Mathematical modelling of tuberculosis epidemics

Autores
Aparicio, Juan Pablo; Castillo Chavez, Carlos
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.
Fil: Aparicio, Juan Pablo. Universidad Metropolitana San Juan; Puerto Rico. Universidad Nacional de Salta; Argentina
Fil: Castillo Chavez, Carlos. Arizona State University. School Of Human Evolution And Social Change; Estados Unidos. Mathematical, Computational and Modeling Sciences Center; Estados Unidos. Santa Fe Institute; Estados Unidos
Materia
Demography
Non-Autonomous Systems
Stochastic Models
Tuberculosis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/71276
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/71276
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Mathematical modelling of tuberculosis epidemicsAparicio, Juan PabloCastillo Chavez, CarlosDemographyNon-Autonomous SystemsStochastic ModelsTuberculosishttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.Fil: Aparicio, Juan Pablo. Universidad Metropolitana San Juan; Puerto Rico. Universidad Nacional de Salta; ArgentinaFil: Castillo Chavez, Carlos. Arizona State University. School Of Human Evolution And Social Change; Estados Unidos. Mathematical, Computational and Modeling Sciences Center; Estados Unidos. Santa Fe Institute; Estados UnidosAmerican Institute of Mathematical Sciences2009-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/71276Aparicio, Juan Pablo; Castillo Chavez, Carlos; Mathematical modelling of tuberculosis epidemics; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 6; 2; 4-2009; 209-2371547-1063CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3934/mbe.2009.6.209info:eu-repo/semantics/altIdentifier/url/www.aimsciences.org/article/doi/10.3934/mbe.2009.6.209info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:52:06Zoai:ri.conicet.gov.ar:11336/71276instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:52:06.288CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Mathematical modelling of tuberculosis epidemics
title Mathematical modelling of tuberculosis epidemics
spellingShingle Mathematical modelling of tuberculosis epidemics
Aparicio, Juan Pablo
Demography
Non-Autonomous Systems
Stochastic Models
Tuberculosis
title_short Mathematical modelling of tuberculosis epidemics
title_full Mathematical modelling of tuberculosis epidemics
title_fullStr Mathematical modelling of tuberculosis epidemics
title_full_unstemmed Mathematical modelling of tuberculosis epidemics
title_sort Mathematical modelling of tuberculosis epidemics
dc.creator.none.fl_str_mv Aparicio, Juan Pablo
Castillo Chavez, Carlos
author Aparicio, Juan Pablo
author_facet Aparicio, Juan Pablo
Castillo Chavez, Carlos
author_role author
author2 Castillo Chavez, Carlos
author2_role author
dc.subject.none.fl_str_mv Demography
Non-Autonomous Systems
Stochastic Models
Tuberculosis
topic Demography
Non-Autonomous Systems
Stochastic Models
Tuberculosis
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.
Fil: Aparicio, Juan Pablo. Universidad Metropolitana San Juan; Puerto Rico. Universidad Nacional de Salta; Argentina
Fil: Castillo Chavez, Carlos. Arizona State University. School Of Human Evolution And Social Change; Estados Unidos. Mathematical, Computational and Modeling Sciences Center; Estados Unidos. Santa Fe Institute; Estados Unidos
description The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.
publishDate 2009
dc.date.none.fl_str_mv 2009-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/71276
Aparicio, Juan Pablo; Castillo Chavez, Carlos; Mathematical modelling of tuberculosis epidemics; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 6; 2; 4-2009; 209-237
1547-1063
CONICET Digital
CONICET
url http://hdl.handle.net/11336/71276
identifier_str_mv Aparicio, Juan Pablo; Castillo Chavez, Carlos; Mathematical modelling of tuberculosis epidemics; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 6; 2; 4-2009; 209-237
1547-1063
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3934/mbe.2009.6.209
info:eu-repo/semantics/altIdentifier/url/www.aimsciences.org/article/doi/10.3934/mbe.2009.6.209
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Institute of Mathematical Sciences
publisher.none.fl_str_mv American Institute of Mathematical Sciences
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613599319818240
score 13.070432

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