Computational Modeling of Infectious Disease Transmission and Emergence
About
Dr. Blumberg develops and applies data-driven computational models of infectious disease spread and clinical trajectory, with particular attention to diseases of subcritical transmission — those where each case generates, on average, fewer than one further case. A central goal is to identify the risk factors that tip the balance between disease quiescence and emergence, and to quantify what patient-specific or population-wide interventions actually accomplish.
His methods have been applied to neglected tropical diseases, zoonoses, vaccine-preventable diseases, and antimicrobial resistance. As an attending physician on the hospital medicine and infectious disease inpatient services, he brings a practical clinical perspective to which investigations and metrics can have immediate impact on guidelines and public health.
Education & Training
BSc, Engineering & Applied Science, 1997 — California Institute of Technology
MD, PhD, Biophysics, 2008 — University of Michigan
Internship, Internal Medicine, 2009 — Iowa Methodist Medical Center
Postdoctoral Fellow, RAPIDD Program, 2012 — NIH Fogarty International Center
Residency, Internal Medicine, 2017 — St. Mary's Medical Center, San Francisco
Fellowship, Infectious Disease, 2019 — NYU Langone Medical Center
Mentoring
Dr. Blumberg welcomes motivated, collaborative-minded trainees. Work in his group suits those with programming experience who are drawn to mathematical, computational, or statistical modeling of disease transmission and clinical progression. He is available to mentor UCSF students, residents, and fellows on research. Example projects are listed at the MINDSCAPE group site.
Key Collaborators
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UCSF Profile & Full Publication List
Research Areas: Mathematical Modeling, Computational Epidemiology, Disease Emergence, Disease Elimination, Healthcare-Associated Infections, Antimicrobial Resistance, Neglected Tropical Diseases
Research Projects
Trachoma Hotspots & the Elimination Endgame
Annual mass drug administration of azithromycin has been remarkably effective at preventing blindness from trachoma, yet progress has fallen short of the World Health Organization's elimination goals. In countries such as Ethiopia, infection persists in problem districts after more than a decade of treatment. Reaching global control requires identifying those districts, detecting hotspots within them, and tailoring treatment strategies accordingly — work this group supports with mathematical and statistical models.
Forecasting trachoma control and identifying transmission hotspots →
Emerging zoonoses such as mpox and Ebola persist through repeated introductions from an animal reservoir rather than sustained human transmission. Subsequent spread between people may be limited, but superspreading and differential transmission within high-risk groups can amplify the impact of each spillover. The group has developed methods for judging whether an isolated cluster poses a risk of endemic spread, and for locating the transmission heterogeneity that makes targeted control possible.
Inference of R₀ and transmission heterogeneity from stuttering chains →
Respiratory Viruses in Congregate Settings
Modeling how effectively mitigation reduces spread in high-risk populations, and building tools to assess whether an individual COVID-19 patient is likely to progress to severe disease. The group is particularly interested in how socioeconomic status and self-identified race shape clinical outcomes, and in evaluating surveillance and preventive measures for reducing SARS-CoV-2 transmission in congregate settings such as prisons.
Modeling scenarios for mitigating outbreaks in congregate settings →
Healthcare-Associated Infections
Using mathematical modeling and machine learning to build decision-making tools that improve risk assessment, prevention, and control of healthcare-associated infections. The approach accounts for spatial and temporal dynamics, gives clinicians continuous real-time feedback, and stays robust as risk factors and prevalence shift over time. Efforts concentrate on two of the most consequential pathogens: methicillin-resistant Staphylococcus aureus and Clostridioides difficile.
Modeling transmission of pathogens in healthcare settings →
Predicting Clinical Deterioration in Inpatients
Evaluating what role continuous biometric sensing can play in giving clinicians actionable warning that a hospitalized patient is deteriorating — early enough for the prediction to change what happens next.
Early identification of hospitalized patients at risk of deterioration →
Full publication list at UCSF Profiles.