Publications & Reports

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

Rolls DA, Wang P, Jenkinson R, Pattison PE, Robins GL, Sacks-Davis R, Daraganova G, Hellard M, McBryde E

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 andfour attributes (location, age, injecting frequency, gender) are estimated using a conditional estimationapproach that respects the structure of snowball sample designs. Those network parameter estimates arethen used to create a novel, model-dependent estimate of network size. Simulated PWID contact networksare created and compared with Bernoulli graphs. Location, age and injecting frequency are shown to bestatistically significant attribute parameters in the ERGM. Simulated ERGM networks are shown to fitthe collected data very well across a number of metrics. In comparison with Bernoulli graphs, simulatednetworks are shown to have longer paths and more clustering. Results from this study make possible sim-ulation of realistic networks for investigating treatment and intervention strategies for reducing hepatitisC prevalence.

Publication

  • Journal: Social Networks
  • Published: 01/04/2013
  • Volume: 35
  • Issue: 4
  • Pagination: 699-710

Authors

Health Issue