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A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance
BACKGROUND: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the wo...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994743/ https://www.ncbi.nlm.nih.gov/pubmed/21152440 http://dx.doi.org/10.1371/journal.pone.0014140 |
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author | Zorzenon dos Santos, Rita M. Amador, Ana de Souza, Wayner V. de Albuquerque, Maria Fatima P. M. Ponce Dawson, Silvina Ruffino-Netto, Antonio Zárate-Bladés, Carlos R. Silva, Celio L. |
author_facet | Zorzenon dos Santos, Rita M. Amador, Ana de Souza, Wayner V. de Albuquerque, Maria Fatima P. M. Ponce Dawson, Silvina Ruffino-Netto, Antonio Zárate-Bladés, Carlos R. Silva, Celio L. |
author_sort | Zorzenon dos Santos, Rita M. |
collection | PubMed |
description | BACKGROUND: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the world, require methods that take into account the disease dynamics to design truly efficient control and surveillance strategies. The usual and well established statistical approaches provide insights into the cause-effect relationships that favour disease transmission but they only estimate risk areas, spatial or temporal trends. Here we introduce a novel approach that allows figuring out the dynamical behaviour of the disease spreading. This information can subsequently be used to validate mathematical models of the dissemination process from which the underlying mechanisms that are responsible for this spreading could be inferred. METHODOLOGY/PRINCIPAL FINDINGS: The method presented here is based on the analysis of the spread of tuberculosis in a Brazilian endemic city during five consecutive years. The detailed analysis of the spatio-temporal correlation of the yearly geo-referenced data, using different characteristic times of the disease evolution, allowed us to trace the temporal path of the aetiological agent, to locate the sources of infection, and to characterize the dynamics of disease spreading. Consequently, the method also allowed for the identification of socio-economic factors that influence the process. CONCLUSIONS/SIGNIFICANCE: The information obtained can contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs. We propose that this novel strategy can also be applied to the evaluation of other diseases as well as other social processes. |
format | Text |
id | pubmed-2994743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-29947432010-12-08 A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance Zorzenon dos Santos, Rita M. Amador, Ana de Souza, Wayner V. de Albuquerque, Maria Fatima P. M. Ponce Dawson, Silvina Ruffino-Netto, Antonio Zárate-Bladés, Carlos R. Silva, Celio L. PLoS One Research Article BACKGROUND: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the world, require methods that take into account the disease dynamics to design truly efficient control and surveillance strategies. The usual and well established statistical approaches provide insights into the cause-effect relationships that favour disease transmission but they only estimate risk areas, spatial or temporal trends. Here we introduce a novel approach that allows figuring out the dynamical behaviour of the disease spreading. This information can subsequently be used to validate mathematical models of the dissemination process from which the underlying mechanisms that are responsible for this spreading could be inferred. METHODOLOGY/PRINCIPAL FINDINGS: The method presented here is based on the analysis of the spread of tuberculosis in a Brazilian endemic city during five consecutive years. The detailed analysis of the spatio-temporal correlation of the yearly geo-referenced data, using different characteristic times of the disease evolution, allowed us to trace the temporal path of the aetiological agent, to locate the sources of infection, and to characterize the dynamics of disease spreading. Consequently, the method also allowed for the identification of socio-economic factors that influence the process. CONCLUSIONS/SIGNIFICANCE: The information obtained can contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs. We propose that this novel strategy can also be applied to the evaluation of other diseases as well as other social processes. Public Library of Science 2010-11-30 /pmc/articles/PMC2994743/ /pubmed/21152440 http://dx.doi.org/10.1371/journal.pone.0014140 Text en Zorzenon dos Santos et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zorzenon dos Santos, Rita M. Amador, Ana de Souza, Wayner V. de Albuquerque, Maria Fatima P. M. Ponce Dawson, Silvina Ruffino-Netto, Antonio Zárate-Bladés, Carlos R. Silva, Celio L. A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title | A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title_full | A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title_fullStr | A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title_full_unstemmed | A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title_short | A Dynamic Analysis of Tuberculosis Dissemination to Improve Control and Surveillance |
title_sort | dynamic analysis of tuberculosis dissemination to improve control and surveillance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994743/ https://www.ncbi.nlm.nih.gov/pubmed/21152440 http://dx.doi.org/10.1371/journal.pone.0014140 |
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