Evaluation of Tuberculosis Control Policies Using Computer Simulation

Tf, Brewer; Sj, Heymann; Ga, Colditz; Me, Wilson; Auerbach, K; Kane, Dianna; Hv, Fineberg

doi:10.1001/jama.1996.03540230048034

Cited by 30 publications

(7 citation statements)

References 41 publications

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“…Recently, there has been renewed interest in using mathematical models to study tuberculosis epidemics (11)(12)(13)(14). Blower et al (11,12) have used a simple analytical framework to calculate threshold levels of preventive therapy and treatment necessary for tuberculosis eradication.…”

mentioning

confidence: 99%

Modeling the impact of global tuberculosis control strategies

Murray¹,

Salomon²

1998

Proc. Natl. Acad. Sci. U.S.A.

206

135

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An epidemiological model of tuberculosis has been developed and applied to five regions of the world. Globally, 6.7 million new cases of tuberculosis and 2.4 million deaths from tuberculosis are estimated for 1998. Based on current trends in uptake of the World Health Organization's strategy of directly observed treatment, short-course, we expect a total of 225 million new cases and 79 million deaths from tuberculosis between 1998 and 2030. Active case-finding by using mass miniature radiography could save 23 million lives over this period. A single contact treatment for tuberculosis could avert 24 million cases and 11 million deaths; combined with active screening, it could reduce mortality by nearly 40%. A new vaccine with 50% efficacy could lower incidence by 36 million cases and mortality by 9 million deaths. Support for major extensions to global tuberculosis control strategies will occur only if the size of the problem and the potential for action are recognized more widely.

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mentioning

confidence: 99%

Modeling the impact of global tuberculosis control strategies

Murray¹,

Salomon²

1998

Proc. Natl. Acad. Sci. U.S.A.

206

135

View full text Add to dashboard Cite

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“…Brewer et al built a multilevel semi-Markov model that divided the US population into 3 age groups (0–15, 16–44, 45+), and each age group is subdivided into 18 clinical states based on the cartesian product of disease status for TB and HIV infection [106]. The six possible TB infection states are: no known infection, low/intermediate/high risk for active TB, active DS-TB, and active DR-TB.…”

Section: Modeling Various Patient Groupsmentioning

confidence: 99%

Epidemiological models of Mycobacterium tuberculosis complex infections

Ozcaglar

Shabbeer

Vandenberg

et al. 2012

Mathematical Biosciences

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The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.

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“…21 Computer simulation models of population based TB control show that combinations of treatment and prevention approaches are more effective than strategies based on treatment alone. 36 Models also show that the combined use of treatment for latent and active TB is more effective at reducing TB in HIV infected populations than current strategies, 37 and widespread treatment of latent TB infection in HIV infected persons in Africa has been recommended. 38 How treatment of latent infection might best be used for TB control in HIV endemic areas is unknown.…”

Section: Prevention Needs To Accompany Treatmentmentioning

confidence: 99%