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      Social encounter networks: characterizing Great Britain

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          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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          Epidemic spreading in scale-free networks

          The Internet, as well as many other networks, has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and prevalence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalize data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
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            Modelling disease outbreaks in realistic urban social networks.

            Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
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              The Web of Human Sexual Contacts

              Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents \(\alpha \approx 2.4\) for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.
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                Author and article information

                Journal
                Proc Biol Sci
                Proc. Biol. Sci
                RSPB
                royprsb
                Proceedings of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8452
                1471-2954
                22 August 2013
                22 August 2013
                : 280
                : 1765
                : 20131037
                Affiliations
                [1 ]Mathematics Institute, University of Warwick , Coventry CV4 7AL, UK
                [2 ]School of Life Sciences, University of Warwick , Coventry CV4 7AL, UK
                [3 ]Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool , Neston, CH64 7TE, UK
                [4 ]University Computing Service, University of Cambridge , Cambridge CB2 3QH, UK
                Author notes
                [†]

                These authors contributed equally to this study.

                Article
                rspb20131037
                10.1098/rspb.2013.1037
                3712448
                23804621
                3ae1acc9-ce3a-42f0-8d7f-94b39b6e0027

                © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 24 April 2013
                : 28 May 2013
                Categories
                1001
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                Research Articles
                Custom metadata
                August 22, 2013

                Life sciences
                social contact,survey,epidemic,infectious disease,network
                Life sciences
                social contact, survey, epidemic, infectious disease, network

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