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Epidemiology poster session 4: Modelling
P1-S4.20 Mathematical modelling of HIV transmission and control among men who have sex with men: a review of 25 years of literature
  1. N Punyacharoensin1,
  2. W J Edmunds1,
  3. D De Angelis2,
  4. R Guy White1
  1. 1London School of Hygiene & Tropical Medicine, London, UK
  2. 2Heath Protection Agency and MRC Biostatistics Unit, UK


Background For a quarter of century, mathematical models have been used to study the spread and control of HIV among men who have sex with men (MSM). We reviewed this literature to summarise the methodologies used, key model developments, and recommended strategies for HIV control among MSM.

Methods Review of the literature on dynamic compartmental models of HIV transmission among MSM was conducted. MEDLINE/EMBASE were searched from earliest date to end 2010.

Results Of 742 studies identified, 127 studies met the inclusion criteria and were selected for review. Most studies employed deterministic methods (80%), and potentially as a result of this, as the complexity of models increased over time with respect to antiretroviral therapy (ART), there was a marked decline in the complexity of models with respect to sexual activity. Only a small proportion of models were fitted to data (22%) and even fewer were validated (17%), somewhat reducing confidence in the findings from these studies. That said, a number of common findings emerged, including (1) the importance of assumed changes in infectivity and sexual contact rates on the impact of ART on HIV incidence, and that this led to follow-up empirical studies to gather these data, and (2) the recommendation that multiple strategies would be required for effective HIV control among MSM.

Conclusions Mathematical models have been useful in indentifying key empirical studies and for showing that multiple prevention strategies would be required for effective control of HIV epidemics in MSM. The lack of model fitting and validation emphasise that this area should be targeted for developments in the future. An improved methodology for parameter estimation will help generate predictions that more fully express uncertainty, allowing more informed public heath decision making.

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