Our broad goals are to leverage mathematical, statistical, and computational modeling to minimize disease transmission and the emergence of antimicrobial resistance in healthcare facilities and other workplaces. We are particularly interested in MRSA, Clostridioides difficile, and SARS-CoV-2 infections.
Current areas of interest include:
- Optimizing the clinical and public health utility of computational tools for determining the risk of acquiring healthcare associated infections, antimicrobial resistance, or severe infection.
- Incorporating patient movement and pathogen exposure data to improve ability to elucidate individual transmission links and quickly identify clusters of infection.
- Developing and validating transmission models for MRSA and C. difficile to evaluate the effectiveness of interventions designed to reduce nosocomial colonization and disease transmission.
- Elucidating the relationship between antimicrobial stewardship efforts and the emergence of antimicrobial resistance.
- Probing the relationship between community and healthcare transmission of antimicrobial resistant infections.
- Using electronic health record data to predict clinical trajectory of COVID-19 cases, risk of disease complications, and benefit of treatment.
- Quantifying the relative contributions of factors driving inequity of COVID-19 outcomes such as infectious exposure, comorbid conditions, hospital care, and post-hospital care.
- Evaluating strategies for COVID-19 surveillance and mitigation of outbreaks in healthcare facilities, congregate facilities, and workplaces.
MINDSCAPE @ UCSF 