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.
Methamphetamine (MA), commonly known by the street name “tik” in South Africa, is a highly addictive stimulant whose production and abuse has increased dramatically. Many questions remain unanswered as to how prevalent is drug abuse and the implications of drug use, especially on disease burden, healthcare and budgetary demands as well as risky behaviour. There is a need to understand the problem, measure drug use trends, design appropriate intervention measures and evaluate the success of these interventions. As is demonstrated here mathematical models can help in modelling the “tik” epidemic.
Welcome to a special edition of SACEMA quarterly epidemiological update – dedicated to SACEMA’s annual ‘research days’ event and other SACEMA related work. This is a glimpse into exciting trends in public health research, where mathematical methods are increasingly applied to a range of problems, to help leverage limited data, think about prospects for interventions, and formulate new hypotheses and experiments. The article also includes a reflection on modelling as a universal practice in all of science and all that differs are the kinds of models, and the techniques used to set them up and manipulate them.
The primary goal of this article is to model the forces (rates) of recovery, relapse and mortality for patients started on rheumatoid arthritis (RA) standard treatment and the effect of adjusting for body functionality. A four state model inclusive of the absorbing state death was fit to these data. A consequence of an improved health is better body functionality, which was measured using a health assessment disability index (HAQ-DI). The modelling was done using a member of the generalised additive mixed models (GAMMs) which utilise nonparametric functions adjusting for over-dispersion and correlation. Based on the results we advocate that patients should be treated until the disease activity score is in remission or lowest possible to enable greater physical functionality whilst alleviating disability and mortality due to RA.
One of the articles in this edition concerns the modelling of the control of the tsetse-borne disease trypanosomiasis using trypanocides or insecticide-treated livestock. SACEMA has been short-listed for WHO/TDR funding of a project focussing on modelling the way in which various climate change scenarios might affect the population dynamics of tsetse flies and the trypanosomes that they transmit. For this study we have access to large, long-term, unique archives of data of the type required to address these questions. These data will be augmented during the study through field studies in Zimbabwe and Tanzania, aimed at understanding the spatiotemporal variability of disease threat and how this is likely to change at different locations and altitudes in the context of climate change. Field studies will address particularly the problem of the interface between humans and tsetse, and suggest optimal methods of disease control.
Across sub-Saharan Africa, several species of trypanosome, transmitted by tsetse flies (Glossina spp), cause human and animal trypanosomiasis. While interventions can be directed against either the vector or the parasite, emphasis has usually been on the use of drugs to treat the disease both in humans and in livestock. Several advances in our understanding of tsetse biology and ecology and improvements in the cost-effectiveness of tsetse control have revived interest in the vector control approach to disease management. This article discusses and compares two different approaches to the control of trypanosomiasis in cattle: either we can control the disease by treating cattle with insecticides that kill the tsetse vectors without having any direct effect on the trypanosomes. Or we can inject the cattle with trypanocides that kill the parasites but leave the tsetse flies unharmed.
A new case of TB is the outcome of a recent infection event (primary TB) or is the result of the reactivation of a latent infection acquired some years previously. In a community where TB is endemic it is important to know the extent to which primary cases contribute to the overall burden as this can inform strategies to deal with the epidemic. This article discusses the methods for estimating the proportion of cases due to recent transmission by using cluster analysis. Sputum specimens from cases reporting to clinics are cultured and the TB strains are identified, commonly using molecular techniques of DNA ‘fingerprinting’. By comparing these fingerprints from various patients it becomes possible to classify them as unique or clustered. The proportion of clustered individuals can then be used as an indicator of the proportion of on-going or recent transmission.
Many mathematical models have investigated the impact of HIV treatment as prevention in combination with other prevention strategies or other guidelines for HIV treatment provision. Generally, all models have predicted positive prevention benefits of HIV treatment, but directly comparing the results of different models has been challenging because each model has been used to answer different questions and has reported different key outcomes. In November 2011, the HIV Modelling Consortium convened a meeting with the aim to understand the extent to which different mathematical models agree about the potential impact of HIV treatment. The results of a model comparison exercise – in which each of the models simulated a standardised set of HIV intervention scenarios and reported common metrics of intervention impact – are reported here.
HIV evolution is sufficiently complex that intuition alone is insufficient to understand its dynamics. Mathematical models attempt to explain real events, observed or not. In this area, studies in mathematical modelling have contributed to the knowledge, for example it was through models that Perelson et al. revealed the high viral turnover in HIV infected individuals Read More