Mui Pham

Mui Pham

she/her

Postdoctoral Research Associate @ University of Utah | formerly: Postdoc at Harvard T.H. Chan School of Public Health and Ph.D. at UMC Utrecht, The Netherlands | Mathematical and statistical modeling of infectious diseases | AMR | Genomics

Research

My research sits at the intersection of infectious disease epidemiology, quantitative modeling, and genomics. I develop statistical and mechanistic models to understand how infections spread and how interventions can be designed to stop them, drawing on large-scale clinical, genomic, and environmental datasets.

For a full list of publications, please see my Google Scholar profile.

Antibiotic Use and Resistance

Understanding the relationship between antibiotic use and resistance emergence is essential for designing effective stewardship interventions.

Using clinical and genomic data from the US Veterans Affairs Healthcare System (comprising approximately 1 million patients across 138 medical centers) and the Calgary Health Region (approximately 25,000 isolates), I have characterized nationwide trends in infection incidence and antimicrobial resistance, and developed statistical models to assess how antibiotic prescribing drives selection for resistance in major bacterial pathogens.

Selected publications:

Environmental Exposures and Infectious Disease Transmission

A growing pillar of my research investigates how environmental factors shape infectious disease dynamics. In my current position at the University of Utah, I am developing panel regression models to quantify the relationship between ambient air pollution and tuberculosis incidence using Brazil’s national TB surveillance data (2003–2024). This work integrates meteorological, air quality, and epidemiological datasets across a country with enormous geographic and socioeconomic diversity, aiming to disentangle the contribution of environmental exposures from other structural determinants of TB.

Selected publications:

Transmission Dynamics and Intervention Modeling

A central thread across my work is using mathematical and statistical models to inform infection prevention and control. During the COVID-19 pandemic, I developed agent-based and compartmental models that directly informed policy: an agent-based model of SARS-CoV-2 transmission in Dutch secondary schools supported re-opening strategies for the Netherlands Ministry of Education, while statistical models quantifying the burden of hospital-acquired COVID-19 contributed to the UK’s Scientific Advisory Group for Emergencies (SAGE). Earlier modeling work examined how self-imposed behavioral changes and government-imposed social distancing jointly shaped epidemic trajectories.

Beyond COVID-19, my PhD research focused on nosocomial transmission of Pseudomonas aeruginosa in intensive care units, combining surveillance data with transmission models to quantify routes of spread, including environmental contamination after patient discharge. More recently, I have applied genomic epidemiology to distinguish local transmission from importation of SARS-CoV-2 within the National Basketball Association, in collaboration with Yale and Nebraska.

Selected publications: