16
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Impact of Hepatitis C Treatment as Prevention for People Who Inject Drugs is sensitive to contact network structure

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Treatment as Prevention (TasP) using directly-acting antivirals has been advocated for Hepatitis C Virus (HCV) in people who inject drugs (PWID), but treatment is expensive and TasP’s effectiveness is uncertain. Previous modelling has assumed a homogeneously-mixed population or a static network lacking turnover in the population and injecting partnerships. We developed a transmission-dynamic model on a dynamic network of injecting partnerships using data from survey of injecting behaviour carried out in London, UK. We studied transmission on a novel exponential-clustered network, as well as on two simpler networks for comparison, an exponential unclustered and a random network, and found that TasP’s effectiveness differs markedly. With respect to an exponential-clustered network, the random network (and homogeneously-mixed population) overestimate TasP’s effectiveness, whereas the exponential-unclustered network underestimates it. For all network types TasP’s effectiveness depends on whether treated patients change risk behaviour, and on treatment coverage: higher coverage requires fewer total treatments for the same health gain. Whilst TasP can greatly reduce HCV prevalence, incidence of infection, and incidence of reinfection in PWID, assessment of TasP’s effectiveness needs to take account of the injecting-partnership network structure and post-treatment behaviour change, and further empirical study is required.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Community structure in social and biological networks

            A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Epidemic Spreading in Scale-Free Networks

              The Internet 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 persistence 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 rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
                Bookmark

                Author and article information

                Contributors
                c.metzig@imperial.ac.uk
                p.white@imperial.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 May 2017
                12 May 2017
                2017
                : 7
                : 1833
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, MRC Centre for Outbreak Analysis and Modelling and NIHR Health Protection Research Unit in Modelling Methodology, , Imperial College London School of Public Health, ; London, W2 1PG UK
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Mathematics, , Imperial College London, ; London, SW7 2AZ UK
                [3 ]ISNI 0000000121901201, GRID grid.83440.3b, Research Department of Infection and Population Health, , University College London, ; London, WC1E 6JB UK
                [4 ]Hepatitis C Trust, 27 Crosby Row, London, SE1 3YD UK
                [5 ]ISNI 0000 0001 2196 8713, GRID grid.9004.d, TB Section, National Infection Service, , Public Health England, ; London, NW9 5EQ UK
                [6 ]ISNI 0000000121901201, GRID grid.83440.3b, MRC Clinical Trials Unit, , University College London, ; London, WC2B 6NH UK
                [7 ]ISNI 0000 0001 2196 8713, GRID grid.9004.d, Modelling and Economics Unit, , National Infection Service, Public Health England, ; London, NW9 5EQ UK
                Author information
                http://orcid.org/0000-0002-5687-6170
                http://orcid.org/0000-0002-6644-3512
                Article
                1862
                10.1038/s41598-017-01862-6
                5431870
                28500290
                e8f8d0c1-b05c-44f9-8438-1d4436f70412
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 October 2016
                : 17 March 2017
                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Uncategorized
                Uncategorized

                Comments

                Comment on this article