Rabies kills thousands of people around the world every year but a global target has now been set to eliminate human deaths from dog-mediated rabies by 2030 and large-scale mass dog vaccination programmes are being undertaken around the world working towards this aim. Extensive genomics and bioinformatics research has generated valuable insights into the diversity, emergence and spread of rabies viruses. Building on this background, this PhD will investigate how genomic data can be incorporated into surveillance to inform rabies elimination programmes.

A new data-centric bioinformatics platform will be used to consolidate and curate publicly available Rabies Virus (RABV) sequences and new sequence data from sub-Saharan Africa and South and Southeast Asia generated through an extended collaborative network. Circulating viral diversity will be quantified and the RABV distribution characterized, to identify gaps in need of improved surveillance. Robust genomic metrics will be developed to measure progress made towards rabies elimination. The project will focus on developing tools to support the endgame including variant discrimination, revealing undetected transmission, identifying sources of incursions and visualizing and communicating insights from genomic surveillance to policymakers, practitioners and the general public to inform control effort. The project will involve tailored genomic analyses for specific epidemiological scenarios in partnership with collaborators to directly support ongoing control activities.

The student should have a strong quantitative background, ideally with computational and statistical experience, willingness to learn laboratory and field techniques and a strong motivation for science communication.
The student is expected to gain:
● Proficiency in bioinformatics techniques including sequence alignments, construction of consensus trees, phylodynamic inference and data visualization methods
● Computational and statistical expertise for undertaking computer simulations and statistical inference
● Effective communication and data visualization skills for conveying information from genetic and epidemiological data and to inform other scientists, policy makers, practitioners and the general public