Developing novel phylodynamic modelling methods for forecasting infectious disease outbreak detection and transmission dynamics

Phylodynamic methods utilising genetic and epidemiological data, such as contact-tracing, hot-spot identification and modelling of strain-specific transmission dynamics, can provide a useful way for forecasting infectious disease outbreaks and transmission dynamics of interest. However, information from epidemiological investigations and genome sequences are rarely utilised together in current gold-standard methods used for outbreak assessment (2). We propose to develop novel phylodynamic methods for real-time outbreak detection of RNA-viruses (Sars-CovV-2, Influenza) using contact-tracing and predicted hotspot identification, as well as further developing understanding of transmission dynamics at the strain-specific level in a sustained outbreak over time. Sars-CoV-2 sequence data is sourced from COG-UK and linked to epidemiological patient data from PHE (3). Influenza sequence data is sourced from PHE, including strain-specific samples for Influenza A and B, and linked to epidemiological patient data from 2015 to present (4). Methods will be developed into publicly available R packages for application in future RNA-virus outbreaks. We aim to advance methods for incorporating genome sequence data into real-time forecasting of outbreak detection (5-8). Additionally, by increasing understanding of strain-specific transmission dynamics, we will advance understanding of seasonal infectious disease transmission at local, community and national level, and inform annual vaccine development in the UK (9).