Abstract
Tuberculosis control and elimination remains a challenge for public health even in low-burden countries. New technology and novel approaches to case-finding, diagnosis, and treatment are causes for optimism but they need to be used cost-effectively. This in turn requires improved understanding of the epidemiology of TB and analysis of the effectiveness and cost-effectiveness of different interventions. We describe the contribution that mathematical modeling can make to understanding epidemiology and control of TB in different groups, guiding improved approaches to public health interventions. We emphasize that modeling is not a substitute for collecting data but rather is complementary to empirical research, helping determine what are the key questions to address to maximize the public-health impact of research, helping to plan studies, and making maximal use of available data, particularly from surveillance, and observational studies. We provide examples of how modeling and related empirical research inform policy and discuss how a combination of these approaches can be used to address current questions of key importance, including use of whole-genome sequencing, screening and treatment for latent infection, and combating drug resistance.
Highlights
Tuberculosis remains a public health challenge, even in low-burden countries, such as the USA (Hill et al, 2012) and European countries (Abubakar et al, 2012a; Lönnroth et al, 2015)
Recent analysis informing National Institute for Health and Care Excellence (NICE) guidance on latent TB infection (LTBI) treatment examined the cost-effectiveness of different regimens and treating different age-groups: whilst older individuals will have fewer years at risk of progression to active TB, and fewer years of life lost if they develop active TB and die of it, and are at greater risk of adverse events from LTBI treatment, they have a greater risk of dying from active TB if they develop it, and modeling is required to determine the overall outcome of this combination of factors (White and Jit, 2015)
There are important questions regarding TB’s natural history and epidemiology, which will affect the impact of interventions, including use of novel technologies such as new vaccines (Table 1)
Summary
Tuberculosis remains a public health challenge, even in low-burden countries, such as the USA (Hill et al, 2012) and European countries (Abubakar et al, 2012a; Lönnroth et al, 2015). What is the impact and cost-effectiveness of whole-genome sequencing on identifying drug resistance patterns (for individual patient care) and transmission clusters (to inform public health responses)?
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