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Center for Nonlinear Studies |
Genomic epidemiology has become a critical part of the infectious disease modelling toolbox, shedding light on the effects of pathogen evolution on disease transmission. While genomic tools are now routinely used to track the emergence of novel pathogens and strains, their use in forecasting efforts is still developing. In this talk, I present 2 recent projects combining mathematical and statistical modelling with genomic data for prediction of pathogen evolution and resulting disease transmission. The first uses phylogenetic features of influenza viruses to forecast which strains may circulate in upcoming seasons, aiding in vaccine design. The second applies global data, including genomic, epidemiological and demographic features, to predict the relative size of an upcoming COVID-19 wave driven by a new variant. Together, these studies highlight how genomic data can advance our predictive capabilities, to inform public health responses and improve pandemic preparedness.
Bio: Jessica Stockdale is an Assistant Professor in the Department of Mathematics at Simon Fraser University. Her research lies at the intersection of infectious disease modeling and genomic epidemiology, focusing on integrating epidemiological and genomic data to better understand disease transmission. She specializes in developing mathematical and statistical methods, such as Bayesian approaches, to analyze outbreak data and address critical questions in epidemiology and public health.
Host: Sara Del Valle (A-1)