Dementia affects 55 million people worldwide. With no accessible treatment, we need to identify ways to reduce people's risks of dementia and improve their experiences of living with dementia. People within our network will include individuals living with dementia, carers and family members, researchers, and people working for charities, health and social care services and industries. We have called this network "Sustainable Prevention, Innovation and INvolvement NETwork (SPIINNET)". We will aim to reduce risks and encourage early support in dementia, through innovative research and collaboration. We will deliver a programme of activities that will use and make connections between the experience, knowledge and resources of people across the network. These activities will include workshops where people can meet to design research projects together, training events, funding innovative ideas, meetings to raise awareness about dementia and prevention, and annual conferences to share our learning. We will work with early-stage researchers and community members, particularly engaging people and communities from diverse communities. We will bring together existing research evidence including "Big Data" resources for prevention of dementia, to be accessible for many different groups of people and to inform the network's activities. Our network model and focus will address the following objectives: Involve diverse communities, with different lived experiences, researchers and service providers. Understand how different experiences and cultures may affect people's actions to prevent dementia. Exchange knowledge between different groups of people to share understanding of dementia, prevention, resources, priorities and concerns. Innovate new ways of strengthening society, services and communities to reduce the risk of dementia within populations, and inspire individuals and families to engage in positive actions for change. Implement and widely evaluate innovations, especially for often-under reached groups, such as minority ethnic groups or people with disabilities. Sustain innovations by connecting resources, skills and learning through the network's activities. We will strive to reduce research waste, and to make our network sustainable beyond the first four years of funding. The above list represents four workstreams and each workstream will have a lead. We will have a Network Governance team with a management group, a Steering Committee and a Group of people with Lived Experience of dementia, including carers. The Lived Experience Group will be involved in decision-making and will help ensure that people who live with dementia are equally involved across the network. The network will hold a Flexible Fund of money where Network partners can apply for money to develop new research projects. The workshops, training events, meetings and annual conferences will be places for people to exchange knowledge, develop ideas together, to then also apply for funding to develop their ideas further. This application has been co-written by a group who are passionate about reducing the risk of dementia through innovative, creative, and collaborative research practices. We welcome the opportunity to create transformative change through the network described in this application.

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.