CORDIS Project
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This project seeks to revolutionize epidemic modeling by integrating phylodynamics with deep learning. It aims to provide real-time analysis of viral epidemiology, enhancing public health responses by utilizing sequence data to estimate key epidemiological metrics.
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 mathem…
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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