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Original article
A review of mathematical models of HIV/AIDS interventions and their implications for policy
  1. Leigh F Johnson1,
  2. Peter J White2,3
  1. 1School of Public Health and Family Medicine, Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
  2. 2Modelling and Economics Unit, Health Protection Agency, London, UK
  3. 3Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK
  1. Correspondence to Leigh F Johnson, Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Anzio Road, Observatory 7925, Cape Town, South Africa; leigh.johnson{at}uct.ac.za

Abstract

Objectives This review aims to summarise key messages emerging from mathematical models of HIV/AIDS interventions and identifies ways in which models can assist policy makers.

Methods A search of the PubMed database was conducted and studies were included if they modelled the effects of HIV prevention or treatment programes. Conclusions of relevance to policy makers were summarised under a number of key themes.

Results Mathematical models have evaluated a wide range of different HIV prevention and treatment programmes. Central themes include the positive effects of interventions beyond the groups in which they are introduced, the importance of intervening early, the potential for risk compensation to reverse gains made in HIV prevention and the emerging threat of drug resistance. Several freely available models have been developed to compare the impact and cost-effectiveness of different interventions. These and other models can be used to assess potential synergies between interventions as well as situations in which intervention impact may be mitigated by other interventions.

Conclusions Mathematical models can assist policy makers in comparing the relative impact and cost-effectiveness of different interventions, generalising the results of randomised controlled trials to the local setting, identifying threats to programme success, identifying opportunities for maximising intervention impact/efficiency and evaluating the extent to which observed trends in HIV prevalence are attributable to HIV/AIDS programme success.

  • HIV/AIDS
  • mathematical model
  • prevention
  • treatment

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Footnotes

  • Funding PJ White thanks the UK Medical Research Council for funding.

  • Competing interests None.

  • Provenance and peer review Commissioned; externally peer reviewed.

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