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Real-time reconstruction of epidemic dynamics from viral phylogenies using Deep Learning

CORDIS · observation · Publication date unknown

Real-time reconstruction of epidemic dynamics from viral phylogenies using Deep Learning Reconstructing epidemic dynamics in real-time has become crucial for effective disease management, as demonstrated by the COVID-19 pandemic. Traditional methods rely on epidemiological data (e.g., reported cases), which can be biased or incomplete due to variable testing policies, particularly in resource-limited settings. Instead, phylodynamics has emerged as a valuable toolkit for using viral phylogenies to understand epidemic dynamics. However, conventional phylodynamic methods rely on mathematical formulas and approximations, which are not scalable to large datasets and are time-consuming, limiting their use primarily to retrospective rather than real-time analysis. This proposal aims to transform phylodynamics by integrating it with deep learning to bypass the cumbersome likelihood calculations, thereby facilitating real-time analysis directly from sequence data. I wi

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR