Prof Eric Morgan (Queens University Belfast), Prof Adam Kleczkowski (University of Strathclyde), Paul Wagstaff (Self-Help Africa), Rob Strey (PEAT GmbH/Plantix).

This project aims to enhance existing systems for monitoring small ruminant health on smallholder farms in Africa by integrating real-time health information with predictive models of parasite transmissions, providing actionable advice on antiparasitic interventions. The project will apply ML to identify the most informative health indicators and the most efficient monitoring strategies. Climate-driven predictions of parasite transmission potential will be incorporated, allowing monitoring and actions to be calibrated to epidemiological risks. The key output will be a smartphone app, providing this capability to farmers and advisors. Additionally, the project will explore aligning the app with comparable risk prediction tools for plant health, helping farmers and others to identify and mitigate multiple threats to food security through simultaneous impacts on crops and animals.

Climate change and drug resistance are posing real issues for farmers, threatening productivity and the sustainability of livestock farming. Problems are especially acute in areas such as Africa, where disease burden is highest and options for control most limited. The project will augment existing systems for monitoring small ruminant health on smallholder farms in Africa by integrating real-time health information with predictive models of parasite transmission – and delivering actionable advice on antiparasitic interventions.

The project will draw on existing and new datasets, applying machine learning to identify the most informative health indicators and the most efficient and effective monitoring strategies. Then, climate-driven predictions of parasite transmission potential will be added so that monitoring and action can be calibrated to epidemiological risks. The key output will be a smartphone app to provide this capability to farmers and advisors, with whom the app will be co-produced. Finally, the project will explore the potential to align the app with comparable risk prediction tools for plant health, supporting farmers and others to identify and head of multiple threats to food security through simultaneous impacts on crops and animals.

The appointed student will benefit from training in machine learning, app development, animal health and epidemiology. They will work closely with stakeholders, including in Africa, to co-develop and deliver the app and apply it in the field, learning from user experiences to optimize design and delivery. They will emerge with cutting-edge skills and experiences in digital health that are in strong demand in research, NGO, public and private sectors.