Modelling the spread of HIV in social networks of injecting drug users

AIDS. 1998 May 7;12(7):801-11. doi: 10.1097/00002030-199807000-00017.


Objective: To explore the risk of a future rise of HIV prevalence in populations of injecting drug users (IDU) with low HIV prevalence but continuing risk behaviour, and to study the potential influence of prevention measures on HIV incidence.

Methods: A stochastic simulation model was used to describe a network of long-term buddy relationships in a population of IDU. HIV transmission took place when borrowing injecting equipment from an infected buddy or stranger. The probability of transmission depended on the duration of infection. Individuals remained in the population on average for 10 years. Two surveys amongst IDU in The Netherlands containing information about risk behaviour were used to estimate model parameters. We investigated the effect of different prevention strategies.

Results: Below a threshold sharing frequency the epidemic never takes off; above the threshold there is a large stochastic variation in prevalence. After reduction of risk behaviour, HIV prevalence decreases very slowly. Reducing sharing with strangers is more effective than reducing the overall sharing frequency. Prevention focused on new IDU greatly reduces HIV incidence. Reduction of sharing frequency in HIV-positive IDU has no significant influence on HIV incidence at HIV testing rates of 10 and 50% per year, if infectivity is highest during primary infection.

Conclusions: A stabilization of HIV prevalence does not exclude the possibility of a future rise. Predictions about the future course of an epidemic are inherently uncertain. The effect of prevention programmes on HIV prevalence only becomes visible on a long time-scale. Social networks of IDU play an important role in transmission dynamics and success of prevention.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • HIV Infections / epidemiology
  • HIV Infections / prevention & control
  • HIV Infections / transmission*
  • Humans
  • Models, Biological*
  • Models, Statistical*
  • Social Support
  • Stochastic Processes
  • Substance Abuse, Intravenous*