A dynamic analysis of tuberculosis dissemination to improve control and surveillance
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...
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creator | 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 |
description | 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.
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.
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. |
doi_str_mv | 10.1371/journal.pone.0014140 |
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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.
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0014140</identifier><identifier>PMID: 21152440</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Brazil - epidemiology ; Cause-effect relationships ; Control ; Correlation analysis ; Disease control ; Disease transmission ; Diseases ; Economic analysis ; Economic factors ; Epidemiology ; Geography ; Geospatial data ; Health risks ; Humans ; Incidence ; Infectious Diseases/Epidemiology and Control of Infectious Diseases ; Infectious Diseases/Respiratory Infections ; Infectious Diseases/Tropical and Travel-Associated Diseases ; Information dissemination ; Information processing ; Mathematical models ; Medical research ; Mortality ; Mycobacterium ; Population Density ; Population Dynamics ; Population Surveillance - methods ; Public Health and Epidemiology/Preventive Medicine ; Public Health and Epidemiology/Social and Behavioral Determinants of Health ; Recruitment ; Socioeconomic Factors ; Spreading ; Surveillance ; Tuberculosis ; Tuberculosis - epidemiology ; Tuberculosis - prevention & control ; Tuberculosis - transmission ; Vaccination</subject><ispartof>PloS one, 2010-11, Vol.5 (11), p.e14140</ispartof><rights>COPYRIGHT 2010 Public Library of Science</rights><rights>2010 Zorzenon dos Santos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Zorzenon dos Santos et al. 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c756t-d105f76d555cfe84ee331f78d37ccd75589abc221e100ad72ae0ade019c4d6c93</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994743/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994743/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21152440$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zorzenon dos Santos, Rita M</creatorcontrib><creatorcontrib>Amador, Ana</creatorcontrib><creatorcontrib>de Souza, Wayner V</creatorcontrib><creatorcontrib>de Albuquerque, Maria Fatima P M</creatorcontrib><creatorcontrib>Ponce Dawson, Silvina</creatorcontrib><creatorcontrib>Ruffino-Netto, Antonio</creatorcontrib><creatorcontrib>Zárate-Bladés, Carlos R</creatorcontrib><creatorcontrib>Silva, Celio L</creatorcontrib><title>A dynamic analysis of tuberculosis dissemination to improve control and surveillance</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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.
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. 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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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>21152440</pmid><doi>10.1371/journal.pone.0014140</doi><tpages>e14140</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Brazil - epidemiology Cause-effect relationships Control Correlation analysis Disease control Disease transmission Diseases Economic analysis Economic factors Epidemiology Geography Geospatial data Health risks Humans Incidence Infectious Diseases/Epidemiology and Control of Infectious Diseases Infectious Diseases/Respiratory Infections Infectious Diseases/Tropical and Travel-Associated Diseases Information dissemination Information processing Mathematical models Medical research Mortality Mycobacterium Population Density Population Dynamics Population Surveillance - methods Public Health and Epidemiology/Preventive Medicine Public Health and Epidemiology/Social and Behavioral Determinants of Health Recruitment Socioeconomic Factors Spreading Surveillance Tuberculosis Tuberculosis - epidemiology Tuberculosis - prevention & control Tuberculosis - transmission Vaccination |
title | A dynamic analysis of tuberculosis dissemination to improve control and surveillance |
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