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Bayesian estimation of optimal spatio-temporal units for disease risk. PhD Mathematics


Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Supervisors:
Dr Trevelyan McKinley, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences
Dr Theo Economou, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences

Project description:
Diseases, both infectious and non-infectious, constitute major risks to public health. Accurate quantification of disease risk from available data is therefore vital to inform control policies that aim to understand and mitigate their impacts. From a statistical point-of-view it is a challenging 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). In many cases the spatial and temporal resolution of the available data is higher than the effective scale of the underlying process, and 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 modelling disease risk.
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 choice of spatio-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 with little loss-of-accuracy. 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 predetermined 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 PhD studentship 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. They will be responsible for the development of novel statistical methods for spatio-temporal model fitting and will be exposed to cutting edge statistical methodology. They will develop expertise in computer programming and numerical inference methods, and will learn key skills in science communication, and gain crucial experience in analysing complex, real-life data sets. These generic skills will serve the student well for a wide range of quantitative careers, as well as contributing scientific insights into a series of important real-world problems.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

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