Harnessing epidemiological and genomic data for understanding of respiratory virus transmission at multiple scales

Modern large-scale genetic and epidemiological data offers a potential revolution in our understanding of the transmission of viral respiratory pathogens particularly if appropriate methods can be developed and applied to combine information sources and extract the necessary scientific insights. The requirement for such a revolution was illustrated by the ubiquity of more traditional analyses to inform our response to the COVID-19 pandemic. Delivering advances in understanding of respiratory pathogens would in turn drive major improvements in the public health policies designed to mitigate the heavy burden respiratory infections placed on individuals and health and social care services. The epidemiology of such pathogens is driven by a largely unobserved process of community infection, and we propose to make particular use of the ONS COVID-19 Infection Survey (CIS), a very large (500,000 participant) longitudinal (2-year) household cohort study, which has now passed 10 million visits. We propose a set of interlinked analyses of CIS and other datasets, developing advanced genomic, data science and modelling methodology, to disentangle routes of transmission, to understand relationship between the community and other settings, and ultimately to inform policy on control of respiratory viruses.