Inflammation-mediated diseases present a global healthcare challenge, causing a significant adverse impact on individuals’ quality of life and a substantial economic strain on healthcare systems.

The Inflammation and Immunity Research Driver Programme will explore inflammation and immunity as general underpinning mechanisms, initially focusing on highly prevalent respiratory and allergic diseases. These conditions can be exacerbated by acute inflammatory episodes due to viral infections and environmental factors such as pollution, tobacco, pollen, weather, drugs, foods stinging insects etc.

Respiratory conditions still unnecessarily blight and claim the lives of far too many people in the UK and globally.  We aim to utilise the UK’s outstanding health and health-related data asset, and work with members of the public, colleagues and partners across the UK, to provide key policy and clinical insights that will improve respiratory outcomes for the UK’s population. We will then take the insights from these experiences in respiratory medicine and use these to catalyse similar improvements for other inflammatory and immune-mediated conditions.”  Professor Sir Aziz Sheikh, Professor of Primary Care Research & Development and Director of the Usher Institute at The University of Edinburgh


This is a fantastic opportunity to improve the quality of data recording and use of data to better respiratory outcomes for people in the UK and to expand this learning to other diseases which will ultimately be included in the Inflammation and Immunity driver programme.
Professor Jennifer Quint, Professor of Respiratory Epidemiology at Imperial College London

    • Develop whole-system capacity to map the epidemiology, healthcare utilisation and outcomes for common allergic and respiratory conditions for each of the UK nations in near real time.
    • Map variations in care processes and health outcomes and identify opportunities for reducing these inequalities in the short- and medium-term
    • Develop and validate next generation risk prediction algorithms (e.g., incorporating genetic, pollution, meteorological, wearable data and using machine learning-based approaches) to improve health outcomes and reduce inequalities.
    • Evaluate the effectiveness, cost-effectiveness and impact of these algorithms through large-scale UK-wide clinical trials.
    • Share outputs, such as the risk prediction algorithms, capacity mapping and effectiveness methodology within the community of practice for data science and connect this work with wider global respiratory research efforts to knowledge share