Novel methods to characterise the determinants of infectious disease transmission from large pathogen sequence datasets, and inform their practical implementation.

Understanding the determinants of pathogen transmission is critical for outbreak control. The unobserved nature of transmission events makes this challenging. Pathogen sequencing can help elucidate transmission patterns, but novel methods are required to manage modern large-scale genome datasets and achieve their potential. Because mutations accrue over time in pathogen genomes, genetically close sequences are informative about transmission events as they are sequenced from epidemiologically linked individuals. I will develop methods leveraging these proximal sequences to characterise transmission between groups. I will build and validate a novel inference framework to estimate the matrix of mixing between age groups from the size and composition of clusters of genetically proximal sequences. Using this framework, I will investigate the role played by different age groups in SARS-CoV-2, seasonal influenza and dengue transmission. I will additionally explore how sampling schemes and sample size impact inferences. Finally, I will demonstrate the use of genetically proximal sequences to estimate vaccine effectiveness against transmission. This work will be applicable across pathogens and transmission determinants. By providing new models to characterise disease transmission from an underutilised data source, this will constitute a critical contribution to inform future epidemic response.