EPSRC Centre for Mathematics of Precision Healthcare

Medicine is undergoing a simultaneous shift at the levels of the individual and the population: we have unprecedented tools for precision monitoring and intervention in individual health and we also have high-resolution behavioural and social data. Precision medicine seeks to deploy therapies that are sensitive to the particular genetic, lifestyle and environmental circumstances of each patient: understanding how best to use these numerous features about each patient is a profound mathematical challenge. We propose to build upon the mathematical, computational and biomedical strengths at Imperial to create a Centre for the Mathematics of Precision Healthcare revolving around the theme of multiscale networks for data-rich precision healthcare and public health. Our Centre proposes to use mathematics to unify individual-level precision medicine with public health by placing high-dimensional individual data and refined interventions in their social network context. Individual health cannot be separated from its behavioural and social context; for instance, highly targeted interventions against a cancer can be undermined by metabolic diseases caused by a dietary behaviour which co-varies with social network structure. Whether we want to tackle chronic disease or the diseases of later life, we must simultaneously consider the joint substrates of the individual together with their social network for transmission of behaviour and disease. We propose to tackle the associated mathematical challenges under the proposed Centre bringing to bear particular strengths of Imperial's mathematical research in networks and dynamics, stochastic processes and analysis, control and optimisation, inference and data representation, to the formulation and analysis of mathematical questions at the interface of individual-level personalised medicine and public health, and specifically to the data-rich characterisation of disease progression and transmission, controlled intervention and healthcare provision, placing precision interventions in their wider context.
The programme will be initiated and sustained on core research projects and will expand its portfolio of themes and researchers through open calls for co-funded projects and pump-priming initiatives.
Our initial set of projects will engage healthcare and clinical resources at Imperial including: (i) patient journeys for disease states in cancer and their successive hospital admissions; multi-omics data and imaging characterisations of (ii) cardiomyopathies and (iii) dementia and co-morbidities; (iv) national population dynamics for epidemiological and epidemics simulation data from Public Health; social networks and (v) health beliefs and (vi) health policy debate. The initial core projects will build upon embedded computational capabilities and data expertise, and will thus concentrate on the development of mathematical methodologies including: sparse state-space methods for the characterisation of disease progression in high-dimensional data using transition graphs in discrete spaces; time-varying networks and control for epidemics data; geometrical similarity graphs to link imaging and omics data for disease progression; stochastic processes and community detection from NHS patient data wedding behavioural and social network data with personal health indicators; statistical learning for the analysis of stratified medicine. The mathematical techniques used to address these requirements will need to combine, among others, ingredients from dynamical and stochastic systems with graph-theoretical notions, sparse statistical learning, inference and optimisation. The Centre will be led by Mathematics but researchers in the Centre span mathematical, biomedical, clinical and computational expertise.

The societal impact of the proposed research will be very broad as the Centre will be actively integrated with NHS bodies (including the 5 hospitals in the Imperial Healthcare Trust) Dementia UK and Public Health England. Our centre is explicitly targeted at the challenges these organisations face: large streaming, incomplete networked datasets which have to be used as a basis for both patient interventions and wider public health strategy. By proposing control strategies for both infectious and chronic disease we hope to move us towards an optimised use of resources in the health service.
The connections of the Centre with the Institute for Global Health Innovation and the eHealth Unit adds another axis of impact towards influencing policy making. An example is the analysis of patients according to their temporal patterns of NHS usage, which could be used to improve patient segmentation leading to improved resource allocation and patient satisfaction. We expect that the British and International public will be beneficiaries of this research through better decision making at the NHS, identification and intervention for complex diseases as well as social aspects of health.
In addition, we have specific interfaces with two third sector representatives: Cancer Research UK and the British Heart Foundation. In both cases precision data is being acquired at the individual level (omics, imaging) but the need is to distill from this data clinical categories, appropriate interventions and optimal symptom management. Our methods would help to bridge this gap and we will actively explore these possibilities with the clinical branches of CRUK and BHF associated with NHS Imperial hospitals. Prime examples of such applications are showcased by existing collaborations of Prof Rueckert with Prof Matthews and Prof Cook on therapeutic applications of medical image analysis.
We will also pursue the potential impact of our research on social network analysis, as a means of obtaining relevant information for healthcare provision and of influencing opinion formation. There are further connections with The Governance Lab at NYU and an NHS-funded project on the use of social networks to find relevant expertise for particular needs. We thus expect that our research can have an impact on social scientists as well as clinicians working on issues related to healthcare provision.
Another axis of impact is through industrial connections. We expect to have an impact on the computational side through the development of novel algorithms for Big Data approaches, imaging analyses, and specific sparse coding approaches for signal processing. Similarly, pharma companies and broad-based companies (such as Syngenta) will benefit from the characterisation of experimental processes where high-dimensional data is collected and from the development of detection markers.
Our work will impact other areas of basic research (specifically data science, network analysis, and systems and synthetic biology) and we will ensure that the impact is maximised by our extensive programme of workshops and challenge-led activities together with a visitor programme with high-profile speakers.
The researchers who will work in this Centre will be uniquely trained, acquiring expertise in mathematical tools coupled to big data as well as healthcare applications. Such expertise is in high demand and the researchers will be able to become national and indeed international leaders themselves in an area where there is a clear shortage of expertise, thus contribution to the UK knowledge base.
Finally the Applied Mathematics community will benefit by the recognition that excellent mathematics and computational skills can be brought together to help solve problems relevant to a broad section of society.