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My main research interest lies in developing, evaluating, and applying methods for improving our understanding of infectious disease dynamics in real-time. I am committed to doing science in the open, and collaboratively, with the aim of producing useful and actionable output. Most of my recent work has been targeted towards the COVID-19 response but my underlying focus is pathogen agnostic sparse data settings.

My current main areas of work are developing and evaluating methods for nowcasting right truncated data, developing and evaluating methods to forecast and understand variant dynamics, reconstructing unobserved infections from a range of data sources (such as count data and prevalence measures), and developing methods for the estimation of the effective reproduction number, the growth rate, and generation interval distribution as well as use cases for these estimates and understanding their interactions.