Preclinical model development
People are often affected by conditions that increase their risk of developing TB. These conditions include HIV, obesity and diabetes. New animal models will be developed to better reflect these human conditions that are associated with increased TB disease risk. This will improve our chances of identifying vaccines to protect vulnerable populations.
TB Modelling and Analysis Consortium
The TB Modelling and Analysis Consortium (TB MAC) is a global community of TB modellers who provide quantitative support for TB policy decisions and implementation. By promoting collaboration and funding research projects, TB MAC has since its inception in 2012 been of great influence in policy debates, and developing the global field of TB modelling.
On the global policy level, TB MAC has updated the World Health Organization methods used to estimate the amount of HIV-related TB globally, and provided input on technical discussions that resulted in the decision by the Global Fund to Fight AIDS, TB and Malaria not to reduce the proportion of funds it allocates to TB. In recent years, TB MAC has also supported the development of tools to integrate modelling into country-level policy discussions. These have been used in collaborations with UNAIDS, the World Health Organization, and the governments of Vietnam, Ghana, South Africa.
In response to the post-2015 End TB Strategy, TB MAC has led the first multi-model TB comparison exercise to assess the feasibility of the new targets of 50% reduction in TB incidence and 75% reduction in TB mortality by 2025 for China, India and South Africa. This work coordinated the efforts of 11 modelling groups, a team of economists and representatives from the country National TB Programmes and advocacy communities. Results from this work have informed policy discussions, in particular in South Africa, where the work has influenced budgets and operational research priorities. The modelling is being used in South Africa in their first ever combined TB and HIV Investment Case.
The consortium is open to anyone who is interested in quantitative methods to improve TB policy and practice. It is funded by a grant to the School from the Bill & Melinda Gates Foundation.
Houben, R. M., D. W. Dowdy, A. Vassall, T. Cohen, M. P. Nicol, R. M. Granich, J. E. Shea, P. Eckhoff, C. Dye, M. E. Kimerling, R. G. White and T. M. T.-H. m. participants (2014). “How can mathematical models advance tuberculosis control in high HIV prevalence settings?” Int J Tuberc Lung Dis 18(5): 509-514.
Zwerling, A., R. G. White, A. Vassall, T. Cohen, D. W. Dowdy and R. M. G. J. Houben (2014). “Modeling of Novel Diagnostic Strategies for Active Tuberculosis – A Systematic Review: Current Practices and Recommendations.” PLoS ONE 9(10): e110558.
TB Impact and Model Estimates (TIME)
TIME is a modelling tool created in 2013 and designed for local TB programme planners in resource constrained settings. Implementation
has four key goals:
(1) enable NTPs to investigate different policy options and support efficient resource allocation;
(2) equip NTPs with the technical skills to use modelling for decision-making;
(3) promote the use of locally-generated evidence for strategic planning;
(4) provide a platform for data collation and promote operational research to strengthen country data.
TIME is being used by NTPs and technical agencies in > 10 high-burden countries including Indonesia, Vietnam and Nigeria.
The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling
The long held mantra that one third of the global population has latent TB infection has now been revised to one quarter (1.7 billion people) as a result of work done at LSHTM. Further, it was estimated that even if all prevalent TB cases were immediately successfully diagnosed and treated at the start of 2015, this reservoir alone would generate an incidence of 8.3 per 100,000 each year in 2050, highlighting the critical need for new tools and approaches to LTBI diagnosis and treatment.
TB Centre members: Rein Houben