Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/583819 |
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| _version_ | 1832549497795772416 |
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| author | David Rehkopf Alice Furumoto-Dawson Anthony Kiszewski Tamara Awerbuch-Friedlander |
| author_facet | David Rehkopf Alice Furumoto-Dawson Anthony Kiszewski Tamara Awerbuch-Friedlander |
| author_sort | David Rehkopf |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36d9d5a02a184365bf3e45ed5a4acbfd |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-36d9d5a02a184365bf3e45ed5a4acbfd2025-02-03T06:11:11ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/583819583819Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata ModelDavid Rehkopf0Alice Furumoto-Dawson1Anthony Kiszewski2Tamara Awerbuch-Friedlander3Department of Medicine, School of Medicine, Stanford University, Medical School Office Building, 251 Campus Drive, Room X3c46, MC5411, Stanford, CA 94305, USAProgram on the Global Environment, The University of Chicago, 5828 S. University Avenue, Pick 101, Chicago, IL 60637, USADepartment of Natural and Applied Sciences, Bentley University, 175 Forest Street, Waltham, MA 02452, USADepartment of Global Health and Population, Harvard School of Public Health, 665 Huntington Avenue, Room 1219, Boston, MA 02115, USATransmission 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.http://dx.doi.org/10.1155/2015/583819 |
| spellingShingle | David Rehkopf Alice Furumoto-Dawson Anthony Kiszewski Tamara Awerbuch-Friedlander Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model Discrete Dynamics in Nature and Society |
| title | Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model |
| title_full | Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model |
| title_fullStr | Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model |
| title_full_unstemmed | Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model |
| title_short | Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model |
| title_sort | spatial spread of tuberculosis through neighborhoods segregated by socioeconomic position a stochastic automata model |
| url | http://dx.doi.org/10.1155/2015/583819 |
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