Leveraging very high-resolution satellite data and machine learning for mapping and risk assessment of schistosomiasis

Schistosomiasis is a neglected tropical disease affecting over 240 million people worldwide, with severe socio-economic and public health consequences, particularly in low-income regions. Transmission is closely linked to environmental factors, including water quality, temperature, and vegetation cover, which influence the distribution of freshwater snails—the intermediate hosts of the parasite. However, the complexity of these interactions makes disease mapping and targeted interventions challenging.

This PhD project will leverage very high-resolution (VHR) satellite imagery, such as multi-spectral data (e.g., PlanetScope), thermal data (e.g., HotSat1), and climate datasets, combined with machine learning (ML) techniques, to advance the detection and risk assessment of schistosomiasis. By integrating remote sensing data with epidemiological and hydrological models, the project aims to identify critical environmental factors influencing disease transmission hotspots.

Key objectives include:

• Developing an automated data integration pipeline incorporating environmental variables like water temperature, flow velocity, and vegetation indices.
• Creating advanced ML and epidemiological models that link ecological dynamics to disease transmission.
• Generating dynamic risk maps that track seasonal and temporal variations, providing actionable insights for public health interventions.

This interdisciplinary research, combining geospatial analysis, epidemiology, and artificial intelligence, will contribute to scalable, data-driven tools for disease control. The findings will support public health agencies in designing targeted intervention strategies, with broader applications for other waterborne diseases and global health challenges.

Open to any UK or international candidates. Up to 30% of our UKRI funded studentships can be awarded to candidates paying international rate fees.

You will need to meet the minimum entry requirements for our PhD programme.

Desired qualifications and experience:

• A first-class or upper second-class honours degree (or equivalent) in Environmental Science, Remote Sensing, GIS, Computer Science, Data Science, or a related discipline.
• A Master’s degree in a relevant field is desirable but not essential.
• Strong skills in geospatial analysis, machine learning, and remote sensing techniques.
• Experience with programming languages such as Python or R for data processing and analysis.
• Understanding of epidemiology, waterborne diseases, or environmental health would be advantageous.
• Demonstrated ability to conduct independent research, analyse large datasets, and work across interdisciplinary teams.

Excellent communication and scientific writing skills, with a motivation to contribute to impactful research addressing global health challenges.