Harnessing complex sparse data for the phylodynamics of RNA virus emergence and elimination

The emergence and elimination of viruses are critical junctures for disease control to prevent spread or resurgence. Rapid response is essential, but policymakers are invariably held up by a lack of data. Phylodynamics can provide rapid insights where traditional epidemiology may fail, but has yet to be applied effectively to more epidemiologically or ecologically complex and data-limited contexts. I will extend the use of phylodynamic methods by developing frameworks to estimate viral transmission where data are spatiotemporally limited. I will 1) develop a framework for using spatially heterogenous data, using phylodynamic methods to infer transmission in locations with no epidemiological or genomic data, aiding intervention design and providing an evidence base for further data collection. I will 2) use novel phylodynamic applications to contextualise ongoing transmission and spillover risk when historical data are sparse, including developing the use of phage-display immunoprecipitation sequencing (PhIP-Seq) for estimating historical viral genetic diversity from contemporary serum samples to fill in temporal gaps in our genomic datasets. Overall, I will develop strategies to fully harness contemporary genomic data to provide information critical for the early prevention of further spread of (re-)emerging viruses, and closure of transmission chains of viruses under elimination.