Statistical and computational approaches to enable real-time phylodynamic inference

The increasing amount of sequence data for most important infectious diseases is stretching current computational approaches, such as phylogenetic inference, to their practical limits. This project aims to develop statistical and computational approaches for pathogen phylodynamics to extract timely epidemiological and evolutionary information from virus genome sequences during an epidemic. Reconstructing pathogen spread from genetic data as they become available during an epidemic represents a common statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an ‘online’ fashion. The proposed project’s goal is to augment existing phylodynamic analyses with novel data to drastically shorten the process of updating results when new data comes in, using a combination of novel statistical approaches to incorporate this data and parallel computing techniques to drastically improve the overall efficiency of inference algorithms.

Keywords:Phylodynamics, Infectious diseases, Parallel computing, Bayesian inference, Transition kernels

Disciplines:Microbiology, Systems biology, Laboratory medicine