Evaluating the effectiveness of phylogenetics for infectious disease control

Phylogenetic analysis provides a tool to quantify infectious disease dynamics by leveraging the information contained in genetic sequence data to infer epidemic spread. When pathogen sequences are routinely generated via drug resistance testing – as is the case in many countries for HIV – these data can be analysed using phylogenetic analysis to provide a unique insight into real time epidemic spread.

With the advent of pre-exposure prophylaxis and intensive test-and-treat policies, HIV elimination has become possible. However, to achieve elimination, we will increasingly require new methods to detect and control clusters of transmission. Real time phylogenetic analysis may provide one such method. However, there is scant evidence on whether incorporating phylogenetic analysis within an infectious disease control programme for HIV elimination is effective.

By working closely with the UK Health Security Agency, this project will develop policy-driven methods to understand the utility of how the UK can effectively use phylogenetic tools to complement the existing HIV elimination strategy.

The project will use an interdisciplinary combination of mathematical or statistical modelling, epidemiology, and phylogenetic analysis. The candidate will develop their quantitative skills using these tools. The student will develop or extend their programming expertise in languages, such as R or Python. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub.

The student will learn to communicate their research through publication in peer-reviewed journals and presentation in scientific conferences. By working closely with experts in public health, sequence data, phylogenetic analysis and mathematical modelling, the student will become comfortable working within an interdisciplinary environment and interacting with a diverse scientific team.

Supervisors:

·      Dr Katherine Atkins, Centre for Global Health, The University of Edinburgh.

·      Prof Sam Lycett, Roslin Institute, The University of Edinburgh.

Requirements

A strong academic track record with a 2:1 or higher in a relevant undergraduate degree, or its equivalent if outside the UK. It is also desirable to have a strong performance in a relevant postgraduate degree. Proven experience in one or more of the following is desirable: mathematical modelling, phylogenetic analysis, advanced statistical modelling, one scientific programming language (e.g. R, C++, Python). The successful candidate will work in a highly interdisciplinary environment and should be able to work independently and as part of a distributed international team.

Following interview, the selected candidate will need to apply and be accepted for a place on the Usher Institute Global Health PhD programme. Details about the PhD programme can be found here: https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2021&id=698

Application procedure

Please provide a CV, a short (300 words maximum) personal statement detailing how your skills and interests match the proposed research, degree certificate(s), marks for your degree(s) and the names of 2 academic references who can be contacted. All documents should be in electronic format and sent via e-mail to: [email protected] with the subject: PhD Application – Atkins. Informal enquiries should be made to [email protected].

The closing date for applications is: Open until filled.