Bayesian Estimation of Optimal Spatio-Temporal Units for Disease Risk - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2886

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr. Trevelyan McKinley
Dr Theo Economou

Penryn Campus, Cornwall

Project Description
Accurate quantification of infectious disease risk from available data is vital for mitigating the associated effects and for planning accordingly. From a statistical point-of-view it is an inference problem: estimating the underlying probability of contracting the disease from noisy data and predicting it under various potentially unobserved conditions (e.g. in locations where no data are available). However, in many cases the spatial and temporal resolution of the available data is higher than the effective scale of the underlying process. The choice of spatio-temporal unit can have a marked effect on the fitting and interpretation of statistical models, rendering some current algorithms infeasible. 

The aim of this project is to develop novel Bayesian methods for optimally aggregating spatial/temporal units, where the level of aggregation is effectively treated as unknown and thus determined as part of the modelling. The project is strongly multidisciplinary, and potentially applicable to a wide range of real-world problems, but here we focus on disease risk modelling. The key aim is accurate and efficient estimation of not only the degree of spatio-temporal risk, but also the optimal level of complexity and structure required in the spatial and temporal units. Areas of the space that have negligible background risks should be amalgamated to form larger regions, thus reducing the computational burden of the models. Insights from these models can be used to produce predictions or inform control policies or targeted surveillance/interventions. 

Various data sets are available for testing the models to be developed. One such data set involves the occurrence of dengue fever and Zika in the city of Rio de Janeiro in Brazil, where data are available at the finest spatio-temporal resolution possible, i.e. spatial locations of households and actual timings of when a case of dengue was recorded. Analysing the data at this level can be very expensive computationally so the goal would be to estimate the optimal aggregation unit at which the analysis should be performed. For instance, should the data be spatially aggregated to a pre-determined spatial unit such as the post code or is there a more optimal spatial configuration that is a mixture of small units (e.g. households) and larger ones (e.g. neighbourhoods). 

The studentship is funded for 3.5 years, and would suit a student with a background in mathematics/statistics or other quantitative subject, who is interested in working on important real-world problems. Additionally, the student will attend the Academy for PhD Training in Statistics (APTS), in order to obtain a rounded view of modern statistical methods.

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Mathematics or Statistics. Experience in Bayesian modelling and/or spatio-temporal modelling is desirable.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email:
Project-specific queries should be directed to the supervisor.

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• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.