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Learning from the past to predict and reduce the risk of infectious disease pandemics

Project Description

The World Health Organisation reports that infectious diseases cause 63% of childhood deaths and 48% of premature deaths. There is the ongoing risk of epidemics and pandemics that can cause widespread morbidity and mortality (Spanish flu, ebola, SARS, E. coli, Listeria etc).

Infectious diseases can reach humans in many different ways: they can be transmitted between healthy and infected people, through consumption of infected food or water, through contact with animals, etc. The world is massively interconnected enabling people, animals and food to move rapidly between continents along with infectious disease agents.

Tracing the origin and spread of infectious diseases has never been more challenging and more important. The spectacular developments in detection and whole genome sequencing of disease agents as well as the computational power which enables timely processing of big data offers the opportunity to tackle this problem.

For example, the geographical spread of infectious disease is generally insufficient to trace back the labyrinth of possible pathways through which humans become infected. However, combining geographical information on disease spread with information on the genetic evolution of the infectious disease agents has promise [1-3]. Methodologies to achieve this are still in their infancy and this project is an opportunity to make a significant contribution in tackling this critical problem. The project will:

1. Use computer workstations to simulate the spread and evolution of infectious disease agents. Simulations will be based on geographical and whole genome sequence datasets of real pathogens. The simulations will generate virtual histories of their spread and evolution.
2. Use these results to develop methods that explain how epidemics occurred.
3. Use this knowledge to predict future epidemics and to develop and simulate strategies to reduce infectious disease risk.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Applied maths, quantitative biology, bioinformatics, statistics, physics, data science, biology but with expertise in statistics and computing.

The applicants should have an interest in modelling of biological systems. Experience in modelling, computer programming and computational models is not essential but would be beneficial. A numerate background would also be beneficial.


• Apply for Degree of Doctor of Philosophy in Physics
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
• Details of 2 academic referees

Informal inquiries can be made to Dr F Perez-Reche () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

Funding Notes

This project is advertised in relation to the research areas of the discipline of physics/applied mathematics, statistics, bioinformatics and data science. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THESE PROJECTS. Applicants should also be aware that Additional Research Costs of £1,000 per annum are required (above Tuition Fees and Living Expenses) for numerical simulation software.


1] Dudas, G. et al. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544, 309–315 (2017).

[2] Weill, F.-X. et al. Genomic history of the seventh pandemic of cholera in Africa. Science 358, 785–789 (2017).

[3] Franz, E. et al. Phylogeographic analysis reveals multiple international transmission events have driven the global emergence of Escherichia coli O157:H7. Clin. Infect. Dis. (2018). doi:10.1093/cid/ciy919

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