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PLoS Comput Biol. 2014 Mar 13;10(3):e1003459. doi: 10.1371/journal.pcbi.1003459. eCollection 2014.

The rise and fall of HIV in high-prevalence countries: a challenge for mathematical modeling.

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  • 1Institute of Public Health, College of Medicine and Health Science, United Arab Emirates University, Al Ain, United Arab Emirates; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada.
  • 2Center for Global Health Research, St. Michael's Hospital, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
  • 3South African Centre for Epidemiological Modelling and Analysis, University of Stellenbosch, Stellenbosch, South Africa.
  • 4Department of Medicine, University of Toronto, Toronto, Canada; Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya.
  • 5Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.


Several countries with generalized, high-prevalence HIV epidemics, mostly in sub-Saharan Africa, have experienced rapid declines in transmission. These HIV epidemics, often with rapid onsets, have generally been attributed to a combination of factors related to high-risk sexual behavior. The subsequent declines in these countries began prior to widespread therapy or implementation of any other major biomedical prevention. This change has been construed as evidence of behavior change, often on the basis of mathematical models, but direct evidence for behavior changes that would explain these declines is limited. Here, we look at the structure of current models and argue that the common "fixed risk per sexual contact" assumption favors the conclusion of substantial behavior changes. We argue that this assumption ignores reported non-linearities between exposure and risk. Taking this into account, we propose that some of the decline in HIV transmission may be part of the natural dynamics of the epidemic, and that several factors that have traditionally been ignored by modelers for lack of precise quantitative estimates may well hold the key to understanding epidemiologic trends.

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