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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 583819, 8 pages
Research Article

Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model

1Department of Medicine, School of Medicine, Stanford University, Medical School Office Building, 251 Campus Drive, Room X3c46, MC5411, Stanford, CA 94305, USA
2Program on the Global Environment, The University of Chicago, 5828 S. University Avenue, Pick 101, Chicago, IL 60637, USA
3Department of Natural and Applied Sciences, Bentley University, 175 Forest Street, Waltham, MA 02452, USA
4Department of Global Health and Population, Harvard School of Public Health, 665 Huntington Avenue, Room 1219, Boston, MA 02115, USA

Received 25 March 2015; Revised 20 June 2015; Accepted 25 June 2015

Academic Editor: Aleksei A. Koronovskii

Copyright © 2015 David Rehkopf et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Transmission of the agent of tuberculosis, Mycobacterium tuberculosis, is dependent on social context. A discrete spatial model representing neighborhoods segregated by levels of crowding and immunocompetence is constructed and used to evaluate prevention strategies, based on a number of assumptions about the spatial dynamics of tuberculosis. A cellular automata model is used to (a) construct neighborhoods of different densities, (b) model stochastically local interactions among individuals, and (c) model the spread of tuberculosis within and across neighborhoods over time. Since infected people may become progressively sick but also heal through treatment, the transition among stages was modeled with transition probabilities. A moderate level of successful treatment (40%) dramatically reduced the number of infections across all neighborhoods. Increasing the treatment in neighborhoods of a lower socioeconomic level from 40% to 90% results in an additional decrease of approximately 25% in the number of infected individuals overall. In conclusion, we find that a combination of a moderate level of successful treatment across all areas with more focused treatment efforts in lower socioeconomic areas resulted in the least number of infections over time.