ArticleProceedings. Biological sciences2013
Social encounter networks: characterizing Great Britain.
Article in Proceedings. Biological sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020Pooled it
- A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections.Epidemiology (Cambridge, Mass.) · 2019Pooled it
- A Bayesian Framework for the Network Analysis of Transmission Dynamics in Infectious Disease.Journal of molecular evolution · 2026Article
- Characterizing social behavior relevant for tuberculosis transmission in four low- and middle-income countries.BMC infectious diseases · 2026Article
- Risk assessment of communicable respiratory diseases transmission based on social contact networks: a primary school contact data survey conducted with portable high-precision devices.BMC public health · 2025Article
- Assessing the impacts of vaccination and viral evolution in contact networks.Scientific reports · 2024Article
- A beta-Poisson model for infectious disease transmission.PLoS computational biology · 2024Article
- Data note on the Avian Contact Study: a questionnaire resource for avian influenza public health planning.Wellcome open research · 2024Article
- Hyper-cores promote localization and efficient seeding in higher-order processes.Nature communications · 2023Article
- Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study.Occupational and environmental medicine · 2023Article
- Spatial immunization to abate disease spreading in transportation hubs.Nature communications · 2023Article
- Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England.BMC medicine · 2023Article
- Predicting Compliance with Sanitary Behaviors among Students in Higher Education During the Second COVID-19 Wave: The Role of Health Anxiety and Risk Perception.Psychologica Belgica · 2023Article
- Article
- Data needs for integrated economic-epidemiological models of pandemic mitigation policies.Epidemics · 2022Article
- Article
- Neighbourhood characteristics and social isolation of people with psychosis: a multi-site cross-sectional study.Social psychiatry and psychiatric epidemiology · 2022Article
- The SARS-CoV-2 pandemic in Germany may represent the sum of a large number of local but independent epidemics each initiated by individuals aged 10-19 years, middle-aged males, or elderly individuals.Journal of medical virology · 2022Article
- Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases.Journal of the Royal Society, Interface · 2022Article
- An assessment of the vaccination of school-aged children in England against SARS-CoV-2.BMC medicine · 2022Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.