Overview
The faculty in this area develop mathematical models and advanced statistical methods to understand disease transmission, predict outbreaks, and evaluate interventions that protect public health. This group combines rigorous quantitative approaches with real-world applications—from tracking emerging pandemic threats andmodeling vaccineeffectiveness to identifying genetic and environmental risk factors for autoimmune and infectious diseases. Research spans the development of mechanistic transmission models for respiratory and vector-borne pathogens, causal inference methods for evaluating public health interventions, real-time outbreak analytics using phylodynamics and machine learning, and genetic epidemiology approaches to understanding complex diseases like multiple sclerosis and other autoimmune conditions.
The impact ranges from informing COVID-19 response strategies and antimicrobial resistance control to advancing pandemic preparedness through broad-spectrum vaccine modeling and novel pathogen surveillance systems. By integrating machine learning, causal inference, and epidemiological theory with collaborations spanning Kaiser Permanente, CDC, WHO, and public health agencies worldwide, our researchers generate actionable insights that directly shape disease prevention policies and outbreak response at local, national, and global scales. With Berkeley's strengths in biostatistics, computational biology, and public health, this work translates cutting-edge analytics into evidence-based strategies that save lives and strengthen health systems against current and future biological threats.
Primary Faculty
- Lisa Barcellos, Professor, Division of Epidemiology, School of Public Health
- Michael Boots, Professor, Department of Integrative Biology
- Alan Hubbard, Professor, Division of Biostatistics, School of Public Health
- Joseph Lewnard, Associate Professor, Division of Epidemiology, School of Public Health
- Charles Whittaker, Assistant Professor, Division of Infectious Diseases and Vaccinology, School of Public Health