No HIV stage is dominant in driving the HIV epidemic in sub-Saharan Africa

AIDS. 2008 May 31;22(9):1055-61. doi: 10.1097/QAD.0b013e3282f8af84.


Objective: To estimate the role of each of the HIV progression stages in fueling HIV transmission in sub-Saharan Africa by using the recent measurements of HIV transmission probability per coital per HIV stage in the Rakai study.

Methods: A mathematical model, parameterized by empirical data from the Rakai, Masaka, and Four-City studies, was used to estimate the proportion of infections due to each of the HIV stages in two representative epidemics in sub-Saharan Africa. The first setting represents a hyperendemic HIV epidemic (Kisumu, Kenya) whereas the second setting represents a generalized but not hyperendemic HIV epidemic (Yaoundé, Cameroon).

Results: We estimate that 17, 51, and 32% of HIV transmissions in Kisumu were due to index cases in their acute, latent, and late stages, respectively. In Yaoundé, the fractions were 25, 44, and 31%. We found that the relative contribution of each stage varied with the epidemic evolution with the acute stage prevailing early on when the infection is concentrated in the high-risk groups with the late stage playing a major role as the epidemic matured and stabilized. The latent stage contribution remained largely stable throughout the epidemic and contributed about half of all transmissions.

Conclusion: No HIV stage dominated the epidemical though the latent stage provided the largest contribution. The role of each stage depends on the phase of the epidemic and on the prevailing levels of sexual risk behavior in the populations in which HIV is spreading. These findings may influence the design and implementation of different HIV interventions.

MeSH terms

  • Cameroon / epidemiology
  • Circumcision, Male / statistics & numerical data
  • Disease Outbreaks / statistics & numerical data*
  • Disease Progression
  • Female
  • HIV Infections / epidemiology*
  • HIV Infections / transmission
  • Humans
  • Kenya / epidemiology
  • Male
  • Models, Statistical*
  • Risk Factors
  • Risk-Taking
  • Sex Work / statistics & numerical data
  • Sexual Behavior / statistics & numerical data
  • Sexual Partners