Real-time pathogen phylodynamics

With high-throughput molecular sequencing, genomic data from pathogenic viruses are becoming available in unprecedented quantities and with remarkable speed, even in in resource-limited settings, aided by portable genome sequencing technology. However, the wealth of sequence data for most important infectious diseases is stretching current computational approaches, such as phylogenetic inference, to their practical limits. By developing and implementing novel statistical and computational approaches into the popular BEAST software package, allowing for worldwide dissemination and adaptation, I plan to drastically reduce the computational burden associated with analysing pathogen data during an epidemic. To this end, a first series of approaches will focus on incorporating novel genomic data into ongoing phylogenetic and phylodynamic analyses, which will avoid having to restart such analyses whenever new data become available (which is still the current predominant approach). The key aim is to avoid wasting precious time by augmenting an ongoing analysis in a clever way in use cases where data become available in a continuous manner, for example during an epidemic or pandemic. This will pave the way for faster analysis as an outbreak unfolds, leading to a much faster understanding of how an epidemic is progressing.

Although targeted at rapidly evolving viruses, the envisioned developments in my research can be applied to other areas of research, for example to genome sequencing projects where incremental analyses - as a result of new data becoming available - need to be paired with a reduction in the time-to-results by exploting previously available inference results.