Pandemic Preparedness and Response

Emerging infectious diseases often spread rapidly before sufficient data are available to characterize their transmission dynamics. This creates major challenges for early detection, situational awareness, forecasting, and intervention design. Our group develops data-driven modeling and inference methods to support pandemic preparedness and response under conditions of uncertainty, sparse data, and rapidly changing epidemiological conditions.

Our work in this area focuses on reconstructing early spatial spread (Zhang et al. PNAS 2026), detecting cryptic transmission (Pei et al. Nature 2021), estimating key epidemiological features of novel pathogens (Li et al. Science 2020), and evaluating the impact of interventions (Pei et al. Sci. Adv. 2020). We combine mechanistic epidemic models with mobility data, surveillance records, serological data, genomic information, and statistical inference to understand how emerging pathogens move through populations and across geographic regions.

A major goal of this research is to develop generalizable computational tools that can be applied early in future outbreaks. These tools are designed to help identify where transmission is occurring, how quickly a pathogen is spreading, which data streams are most informative, and what interventions may reduce transmission. This work spans respiratory viruses and other emerging infectious disease threats.