Disease transmission and control in complex, structured populations
Lead Research Organisation:
University of Warwick
Department Name: Mathematics
Abstract
Infectious disease is the main thing that kills people. Some of the greatest improvements to human health have involved improvements in our understanding and control of germs - from John Snow's pioneering work on cholera in the 19th century to the eradication of smallpox in the 20th century. The 21st century sees a new set of challenges in the understanding and control of infections - while the eradication of polio progresses, we see new influenza strains causing or threatening pandemics, the continued progression of HIV and a massive health burden of often simply but expensively preventable diseases in the developing world.Epidemiology - the science of looking for significant patterns in cases of disease - has always been at the heart of controlling infectious diseases, and mathematics has always been central epidemiology.This project applies advanced mathematics to the science of epidemiology, making use of the large datasets and modern computational resources that are available. New insights about the structure of complex systems offer the promise of making massive advances in this field, through enhanced understanding of transmission routes of infection, risk factors and changes in the disease over time. These insights can in turn be combined with mathematical methods to design optimised interventions against infection so that diseases can be controlled in the most effective way.
Planned Impact
The ultimate aim for impact from my research is to reduce the burden of infectious disease on the human population - quantitative epidemiology has always been at the centre of efforts to control pathogens.Infectious disease remains the main cause of human mortality. This means that major advances in our understanding of them can revolutionise public health, while even minor improvements in science can have highly significant effects on the general health and well-being of the general population.The intermediate beneficiaries of my work are the Health Protection Agency (HPA), which is the body responsible for public health - including infection control - in England, the medical research institute KEMRI in Kenya, and also the Department of Health and associated scientific advisory bodies. Through working with these bodies as detailed in the Pathways to Impact document I will ensure that any policy-relevant conclusions from my work are swiftly disseminated.
People |
ORCID iD |
| Thomas House (Principal Investigator / Fellow) |
Publications
Ball F
(2016)
Reproduction numbers for epidemic models with households and other social structures II: Comparisons and implications for vaccination.
in Mathematical biosciences
Ball F
(2015)
Seven challenges for metapopulation models of epidemics, including households models.
in Epidemics
Black AJ
(2014)
The effect of clumped population structure on the variability of spreading dynamics.
in Journal of theoretical biology
Black AJ
(2013)
Epidemiological consequences of household-based antiviral prophylaxis for pandemic influenza.
in Journal of the Royal Society, Interface
Britton T
(2015)
Five challenges for stochastic epidemic models involving global transmission.
in Epidemics
Buckingham-Jeffery E
(2017)
Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.
in BMC public health
Danon L
(2012)
Social encounter networks: collective properties and disease transmission.
in Journal of the Royal Society, Interface
Danon L
(2013)
Social encounter networks: characterizing Great Britain.
in Proceedings. Biological sciences
De Angelis D
(2015)
Four key challenges in infectious disease modelling using data from multiple sources.
in Epidemics
Del Genio CI
(2013)
Endemic infections are always possible on regular networks.
in Physical review. E, Statistical, nonlinear, and soft matter physics
Graham M
(2014)
Dynamics of stochastic epidemics on heterogeneous networks.
in Journal of mathematical biology
Hancock PA
(2014)
Strategies for controlling non-transmissible infection outbreaks using a large human movement data set.
in PLoS computational biology
House T
(2014)
Heterogeneous clustered random graphs
in EPL (Europhysics Letters)
House T
(2014)
Epidemiological dynamics of Ebola outbreaks.
in eLife
House T
(2013)
How big is an outbreak likely to be? Methods for epidemic final-size calculation
in Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
House T
(2016)
Lie Algebra Solution of Population Models Based on Time-Inhomogeneous Markov Chains
in Journal of Applied Probability
House T
(2012)
Modelling epidemics on networks
in Contemporary Physics
House T
(2015)
Algebraic moment closure for population dynamics on discrete structures.
in Bulletin of mathematical biology
House T
(2012)
Estimation of outbreak severity and transmissibility: Influenza A(H1N1)pdm09 in households
in BMC Medicine
Inglis NJ
(2014)
Measuring the effect of influenza A(H1N1)pdm09: the epidemiological experience in the West Midlands, England during the 'containment' phase.
in Epidemiology and infection
Pellis L
(2015)
Exact and approximate moment closures for non-Markovian network epidemics.
in Journal of theoretical biology
Pellis L
(2020)
Systematic selection between age and household structure for models aimed at emerging epidemic predictions.
in Nature communications
Rhodes CA
(2013)
The rate of convergence to early asymptotic behaviour in age-structured epidemic models.
in Theoretical population biology
Ritchie M
(2014)
Higher-order structure and epidemic dynamics in clustered networks.
in Journal of theoretical biology
Sutton AJ
(2012)
Modelling HIV in the injecting drug user population and the male homosexual population in a developed country context.
in Epidemics
| Description | See EP/J002437/2 (the same grant, there are only different numbers because of a change of institution) |
| Exploitation Route | See EP/J002437/2 (the same grant, there are only different numbers because of a change of institution) |
| Sectors | Healthcare,Government, Democracy and Justice |
| URL | http://personalpages.manchester.ac.uk/staff/thomas.house |
| Description | See EP/J002437/2 (the same grant, there are only different numbers because of a change of institution) |
| First Year Of Impact | 2010 |
| Sector | Healthcare,Government, Democracy and Justice |
| Impact Types | Cultural,Societal,Policy & public services |
| Title | EpiStruct |
| Description | Mathematical routines for epidemic modelling and inference in structured populations. |
| Type Of Technology | Software |
| Year Produced | 2014 |
| Open Source License? | Yes |
| Impact | Use in various modelling studies / applications. |
| URL | http://epistruct.sourceforge.net/ |
| Title | elife-ebola-code |
| Description | Software for working with subcritical disease outbreaks including Ebola. |
| Type Of Technology | Software |
| Year Produced | 2014 |
| Open Source License? | Yes |
| Impact | DOI 10.7554/eLife.03908 |
| URL | https://github.com/thomasallanhouse/elife-ebola-code |
| Company Name | Spectra Analytics |
| Description | Spectra Analytics is a boutique data analysis and research consultancy. |
| Year Established | 2014 |
| Impact | Formed by my PhD student Dan Sprague. |
| Website | http://www.spectraanalytics.com/ |