuberculosis, SARS-CoV-2, malaria, influenza, dengue, Ebola and cancer-related infectious diseases worldwide.
BBE's 40-plus faculty members collaborate with scientists across VIDD and the globe to study the spread and containment of infectious diseases in populations, lead the statistical design of early-to-late-phase clinical trials of vaccines and other interventions, optimize analyses of complex immunological and virological data, and advance statistical methodology and computational methods.
A hallmark of BBE is the simultaneous pursuit of excellence in both methodological and applied science. Faculty play central roles in interdisciplinary research programs, shaping the scientific direction of these fields and routinely crafting novel quantitative methods to answer complex questions that arise in ongoing studies.
Faculty and Labs
BBE faculty are leaders in multiple quantitative disciplines — including biostatistics, machine learning, bioinformatics, computational biology and mathematical modeling — and apply these approaches across the full spectrum of infectious disease research. Their work spans scales from molecular and single-cell systems to tissues, organisms and populations, integrating data and methodology to advance translational and population-level impact.
News
See the latest news about our program scientists, research, grants and discoveries.
Featured Lab: Inferring Efficacy of the Active Control in HIV Prevention Trials
Dr. Fei Gao leads research to overcome the challenge faced when it is no longer ethical to include a placebo arm in clinical trials that assess the efficacy of HIV prevention methods. Her work improving clinical trial design has the potential to increase adherence to and accessibility of long-acting injectable PrEP options. See Fred Hutch HIV/AIDS Research Projects.
Holly Janes, PhD
Dr. Holly Janes is a biostatistician researching infectious disease prevention with particular expertise in HIV prevention and vaccine science. She has special interest in the prevention of vertical transmission of HIV from mother to infant. Her research focuses on vaccine evaluation and statistical design of late-phase vaccine trials.
Ollivier Hyrien, PhD
Dr. Ollivier Hyrien is a statistician and computational biologist. His group develops and applies advanced quantitative approaches, ranging from statistical and machine learning methods to stochastic modeling and optimization, to address scientific questions arising from laboratory experiments and vaccine studies. Much of his current research supports the development of treatments against diseases caused by pathogens such as HIV, malaria, SARS-CoV-2 and herpes simplex virus (HSV), as well as cancer.
Laina D. Mercer, PhD
Dr. Laina D. Mercer is the associate director of the Statistics and Data Science Unit for the Infectious Diseases Clinical Research Consortium (IDCRC), a protocol statistician with the HIV Prevention Trials Network (HPTN), and the director of Statistical Science and senior staff scientist in the Brown Lab. Her research focuses on the design and analysis of clinical trials, the application of statistical models to clinical, surveillance and survey data, and development of novel clinical assays.
Bryan Mayer, PhD
Dr. Bryan Mayer is the scientific co-director for the Vaccine & Immunology Statistical Center (VISC), which supports the Collaboration for AIDS Vaccine Discovery (CAVD), and a Senior Staff Scientist in the Hyrien Lab. His research interests include analysis of immune responses to vaccine candidates, study design for challenge studies, statistical methods for biological assays, mathematical models of viral load kinetics and pharmacokinetic/pharmacodynamic (PK/PD) modeling.