Project
Statistical and computational approaches to enable real-time phylodynamic inference
The current wealth of sequence data for most important infectious diseases is stretching current computational approaches, such as phylodynamic inference, to their practical limits. This project aims to develop statistical and computational approaches to extract timely epidemiological and evolutionary information from virus genome sequences during an ongoing epidemic. Reconstructing pathogen spread from genetic data as they become available during an epidemic represents a statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an ‘online’ fashion. The project’s goal is hence to augment existing phylodynamic analyses – an area of research that deals with how epidemiological, immunological, and evolutionary processes act and potentially interact to shape viral phylogenies - with novel data to drastically shorten the process of updating results when new data comes in, using a combination of novel statistical and computational techniques. To this end, I will extend a widely-used Bayesian analysis framework to ensure widespread adoption of the developed methods and will prepare the field with novel tools for the next outbreak.