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Details of Grant 

EPSRC Reference: EP/N014529/1
Title: EPSRC Centre for Mathematics of Precision Healthcare
Principal Investigator: Barahona, Professor M
Other Investigators:
Matthews, Professor P Degond, Professor PAA Guo, Professor Y
Colijn, Dr C Jones, Professor N Darzi, Professor AW
Rueckert, Professor D
Researcher Co-Investigators:
Project Partners:
AstraZeneca Dementias Platform UK Imperial College London
IXICO Technologies Ltd Johnson & Johnson Omicia Inc
Public Health England Sinnia The Sainsbury Laboratory
University of Oxford Wellcome Trust
Department: Mathematics
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 April 2016 Ends: 31 March 2021 Value (£): 2,056,655
EPSRC Research Topic Classifications:
Complexity Science Non-linear Systems Mathematics
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Sep 2015 Maths in Healthcare - Interviews Announced
Summary on Grant Application Form
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.

Key Findings
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Organisation Website: http://www.imperial.ac.uk