Currently we are faced with two major threats from viral diseases: Over the last 30 years HIV has spread across the world and continues to plague us. Over the last 3 months the hemorrhagic fever caused by the Ebola virus has spread across West Africa killing thousands of people. If we are to contain HIV in the long-run and Ebola, hopefully, in a much shorter time, this will depend on our ability to understand the nature of the threat and the strategies of the disease causing organisms.
Epidemiological models for describing how a disease spreads through a population have been extremely useful to reduce the number of individuals who get sick or even die from illness. Developing meaningful and useful models is not easy however. In this paper, we first motivate the use of agent-based modelling and secondly, we present common challenges associated with agent-based modelling (of HIV) and our approaches to dealing with them.
Expanding ART coverage to healthier HIV patients is widely regarded as a potential strategy for addressing the rampant TB epidemic in high HIV-TB burden settings. Estimating the population-level impact of ART expansion on TB disease has proven challenging. We set out to estimate the potential effects of changing HIV treatment policy on TB outcomes in South Africa, comparing the results of three independent TB models. This project was part of a broader effort to shed light on the consequences of HIV policy changes, through model comparison and consensus building, a process pioneered in the HIV modelling field by the HIV Modelling Consortium.
Existing approaches to TB control have been no more than partially successful in areas with high HIV prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing strategies most likely to bring public health and economic benefits. Recognising the urgency of TB control in high HIV prevalence settings and the potential contributions of modelling, the TB Modelling and Analysis Consortium (TB MAC) convened its first meeting between empirical scientists, policy makers and mathematical modellers in September 2012 in Johannesburg. Here we present a summary of results from these discussions, as well as progress made in South Africa.
The evolutionary origin of Ebolaviruses is not very clear. The simple notion that these viruses have been circulating for many millennia in wildlife in tropical parts of Africa, occasionally spilling over into human populations, often brought on by human activities, may not be correct or at least incomplete. Over time a number of Ebola disease outbreaks reported and a pattern in the outbreak response seemed to have been established. A lot was also learnt about Ebola viruses, their epidemiology and ecology.
However, the 2014 Ebola outbreak challenges our understanding in many respects.
On the occasion of AIDS 2014, the twentieth International AIDS Conference in Melbourne, SACEMA released a policy brief on the ongoing debate about appropriate initiation of antiretroviral therapy for HIV positive people.
Robust tests for recent HIV infection (as opposed to just HIV infection) would substantially reduce the enormous challenges of estimating HIV incidence (the rate of occurrence of new infections). This article of SACEMA and the related Policy Brief provides the results of the first independent evaluation of five incidence assays conducted by the Consortium for the Evaluation and Performance of HIV Incidence Assays.
Once more we are hearing about ‘exponential growth’ – popularly some sort of synonym for ‘rapid growth’ or ‘explosive growth’ – but actually a technical term with a quite specific meaning. This time the talk is about the ongoing Ebola outbreak in West Africa, understandably causing increasing disruption (is devastation too strong a word?) in the region, and alarm much further afield.