Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach

Autores
López, Leonardo Rafael; Burguerner, Germán; Giovanini, Leonardo Luis
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease
Fil: López, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
Fil: Burguerner, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
Materia
CELLULAR AUTOMATA
EPIDEMY
POPULATION HETEROGENITY
PUBLIC HEALTH
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:11336/15751
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/15751
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Addressing population heterogeneity and distribution in epidemics models using a cellular automata approachLópez, Leonardo RafaelBurguerner, GermánGiovanini, Leonardo LuisCELLULAR AUTOMATAEPIDEMYPOPULATION HETEROGENITYPUBLIC HEALTHhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the deseaseFil: López, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; ArgentinaFil: Burguerner, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; ArgentinaFil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; ArgentinaBiomedical Central2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15751López, Leonardo Rafael; Burguerner, Germán; Giovanini, Leonardo Luis; Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach; Biomedical Central; BMC Research Notes; 7; 2-2014; 234-2451756-0500enginfo:eu-repo/semantics/altIdentifier/url/https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-7-234info:eu-repo/semantics/altIdentifier/doi/10.1186/1756-0500-7-234info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:05Zoai:ri.conicet.gov.ar:11336/15751instacron: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 09:56:05.784CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
spellingShingle Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
López, Leonardo Rafael
CELLULAR AUTOMATA
EPIDEMY
POPULATION HETEROGENITY
PUBLIC HEALTH
title_short Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_full Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_fullStr Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_full_unstemmed Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_sort Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
dc.creator.none.fl_str_mv López, Leonardo Rafael
Burguerner, Germán
Giovanini, Leonardo Luis
author López, Leonardo Rafael
author_facet López, Leonardo Rafael
Burguerner, Germán
Giovanini, Leonardo Luis
author_role author
author2 Burguerner, Germán
Giovanini, Leonardo Luis
author2_role author
author
dc.subject.none.fl_str_mv CELLULAR AUTOMATA
EPIDEMY
POPULATION HETEROGENITY
PUBLIC HEALTH
topic CELLULAR AUTOMATA
EPIDEMY
POPULATION HETEROGENITY
PUBLIC HEALTH
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease
Fil: López, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
Fil: Burguerner, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
description BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease
publishDate 2014
dc.date.none.fl_str_mv 2014-02
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/11336/15751
López, Leonardo Rafael; Burguerner, Germán; Giovanini, Leonardo Luis; Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach; Biomedical Central; BMC Research Notes; 7; 2-2014; 234-245
1756-0500
url http://hdl.handle.net/11336/15751
identifier_str_mv López, Leonardo Rafael; Burguerner, Germán; Giovanini, Leonardo Luis; Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach; Biomedical Central; BMC Research Notes; 7; 2-2014; 234-245
1756-0500
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-7-234
info:eu-repo/semantics/altIdentifier/doi/10.1186/1756-0500-7-234
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 Biomedical Central
publisher.none.fl_str_mv Biomedical Central
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
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score 13.070432

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