New challenges for mathematical and statistical modeling of HIV and hepatitis C virus in injecting drug users

AIDS. 2008 Aug 20;22(13):1527-37. doi: 10.1097/QAD.0b013e3282ff6265.


Injecting drug users are not only driving blood-borne transmission of HIV and hepatitis C virus but also likely drive sexual transmission of HIV in large parts of the world. Mathematical and statistical modeling can provide important insights in these epidemiological processes and on the potential impact of interventions but have been little used to date. This review aims to discuss the potential areas of application of modeling in the field of viral infections in injecting drug users. After reviewing key examples of published modeling work on HIV and hepatitis C virus in injecting drug users, we discuss recent developments in the epidemiology, diagnosis, treatment and prevention of these infections. In particular, new methods for the diagnosis of early HIV infection, new antivirals for a more effective treatment of HIV, hepatitis B and hepatitis C virus infections, new concepts in design and surveillance of interventions for drug users and increasing possibilities of molecular typing of pathogens are changing the questions and decisions for public health policy makers who deal with drug-related infectious diseases. Research including mathematical modeling is needed to understand the impact of new diagnostic tools, new treatment options and combined intervention strategies on the epidemiology of viral infections in injecting drug users. Methodological advances in mathematical modeling are required to adequately approach some of the ensuing research questions. Modeling has much to offer for solving urgent policy questions, but current levels of funding in modeling research are insufficient and need to be scaled up to make better use of these possibilities.

Publication types

  • Editorial
  • Review

MeSH terms

  • HIV Infections / epidemiology*
  • HIV Infections / transmission
  • HIV*
  • Hepacivirus*
  • Hepatitis C / epidemiology*
  • Hepatitis C / transmission
  • Humans
  • Models, Biological
  • Models, Statistical*
  • Substance Abuse, Intravenous / epidemiology*