Age-specific representations of social contact networks using egocentric survey data
Lead Research Organisation:
University of Warwick
Department Name: Mathematics
Abstract
When modelling the spread of a disease outbreak epidemiologists often use the assumption that the population of interest is 'well-mixed'. Under this assumption, all individuals interact at the same rate, providing a powerful mechanism to predict disease prevalence at a societal level. However, in many cases there exists groups of individuals ('superspreaders'), who infect disproportionately many more people than the mean. These heterogeneities in the secondary case distribution are pervasive in different pathogens and settings, with widespread use of the 80/20 rule throughout epidemiological literature (80% of cases are attributable to 20% of infected individuals). The factors governing the identification of superspreaders are not well understood, and could be behavioural, biological or a mixture of both.
A widely used approach to incorporate heterogeneity of secondary case distribution in the modelling process is to induce disease transmission on a contact network. This project will implement an individual-based network approach, to better understand behavioural heterogeneities. In particular, contact data will be inferred from the CoMix study, and possibly other pre-COVID surveys such as POLYMOD, the BBC pandemic survey [https://doi.org/10.1016/j.epidem.2018.03.003] and the Warwick contact survey. These data sets provide a rich body of information on egocentric contact structures in the UK (e.g. the number and type of contacts from a respondent), however, a standardised mechanism for constructing the complete population level contact network from this type of data does not exist. There are many competing approaches to create a representative contact network from this egocentric data, with many failing to provide the clustering or assortative mixing known to be present in human social networks. One further difficulty arises from the intractability of true link weights, which emerge from some interactions being much more likely to spread disease, due to duration of interaction, the closeness of the interaction or other factors. This project will address the importance of duration, physicality and setting in which a contact is made on the network link structure and ultimately epidemic
dynamics. It will bridge the gap between the power law degree distributions of networks commonly reported in society and the negative binomial secondary case distributions described for a wide range of pathogens.
This can be achieved by incorporating link weights based on each contacts characteristics and fitting these using an epidemic model to known outbreak secondary case distributions. Danon et al. proposed methods to translate connection properties to link weights, which we can explore in the case of the COVID-19 outbreak and the novel mixing structures these lockdowns caused. The project will further consider the implications of network clustering (often observed as cliques of highly interconnected individuals). Throughout, I will use a mixture of simple analytical approaches and simulation models to gain a greater understanding; both will be informed by or fitted to the wealth of observations that are being collected.
A widely used approach to incorporate heterogeneity of secondary case distribution in the modelling process is to induce disease transmission on a contact network. This project will implement an individual-based network approach, to better understand behavioural heterogeneities. In particular, contact data will be inferred from the CoMix study, and possibly other pre-COVID surveys such as POLYMOD, the BBC pandemic survey [https://doi.org/10.1016/j.epidem.2018.03.003] and the Warwick contact survey. These data sets provide a rich body of information on egocentric contact structures in the UK (e.g. the number and type of contacts from a respondent), however, a standardised mechanism for constructing the complete population level contact network from this type of data does not exist. There are many competing approaches to create a representative contact network from this egocentric data, with many failing to provide the clustering or assortative mixing known to be present in human social networks. One further difficulty arises from the intractability of true link weights, which emerge from some interactions being much more likely to spread disease, due to duration of interaction, the closeness of the interaction or other factors. This project will address the importance of duration, physicality and setting in which a contact is made on the network link structure and ultimately epidemic
dynamics. It will bridge the gap between the power law degree distributions of networks commonly reported in society and the negative binomial secondary case distributions described for a wide range of pathogens.
This can be achieved by incorporating link weights based on each contacts characteristics and fitting these using an epidemic model to known outbreak secondary case distributions. Danon et al. proposed methods to translate connection properties to link weights, which we can explore in the case of the COVID-19 outbreak and the novel mixing structures these lockdowns caused. The project will further consider the implications of network clustering (often observed as cliques of highly interconnected individuals). Throughout, I will use a mixture of simple analytical approaches and simulation models to gain a greater understanding; both will be informed by or fitted to the wealth of observations that are being collected.
Planned Impact
In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.
Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.
Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.
Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.
Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.
Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.
Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.
Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.
Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.
People |
ORCID iD |
Luke Murray Kearney (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S022244/1 | 30/09/2019 | 30/03/2028 | |||
2737744 | Studentship | EP/S022244/1 | 02/10/2022 | 29/09/2026 | Luke Murray Kearney |