Mathematical models are often used to gain theoretical insights into the epidemiology of sexually transmitted infections (STIs) and to inform policy around the prevention and treatment of STIs. Yet these models differ greatly in the assumptions they make, and can sometimes produce vastly different estimates of the likely impact of STI control programmes. So which modelling approaches are most realistic? How much bias might we be introducing with certain simplifying assumptions? This article summarises a recent paper that attempted to address these questions by comparing two broad modelling approaches: deterministic, frequency-dependent models and individual-based, network models.
HIV testing is critically important to HIV prevention and treatment. Therefore UNAIDS has called for 90% of all HIV-positive individuals to be diagnosed by 2020. However, there are practical challenges associated with measuring progress towards this target. Many countries simply quote the proportion of adults who report having ever been tested for HIV in national household surveys. In a recent study, we attempted to obtain more accurate estimates of rates of HIV testing in South Africa, by combining survey data and routine testing data from health services. The results suggest that there is likely to be significant bias in self-reporting of past HIV testing. The results also show that South Africa has made substantial progress in scaling up access to HIV testing and counselling, with 76% of HIV-positive adults diagnosed by 2012. However, men and older adults appear to have a relatively low rate of HIV testing.