EPSRC Hub for Quantitative Modelling in Healthcare

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.

The Hub will serve as a focal point that brings together a wide-range of end users with the most appropriate for their needs academic expertise available at the University of Exeter in order to deliver our impact strategy. We will engage directly with clinical partners, the wider research community and industry, as well as outreach to patient and the wider public, and via capacity building on the interface of mathematics and healthcare. The activities in WP3 will in particular provide mechanisms for user engagement driven by user needs and priorities that align with the Hub Research Themes.

* NHS: The Hub will create impact for the NHS in terms improved delivery of services that is of interest to health sector managers and policy makers. Quantitative Modelling (QM) in Healthcare involving dynamic models of complex disease along with uncertainty quantification for personalised prognosis and diagnosis could result in significant cost savings to the NHS. The novelty of the Hub is that it endeavours to open new avenues of application of QM in healthcare for much wider range of problems and applications.

* Clinicians: The Hub will to create impact for clinicians by collaborative development of tools that use of quantitative models for personalising diagnosis and predicting treatment responses or outcomes. Multiple health conditions and healthcare providers could benefit from this approach, via applying cutting edge research in a clinical setting that may lead to earlier, more cost effective and more reliable diagnosis. The Hub will promote this through its network of basic researchers and clinical partners through via WP3. For example, researchers at Exeter and clinicians from RD&E are interested in quantifying the link between personal interventions and ongoing metabolic (e.g. microbiomes and endocrine function) health based on monitoring key bio indicators such as lipids or cholesterol.

* Patients: The Hub aims to develop quantitative tools for health management at personalised level. We will pursue active engagement with patients' groups such as the Lived Experience Group at the Mood Disorder Centre. Specifically we will work with patient representatives to develop appropriate public involvement and engagement activities by providing a fresh perspective and encourage researchers to think about the societal problems their research is addressing; commenting on language used in public facing communications; helping prepare for public presentations or outreach events.

* Industry: There is a real potential to incorporate quantitative models including uncertainty quantification into both new and existing healthcare technologies, i.e. into technology and tools for areas such as mental health management. There is further potential for the quantitative models and associated uncertainty quantification tools developed within the Hub to deliver precision medicine technologies for metabolic health and novel antimicrobial therapies.

* Public outreach: Healthcare is an issue that affects all of us at some time as patients or carers and unsurprisingly there is a high level of public interest and newsworthiness of new developments that may revolutionise aspects of healthcare. What is less familiar is the role of mathematics in this, and the Hub will provide a forum to educate on aspects of this.

* Impact via research team training: The Hub will support the training of two postdoctoral researchers and at least 4 PhD students through joint University and industry support. This will include both generic and specific training at the core of mathematics for healthcare. In the longer term this approach will increase the number of highly trained researchers working on the interface between mathematics and healthcare, and so build capacity to ensure that an increasingly data-rich area of healthcare science will pull through to impacts.