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.

Hospital neurology and neurophysiology services are increasingly overwhelmed. With a growing and ageing population, the incidence of many brain conditions (such as dementia and epilepsy) are rapidly increasing. Compounded by the COVID-19 pandemic, there are now over 10,000 people in the UK waiting more than a year for an appointment with a neurologist. Things must change! The purpose of our Network is to address these challenges through the development of new technologies that enable diagnosis and management in the community. These services could be provided in a community diagnostic hub, by high-street healthcare professionals, in a GP surgery, in a mobile unit or even in the home environment. Our focus will be on new digital solutions built around neural interfacing, signal processing, machine learning and mathematical modelling. We will work closely with partners developing technologies for measuring brain, eye, spinal, and peripheral nerve activity using wearable technology and minimally invasive devices. Collectively, this will contribute to a significant increase in capacity that will augment the expertise provided in neurology services. To achieve this, we will build a network of partners with backgrounds spanning academia, industry, hospitals and GP surgeries, charities and policy makers. Crucially we will ensure that people with lived experience of neurological conditions are at the heart of our network. Their experience will inform debate and shape our research priorities, ensuring feasibility and acceptability of emerging technologies. We will empower people from different backgrounds and career stages to work together on challenging problems whose solutions will lead to societal benefit. To enable this we plan a suite of activities built around the principles of connect, communicate and collaborate. To connect people we will build a website and social media presence, create a public representation group and build new parnterships. We will establish a mentorship scheme and post opportunities for people at different career stages to undertake secondments with partner organisations. To facilitate communication, we will engage with stakeholders including the public, people with neurological conditions, healthcare providers and policy makers. We will host workshops on emerging areas of interest, as well as an annual conference to celebrate findings from across the network. To enable collaboration we will host events including stake-holder led study groups, sandpits and research incubators: where teams of partners will work collaboratively in a facilitated environment, conducting feasibility studies over 6-9 months.