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Int J Tuberc Lung Dis. 2014 May;18(5):509-14. doi: 10.5588/ijtld.13.0773.

How can mathematical models advance tuberculosis control in high HIV prevalence settings?

Author information

1
TB Modelling Group, TB Centre, and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, UK.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
3
Department of Global Health and Development, LSHTM, London, UK.
4
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
5
Division of Medical Microbiology and Institute of Infectious Diseases and Molecular Medicine, University of Cape Town and National Health Laboratory Service, South Africa.
6
Joint United Nations Programme on HIV/AIDS, World Health Organization (WHO), Geneva, Switzerland.
7
Oxford-Emergent Tuberculosis Consortium, Wokingham, UK.
8
Intellectual Ventures Laboratory, Bellevue, Washington, USA.
9
HIV, TB Malaria and Neglected Tropical Diseases Cluster, WHO, Geneva, Switzerland.
10
Bill and Melinda Gates Foundation, Seattle, Washington, USA.

Abstract

Existing approaches to tuberculosis (TB) control have been no more than partially successful in areas with high human immunodeficiency virus (HIV) prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing those strategies that are most likely to bring public health and economic benefits. In this paper, we present an overview of past and recent contributions of TB modelling in this key area, and suggest a way forward through a modelling research agenda that supports a more effective response to the TB-HIV epidemic, based on expert discussions at a meeting convened by the TB Modelling and Analysis Consortium. The research agenda identified high-priority areas for future modelling efforts, including 1) the difficult diagnosis and high mortality of TB-HIV; 2) the high risk of disease progression; 3) TB health systems in high HIV prevalence settings; 4) uncertainty in the natural progression of TB-HIV; and 5) combined interventions for TB-HIV. Efficient and rapid progress towards completion of this modelling agenda will require co-ordination between the modelling community and key stakeholders, including advocates, health policy makers, donors and national or regional finance officials. A continuing dialogue will ensure that new results are effectively communicated and new policy-relevant questions are addressed swiftly.

PMID:
24903784
PMCID:
PMC4436821
DOI:
10.5588/ijtld.13.0773
[Indexed for MEDLINE]
Free PMC Article

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