Modelling a disease-relevant contact network of people who inject drugs

David A. Rolls, Peng Wang, Rebecca Jenkinson, Phillipa E. Pattison, Garry L. Robins, Rachel Sacks-Davis, Galina Daraganova, Margaret Hellard, Emma McBryde

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

This study uses social network analysis to model a contact network of people who inject drugs (PWID) relevant for investigating the spread of an infectious disease (hepatitis C). Using snowball sample data, parameters for an exponential random graph model (ERGM) including social circuit dependence and four attributes (location, age, injecting frequency, gender) are estimated using a conditional estimation approach that respects the structure of snowball sample designs. Those network parameter estimates are then used to create a novel, model-dependent estimate of network size. Simulated PWID contact networks are created and compared with Bernoulli graphs. Location, age and injecting frequency are shown to be statistically significant attribute parameters in the ERGM. Simulated ERGM networks are shown to fit the collected data very well across a number of metrics. In comparison with Bernoulli graphs, simulated networks are shown to have longer paths and more clustering. Results from this study make possible simulation of realistic networks for investigating treatment and intervention strategies for reducing hepatitis C prevalence.

Original languageEnglish
Pages (from-to)699-710
Number of pages12
JournalSocial Networks
Volume35
Issue number4
DOIs
Publication statusPublished - Oct 2013
Externally publishedYes

Keywords

  • Exponential random graph model
  • Hidden population
  • Network size
  • Snowball sample
  • Social network

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