Infectious Disease Forecasting










Similar with numerical weather prediction, operational forecast of infectious disease outbreaks can be realized using dynamical models in conjunction with data assimilation techniques. In my research, I develop computational methods to advance real-time forecasts of infectious disease spread, with a particular focus on the spatial transmission of influenza, dengue and COVID-19. I also address the problem of optimizing surveillance networks for respiratory diseases. See related works published in PNAS, Nature Communications (2017), PLoS Computational Biology and Nature Communications (2021).


Human Behavior and Disease Transmission










Next-generation mathematical models of respiratory diseases require better representation of human behavior in order to improve simulation, inference and forecasting accuracy. In an NSF-funded project, we will leverage behavior theories and detailed data to develop behavior-driven epidemic models, study the transmission dynamics of COVID-19 and generate improved forecasting systems. These studies will promote the integration of mathematical, epidemiological, and behavioral sciences to deepen understanding about behavior-disease interaction.


Control of Antimicrobial Resistant Pathogens













Antimicrobial resistant (AMR) pathogens remain a major cause of healthcare associated infections (HAIs) worldwide. The increasing prevalence of emerging AMR agents continues to impose a heavy burden on global healthcare systems. In light of this situation, control of AMR infections in hospital becomes a pressing issue. To address this problem, I develop mathematical models at various scales to simulate transmission of AMR pathogens in healthcare facilities, and use those models to design better targeted intervention against HAIs. My research particularly focuses on the interactions between community and nosocomial transmission, as well as asymptomatic carriage of AMR pathogens. Related works were published in PNAS and eLife. The study on identifying asymptomatic spreaders of MRSA is highlighted by Nature's Research Highlight.