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Details of Grant 

EPSRC Reference: EP/T017856/1
Title: EPSRC Hub for Quantitative Modelling in Healthcare
Principal Investigator: Tsaneva-Atanasova, Professor KT
Other Investigators:
Hattersley, Professor A Challenor, Professor P Gudelj, Professor I
Dhillo, Professor WS Ashwin, Professor P Beardmore, Professor RE
Wedgwood, Dr K C A Gow, Professor N
Researcher Co-Investigators:
Project Partners:
Brain in Hand Brainbow Limited Certus Technology Associates Ltd
Devon Partnership NHS Trust First Databank Europe Ltd IP Pragmatics
Ludger Ltd Nanyang Technological University North Bristol NHS Trust
Royal Devon and Exeter NHS Fdn Trust SW Academic Health Science Network Taunton & Somerset NHS Foundation Trust
The Alan Turing Institute University of Sydney
Department: Mathematics
Organisation: University of Exeter
Scheme: Standard Research
Starts: 01 September 2020 Ends: 31 August 2024 Value (£): 1,231,618
EPSRC Research Topic Classifications:
Mathematical Analysis Mathematical Aspects of OR
Medical science & disease Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Dec 2019 Hubs for Mathematical Sciences in Healthcare Deferred
11 Feb 2020 Hubs for Mathematical Sciences in Healthcare Interviews Announced
Summary on Grant Application Form
Our Hub brings together a team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new quantitative methods for applications to diagnosing and managing long-term health conditions such as diabetes and psychosis and combating antimicrobial infections such as sepsis and bronchiectasis. This approach is underpinned by the world-leading expertise in diabetes, microbial communities, medical mycology and mental health concentrated at the University of Exeter. It uses the breadth of theoretical and methodological expertise of the Hub's team to give innovative approaches to both research and translational aspects.

Although quantitative modelling is a well-established tool used in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. However, up to now it has been restricted to health economics in the context of healthcare services and systems management. Applications to develop future therapies, optimising treatments and improving community health and care are in its infancy. This is due to a number of challenges from both mathematical (methodological) as well as clinical and patients' perspectives. Our Hub approach will allow us to develop novel statistical and mathematical methodologies of relevance to our clinical and industrial partners, informed by relevant patient groups. Building this new generation of quantitative models requires that we advance our mathematical understanding of the effective network interaction and emergent patterns of health and disease. Clinical translation of mathematical and statistical advances necessitates that we further develop robust uncertainty quantification methodology for novel therapy, treatment or intervention prediction and evaluation.

NHS long-term planning aspires to deliver healthcare that is more personalised and patient centred, more focused on prevention, and more likely to be delivered in the community, out of hospital. Our Hub will contribute to this through developing mathematical and statistical tools needed to inform clinical decision making on a patient-by-patient basis. The basis of this approach is quantitative patient-specific mathematical models, the parameters of which are determined directly from individual patient's data.

As an example of this, our recent research in the field of mental health has revealed that movement signatures could be used to distinguish between healthy subjects and patients with schizophrenia. This hypothesis was tested in a cohort of people with schizophrenia and we developed a quantitative analysis pipe line allowing for classification of individuals as healthy or patients. The features used for classification involving data-driven models of individual movement properties as well as measures of coordination with a virtual partner were proposed as a novel biomarker of social phobias. To validate this in an NHS setting, we have recently carried out a feasibility study in collaboration with the early intervention for psychosis teams in Devon Partnership Mental Health Trust. The success of this study could significantly advance the early detection of psychosis by enabling diagnosis using novel markers that are easily measured and analysed and improve accuracy of diagnosis.

Indeed, personalised quantitative models hold the promise for transforming prognosis, diagnosis and treatment of a wide range of clinical conditions. For example, in diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.
Key Findings
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Organisation Website: http://www.ex.ac.uk