This project aims to develop computational methods tailored for genomic epidemiology, addressing the limitations of current tools designed for evolutionary biology.

By leveraging the similarity of closely related sequences typical in pandemic scenarios, such as COVID-19, the project will create algorithms thousands of times more efficient than existing ones, capable of analysing millions of genome sequences. These improvements will enhance the accuracy of tracking pathogen evolution, transmission histories, and identifying variants of concern. The project encompasses developing a software library for integration with other analysis tools and enabling pandemic-scale analyses through Bayesian phylogenetics within the BEAST package. This initiative aligns with priorities in global health, infections and immunity, antimicrobial resistance, and data science, promising to significantly advance our response to ongoing and future pandemics.

Nick Goldman (EMBL-EBI)