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).

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.

Information Diffusion in Social Networks

As a ubiquitous process in social networks, information diffusion plays a central role in applications ranging from the spread of news and opinions, the propagation of innovations, word-of-mouth viral marketing and change in behaviour to product adoption. In my research, I develop and validate methods to identify superspreaders of information, analyze large-scale social media data to understand the pattern of information diffusion, and use those patterns to guide modeling of information spread. See related works published in Nature Human Behaviour and Scientific Reports.