The impact of background genetic diversity in inferences of SARS-CoV-2 transmission and selection (360G-Wellcome-228319_Z_23_Z)

The virus SARS-CoV-2 is the cause of the COVID-19 pandemic. At any point during the pandemic, there is a range of SARS-CoV-2 viruses with different mutations spreading: the background diversity. I will use the Office for National Statistics' COVID-19 infection Survey (ONS-CIS) genomic data to examine how changing levels of background diversity affects the methods we use to infer transmission when we have two linked sequences; to do this I will focus on using phylogenetics (tree-based methods). For example, if the background diversity is high, we have more confidence that two sequences from the same household that are highly similar represent a transmission event, compared to the same two sequences against a low diversity background. Additionally, I will examine methods for determining whether two sequences from the same individual represent a persistent infection or reinfection, taking into account changing background diversity. Finally, I aim to investigate selection, examining whether conflicts occur in which a mutation provides an advantage during infection (within-host) host but becomes disadvantageous during transmission (between host). This project will allow us to examine current methods and evaluate their suitability at different background diversity levels, and will have implications for the optimal use of genomics in other infections.