title: Epidemiological models of Mycobacterium tuberculosis complex infections.
abstract: 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.
authors.affiliation: Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, USA. ozcagc2@cs.rpi.edu
authors.forename: Cagri
authors.initials: C
authors.lastname: Ozcaglar
authors.forename: Amina
authors.initials: A
authors.lastname: Shabbeer
authors.forename: Scott L
authors.initials: SL
authors.lastname: Vandenberg
authors.forename: Bülent
authors.initials: B
authors.lastname: Yener
authors.forename: Kristin P
authors.initials: KP
authors.lastname: Bennett
mesh_terms: D000995:Antitubercular Agents / Q000627:therapeutic use; D003198:Computer Simulation; D024901:Drug Resistance, Multiple, Bacterial; D015658:HIV Infections / Q000453:epidemiology / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification*; D014376:Tuberculosis / Q000453:epidemiology* / Q000821:virology
chemical_list: D000995:Antitubercular Agents