Speaker
Abstract: Influenza vaccination is our best defense against seasonal epidemics, yet vaccine effectiveness can dramatically vary between individuals. Age, exposure history, genetic background, and socio-environmental factors all shape immune responses, but current vaccine recommendations rarely account for this diversity. Using longitudinal serological and clinical data from thousands of individuals spanning infancy to old age, we apply computational modeling and causal inference to map how immunity evolves across the human lifespan. These analyses reveal modifiable drivers of vaccine response in distinct age groups and highlight strategies for personalized, adaptive vaccination. This work lays the groundwork for integrating individualized predictions into clinical practice and public health policy to improve influenza protection for all.
Bio: Dr. Einav is a computational biologist who develops sophisticated machine learning algorithms and biophysical models to understand how human antibodies neutralize deadly viruses. With a multidisciplinary background spanning theoretical physics, mathematics, and biophysics, he leverages vast immunological datasets to create personalized medicine approaches that predict individual vaccine responses. His research aims to transform one-size-fits-all vaccination into precision medicine by establishing computational "laws of the immune system" that could provide stronger, longer-lasting immune protection.
Zoom Information: https://duke.zoom.us/j/97489470596
Meeting ID: 974 8947 0596
This seminar series is organized by the Multiscale Immune Systems Modeling (MISM) Center of Excellence, funded by NIAID/NIH (U54AI191253).
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