David W Dowdy a & Richard E Chaisson b
a. University of California, San Francisco, CA United States of America.
b. Johns Hopkins University, Baltimore, MD, USA.
Correspondence to Richard E Chaisson (e-mail: email@example.com).
Bulletin of the World Health Organization 2009;87:B-C. doi: 10.2471/BLT.09.071282
Marais & van Helden provide an important historical context for the role of mathematical modelling in formulating performance targets for tuberculosis (TB) control. Furthermore, they appropriately highlight that such models serve as simplifications of a far more complex reality, in which M. tuberculosis is transmitted in heterogeneous fashion. They mention two key factors – case density and transmission saturation – that contribute to such heterogeneity. However, there are many more, including nosocomial transmission clusters,1 strains of different fitness,2 social determinants of TB transmission3 and complex interactions with the HIV co-pandemic.4 Ultimately, no model can account for all potentially relevant aspects of TB transmission. Thus, we need simple models capable of distilling key components of transmission dynamics into clear messages. However, more complex models can be created to try to show us where – and to what degree – simple models may go wrong. Models exploring case density and transmission saturation could have an important role to play in this regard, and we welcome such efforts.
Ultimately, we must also remember that mathematical models are but one component of a broader TB research agenda that is sorely in need of expansion.5 While refining our models, we must not lose sight of the fact that approaches over the past 20 years have failed to stem the tide of ongoing TB transmission and that a broad-based, concerted effort – including an expanded research agenda, relentless improvements in case detection and development of better tools for TB diagnosis and treatment – will be required to meet current goals for TB control. Over the next 20 years, the value of TB mathematical models may be measured less by their ability to accurately describe the dynamics of TB transmission, and more by their power to galvanize support and inform appropriate policy. ■
Competing interests: None declared.
- Basu S, Friedland GH, Medlock J, Andrews JR, Shah NS, Gandhi NR, et al., et al. Averting epidemics of extensively drug-resistant tuberculosis. Proc Natl Acad Sci USA 2009; 106: 7672-7 doi: 10.1073/pnas.0812472106 pmid: 19365076.
- Cohen T, Murray M. Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness. Nat Med 2004; 10: 1117-21 doi: 10.1038/nm1110 pmid: 15378056.
- Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med 2009; 68: 2240-6 doi: 10.1016/j.socscimed.2009.03.041 pmid: 19394122.
- Williams BG, Korenromp EL, Gouws E, Schmid GP, Auvert B, Dye C. HIV infection, antiretroviral therapy, and CD4+ cell count distributions in African populations. J Infect Dis 2006; 194: 1450-8 doi: 10.1086/508206 pmid: 17054076.
- Chaisson RE, Harrington M. How research can help control tuberculosis. Int J Tuberc Lung Dis 2009; 13: 558-68 pmid: 19383187.