Our team is focused on developing and applying analytical methods to inform data-driven and value-based decisions in clinical care, public health, and global health.
Decision making in these contexts is usually challenged by several factors: 1) decisions that have long-term health and cost consequences beyond the limited period of randomized control trials or observational studies, 2) objectives that are conflicting (e.g., cost containment versus expanded access to care), 3) incomplete data or knowledge that could create substantial uncertainties in projecting the consequences of different decisions, and 4) continuously changing systems that require decisions that are adaptive to system changes. The distinctive feature of our work is that we develop and apply methods to guide decision making in situations where all four challenges are present (e.g., during outbreaks caused by novel pathogens or the spread of drug-resistant infections).
We use a combination of methods in our work including:
- Simulation and mathematical modeling (agent-based, microsimulation/patient-level, and transmission dynamics models)
- Health care decision analysis and cost-effectiveness analysis
- Machine learning and prediction modeling
- Dynamic optimization and reinforcement learning
- Discrete-choice experiments
And we are interested in a variety of clinical, public health, and global health problems such as:
- Optimizing public health responses to infectious threats (e.g., novel viral pathogens and antimicrobial-resistant bacteria).
- Predicting adverse public health and clinical outcomes (e.g., surges in hospitalizations due to COVID-19 and infection with multidrug-resistant tuberculosis)
- Personalizing treatment regimens for the empiric treatment of drug-resistant bacteria infections (in particular, tuberculosis and gonorrhea).