- Assess the links between environmental abiotic variables (chemical and mineralogical) and disease prevalence (podoconiosis), and proximities, in Ethiopia and Cameroon.
- Integrate soil data, locations and characteristics, with multispectral and hyperspectral remotely sensed data for use in podoconiosis mapping. Incorporate the use of machine learning and data assimilation to determine the most accurate soil and health risk classification approaches.
- Develop and validate spatial modelling techniques to provide supporting evidence for the role of soil properties and locations in the local development of podoconiosis with the view to expand model analysis to regional and continental scales.
Background and rationale
Podoconiosis (non-infectious elephantiasis) causes swelling of the foot and lower limbs of affected individuals, occurring in subsistence farming communities in highland areas of tropical Africa, Central America and north-west India. This neglected tropical disease affects 5–10% of the population in some areas, contributing substantial morbidity and resulting in significant productivity loss and the stigmatisation of affected individuals. Although the precise mechanism of disease development is not fully understood, previous research has identified contact with irritant particles in volcanic soils as responsible for disease development.
However, the specific soil components which result in this chronic inflammatory condition have not yet been discovered. The PhD project aims to use epidemiological, geographical and remote sensing methods to assess the links between environmental variables (chemical and mineralogical) and disease prevalence (podoconiosis) in Ethiopia and Cameroon. Building on previous work, interdisciplinary methods, including remote sensing, geostatistics and epidemiological analysis, will be applied to these data for quantitative assessment of the relationships between podoconiosis occurrence and a range of soil properties. This will improve understanding of the specific soil components that contribute to disease development. The application of remote sensing, geostatistics and spatial regression analysis will allow the extrapolation of soil data to provide spatial prediction of potentially irritant soils and, if appropriate, the spatial prediction of podoconiosis prevalence across the study areas through a risk analysis.
This interdisciplinary project will span the disciplines of epidemiology and geography (particularly remote sensing, geostatistics and spatial analysis) so we aim to recruit a student with strong skills in one or both of these areas, preferably with a master’s degree in a relevant subject. The work will involve both epidemiological and spatial modelling techniques, with the ultimate aim of providing evidence regarding soil properties which are involved in the development of podoconiosis. The project outputs will inform the development of improved evidence-based control measures. The successful candidate will join a team of collaborative researchers from Brighton, Lancaster, Southampton, London, Ethiopia and Cameroon, with the opportunity to spend research time at our collaborating institutions.