The Mycobacterium tuberculosis (TB) bacilli’s potency to cause persistent latent infection that is unresponsive to the current cocktail of TB drugs is strongly associated with its ability to adapt to changing intracellular environments, and tolerating, evading and subverting host defence mechanisms. We applied a combination of bioinformatics and mathematical modelling methods to enhance the understanding Read More
The introduction and scale-up of new tools for the diagnosis of tuberculosis (TB) has the potential to make a huge difference to the lives of millions of people. To realise these benefits and make the best decisions, policy makers need answers to many difficult questions about which new tools to implement and where in the diagnostic algorithm to apply them cost effectively. Here we explore virtual implementation as a tool to predict the health system, patient, and community impacts of alternative diagnostics and diagnostic algorithms for TB, in order to facilitate context specific decisions on scale-up. Virtual implementation is an approach that can model the impacts of implementation of a new diagnostics by taking data from the context being considered alongside data from contexts where the new technology has been implemented (probably as a trial).
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.
Although antibiotics have saved millions of lives, their use and misuse has often led to the development of resistance in the agents that they are intended to fight. SACEMA's Brian Williams recently published a commentary on an article in published in Science Translational Medicine in which Sergeev et al. used dynamical models to investigate the Read More
There are few students in epidemiological modeling and analysis who can resist the temptation to fit a theoretical disease model to real epidemic data. A recent DNA fingerprinting project from Masiphumelele, a township near Cape Town, offered such a temptation. The result is a short journey into the world of statistically rare events, in this case brought about by the relatively small size of Masiphumele and by the slow reactivation rates of TB.
Transmission of tuberculosis (TB) involves random processes operating at two very different levels. On the one hand, an infectious person must be in reasonably close proximity to a susceptible person for a minimum period of time (macro-level). On the other hand, minute droplets containing bacteria entities that are exhaled by the infectious person are inhaled by the recipient (micro-level). At both these levels the chance events can be described by a statistical formula, the Poisson distribution. An analysis of these two processes in this way reveals a surprising phenomenon that manifests in communities experiencing high incidences of TB disease.
In South Africa, the tuberculosis infector pool is growing rapidly because neither the epidemic nor the fundamentals of control are clearly understood. The central issue in the strategy to control tuberculosis in South Africa is an understanding of the nature and extent of the infector pool and the dynamics of its maintenance and expansion. The combination of the unrestricted spread of HIV and an ineffective unbalanced tuberculosis control strategy is deadly. This article gives some (historic) background on the issue of the infector pool and proposes a way forward for tuberculosis control.
Given the enormous burden that the current HIV/AIDS and TB epidemics have imposed on rural areas in the eastern and central provinces of South Africa, how much attention should the National and Provincial governments be paying to zoonotic diseases such as bovine TB (BTB) in these areas? The answer lies not only in the extent to which BTB posses an additional burden on human health in the region, but also on the degree to which it threatens food security, exacerbates poverty, and threatens conservation and green tourism.
There are many different strains of TB. If there happen to be many strains circulating in a population, then individuals could be infected with more than one strain at a time which we define as mixed infection. This article focuses on the question whether mixed infection can explain the high prevalence of TB in some areas with overcrowding, low HIV prevalence and a high diversity in TB strains. The aim was to identify the factors that characterize mixed infection and investigate their impact on both the prevalence of TB and the proportion of mixed infection in these areas. To investigate the impact of these parameters, a mathematical model was developed for TB transmission dynamics that accounts for mixed infection.
In the last 20 years the number of new tuberculosis (TB) cases had tripled in high HIV prevalence countries, and at least a third of the world’s 33.2 million persons living with HIV/AIDS (PLWHA) are infected with TB. Approximately 80% of people with TB/HIV infections live in sub-Saharan Africa, where TB is the leading cause Read More