ArticleJournal of the Royal Society, Interface2012
Social encounter networks: collective properties and disease transmission.
Article in Journal of the Royal Society, Interface, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
What it found
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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
56 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
- Social contact patterns in the United Kingdom following the COVID-19 pandemic: The Reconnect cross-sectional survey.PLoS medicine · 2026Article
- Assessing the effect of social contact structure on the impact of pneumococcal conjugate vaccines.Scientific reports · 2025Article
- Social contact patterns in South Korea: an analysis of a survey conducted in 2023-2024.BMC infectious diseases · 2025Article
- Assessing the impacts of vaccination and viral evolution in contact networks.Scientific reports · 2024Article
- Article
- The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.Royal Society open science · 2023Article
- Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models.BMC infectious diseases · 2022Article
- Patterns of human social contact and mask wearing in high-risk groups in China.Infectious diseases of poverty · 2022Article
- Epidemic management and control through risk-dependent individual contact interventions.PLoS computational biology · 2022Article
- Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study.The Lancet regional health. Western Pacific · 2022Article
- Understanding collective regularity in human mobility as a familiar stranger phenomenon.Scientific reports · 2021Article
- Model-driven mitigation measures for reopening schools during the COVID-19 pandemic.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Modelling SARS-CoV-2 transmission in a UK university setting.Epidemics · 2021Article
- High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions.Nature communications · 2021Article
- SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return.Royal Society open science · 2021Article
- Mapping social distancing measures to the reproduction number for COVID-19.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/2021 academic year.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of -2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions.
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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.