Social assortativity and contagious processes in modern Britain.

Assortativity is the property describing when individuals tend to mix with others who have similar (social or demographic) properties to themselves. As such it is a key structural property of social networks. The extent to which interactions between individuals are assortative has important implications for contagious processes which operate on such interaction networks, including the spread of infectious diseases and the propagation of ideas or beliefs. The spread of infection provides a good indicator of assortativity and network structure as data on disease is regularly recorded across a range of scales. In addition, accurately reflecting assortativity in models of infection is necessary if such models are to robustly inform public-health policy.

Recent work has shown that there is strong assortative mixing by age in the UK population: most of the social encounters made by children are with similarly aged children, while adults tend to interact with a broad age range of other adults. This insight into social mixing helps explain some of the patterns of infection observed in past epidemics, including the early stages of the 2009 influenza pandemic. However, age assortative mixing alone does not explain the inequalities and clustering of infection often seen in epidemics - a better quantitative understanding of how interactions within modern society are structured by age, gender, socioeconomic status, education and ethnicity would improve models of the spread of infection and beliefs.

There is a wealth of information on the assortative nature of modern British society collected by a range of social and economic surveys but, to date, this information has not been incorporated into mathematical models of social networks. Current mathematical models of infection, routinely used to inform health planning and predictions, do not yet include realistic social structures. There is, therefore, an opportunity to exploit these datasets and considerably improve the realism of models of the social structure of the UK. It is likely that there is assortative structure within household, workplace and community settings by a variety of individual metrics. Strong assortativity would have important impact on the dynamics of infection and other contagious processes.

We shall compile and incorporate information on assortativity from a range of sociological cross-sectional and cohort studies to analyse the character and strength of assortativity of interactions made by people in modern Britain. We will assess the assortativity and dissassortativity of British society within home, workplace, social and community settings, as well whether significant correlations exist between different assortative structures. By linking assortativity and interaction data with census and travel information, and with information from two recent GB-based social encounter studies we will develop models of social interactions and contagious processes: the spread of both infections and belief.

Our analysis will provide ourselves and other researchers, particularly those in strategic health planning and computational sociology, with an improved understanding of which social structures are important for the spread of diseases or beliefs. By incorporating this extra information into models of contagion we expect to considerably improve our understanding of observed patterns of infection within communities and how they may be best controlled in terms of efficacy and resources. In addition, we hope to lay a quantitative foundation with which researchers can model and explore contagion of information and belief within the UK. All final models, parameters and collated data generated through the project will be released to the academic community and other researchers.

There is considerable interest in the development of mechanistic models of social interactions or to parameterize social networks, primarily from the need to improve predictive mathematical models of infection spread. This work has direct impact on public-health policy; for example the age-structured POLYMOD study (Mossong 2008) has been extensively used to help inform vaccination policy in the UK and elsewhere. We envisage that our study will have similar benefits, allowing us to substantially improve the models used to predict the impact of policy. While the application and data of the research are UK-focussed, the methodology and modelling approaches are applicable to any population: academic researchers and allied health professionals from a range of countries where similar data is available would find this work directly relevant to their research and policy-advisory tools.

We will ensure health professionals are aware of our work through publication in high impact academic journals, presentation of the work at an international conference, and through the team's direct links with researchers in the Health Protection Agency (to be Public Health England), and government advisory bodies such as the Joint Committee on Vaccination and Immunisation and the Scientific Pandemic Influenza Advisory Committee. Given that vaccination and other public-health policy is under continual review and change is common, we believe our work will be have an ongoing important impact on health policy. The general public will therefore benefit indirectly through improved models of social interactions contributing towards better infectious disease control.

In addition, beyond the modelling of infection, our work will help to generate important insights into the spread and maintenance of opinions, beliefs and behaviours within the UK. This would be helpful in understanding and possibly changing a range of health-relevant behaviours such as cessation of smoking, diet and lifestyle choices, and refusal to accept vaccination or treatment.