Inferring infectious disease dynamics: Inverse problems for outbreak response and control
Using data and mathematical models to uncover hidden disease transmission, improve forecasting, surveillance, risk assessment, and outbreak control.
Mathematical and computational models provide a powerful framework for understanding infectious disease dynamics, but their usefulness depends on our ability to infer hidden transmission processes from incomplete, noisy, and heterogeneous observations. This talk will frame infectious disease modeling as an inverse problem: using surveillance, mobility, genomic, clinical, and behavioral data to estimate epidemiological parameters, reconstruct unobserved dynamics, and support outbreak response and control.
Drawing on applications in respiratory outbreaks and healthcare-associated infections, Sen will illustrate how mechanistic models and modern statistical inference can extract actionable information from limited observations and improve forecasting, surveillance, risk assessment, and intervention design.
Speaker
Dr Sen Pei
Dr Sen Pei is an Assistant Professor in the Department of Environmental Health Sciences at Columbia University Mailman School of Public Health. Trained in applied mathematics, network science, and complex systems, he combines data-driven mathematical modeling with statistical inference to better understand, predict, and prepare for recurrent and emerging infectious disease outbreaks.
He has published more than 100 peer-reviewed articles, with work appearing in leading scientific journals. He is also the principal investigator of multiple projects funded by the NIH, CDC, and NSF. His recent research focuses on emerging and recurrent respiratory outbreaks and the transmission of antimicrobial-resistant pathogens in healthcare settings.
Event notices
- Please note that you can join this event in person or you can join the session remotely
- Please note that the recording link will be listed on this page when available
Admission
Contact