Bayesian spatial statistics for small area estimation of HIV

This research aims to develop Bayesian methods for sub-national modelling and inference of HIV in sub-Saharan Africa.
Accurate sub-national estimates are needed to inform the targeted policy interventions required to reduce HIV disease burden and end the epidemic.

There are three key objectives of this research.
The first is to create models for spatial random effects which take a more realistic view of space.
The second objective is to develop new inference methods which extend the integrated nested Laplace approximation to models outside the latent Gaussian class.
Lastly, this research aims to advance a Bayesian approach to survey design which will make use of the small area estimation model to achieve greater sample efficiency.

This project falls within the healthcare technologies and mathematical sciences EPSRC research areas.

The primary CDT impact will be training 75 PhD graduates as the next generation of leaders in statistics and statistical machine learning. These graduates will lead in industry, government, health care, and academic research. They will bridge the gap between academia and industry, resulting in significant knowledge transfer to both established and start-up companies. Because this cohort will also learn to mentor other researchers, the CDT will ultimately address a UK-wide skills gap. The students will also be crucial in keeping the UK at the forefront of methodological research in statistics and machine learning.
After graduating, students will act as multipliers, educating others in advanced methodology throughout their career. There are a range of further impacts:
- The CDT has a large number of high calibre external partners in government, health care, industry and science. These partnerships will catalyse immediate knowledge transfer, bringing cutting edge methodology to a large number of areas. Knowledge transfer will also be achieved through internships/placements of our students with users of statistics and machine learning.
- Our Women in Mathematics and Statistics summer programme is aimed at students who could go on to apply for a PhD. This programme will inspire the next generation of statisticians and also provide excellent leadership training for the CDT students.
- The students will develop new methodology and theory in the domains of statistics and statistical machine learning. It will be relevant research, addressing the key questions behind real world problems. The research will be published in the best possible statistics journals and machine learning conferences and will be made available online. To maximize reproducibility and replicability, source code and replication files will be made available as open source software or, when relevant to an industrial collaboration, held as a patent or software copyright.