Operationalising Modern Mathematical Epidemiology

Lead Research Organisation: University of Manchester
Department Name: Mathematics

Abstract

Data on healthcare is increasingly collected and processed electronically, creating both opportunities and challenges.

Fortunately, recent years have seen major developments in the field of mathematical epidemiology, which systematically models the patterns of disease and health in the population to disentangle the complexities of the available data.

This project seeks to drive some key insights from mathematical epidemiology into the healthcare system, particularly relating to: the analysis of infectious disease data collected at the household level; how different viruses linked to colds and 'flu hinder and help each others' spread through the population; understanding how people influence each others' health-related behaviours; and cancer radiotherapy.

Other aspects of the project are technical, but seek to answer the questions: What should be done with data that are not experimental, but appear in an uncontrolled way such as during disease outbreaks? And what should we do with data that are constantly being collected through routine surveillance?

Planned Impact

The main Impact of the research will be for the general public, who will benefit from improved public health policy relating to infections disease, particularly control measures within households and vaccination policy. The public will also benefit from the situational awareness, detection of emerging threats and other positive outcomes of improved surveillance.

Patients with cancers in need of radiotherapy will benefit from enhanced methods for treatment planning to optimise the trade-off between the probabilities of successful treatment and of normal tissue damage.

Healthcare providers (in the UK, primarily NHS institutions) will benefit from the ability to understand and predict health and healthcare-related behaviours better, and to calibrate these predictions to the population that they serve.

Publications

10 25 50
 
Description We have improved understanding of routine surveillance data, 'social contagion' and the quantification social effects using advanced mathematical methods in population health and epidemiology, the role of near-critical, extremely heterogeneous, clustered heterogeneous and other complex contact networks in epidemic spreading, and methods for dealing with emergence. We have developed new methodology for inference in close contact situations such as care homes using cutting-edge numerics, and for the inclusion of covariates in households models
Exploitation Route The work has been taken forwards, particularly in clustered networks / households, as part of ongoing pandemic response.

Various parts of the public health and healthcare system - notably PHE and hospitals - are particularly interested in taking these results forwards. It has also led to funded collaboration with IBM Research.
Sectors Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Healthcare,Government, Democracy and Justice

URL https://personalpages.manchester.ac.uk/staff/thomas.house/about.html
 
Description The work on social contagion is being taken forwards by the private partner in development of a product. IBM research is collaborating on new MCMC and Gaussian Process methodology. Households / close contact work is being taken up in various places by the healthcare and public health system, and is being used to inform policy in the current Coronavirus outbreak through SAGE and its sub-committees.
First Year Of Impact 2017
Sector Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Healthcare,Government, Democracy and Justice
Impact Types Societal,Economic,Policy & public services

 
Description Industrially funded PhD
Amount £80,000 (GBP)
Organisation Autotrader 
Sector Private
Country United Kingdom
Start 01/2017 
End 12/2020