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

Every time someone coughs or sneezes, a chain of invisible transmission events begins—and pathogen genomes carry the hidden record of that chain. This project builds new computational methods to read that record from large-scale genetic sequence data, revealing who is infecting whom. Current outbreak control struggles because transmission is rarely observed directly. Pathogen genomes accumulate mutations over time, so genetically similar sequences often come from people who infected each other. The researcher will develop a statistical framework that uses clusters of these close sequences to estimate how often transmission occurs between different age groups—for example, from schoolchildren to elderly relatives. The method will be tested on SARS-CoV-2, seasonal influenza, and dengue virus. If successful, this work will give public health agencies a practical tool to identify which age groups drive outbreaks and to estimate how well vaccines block onward transmission. It does not require new laboratory equipment or clinical trials—just better analysis of data already being collected. This is fundamental methodological research, but similar advances in pathogen genomics have already transformed how we track foodborne illness and hospital outbreaks. The same approach could eventually help target interventions during future epidemics, from school closures to vaccine prioritisation, without waiting for hospitals to fill.