Spatially extended SHAR epidemiological framework of infectious disease transmission

Autores
Knopoff, Damián Alejandro; Cusimano, Nicole; Stollenwerk, Nico; Aguiar, Maíra
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health questions, e.g., when wanting to differentiate transmission patterns across geographical regions or when considering spatially heterogeneous intervention measures. However, the introduction of spatial effects can have significant consequences on the observed model dynamics and hence must be carefully analyzed and interpreted. Cellular automata epidemiological models typically rely on simplified computational grids but can provide valuable insight into the spatial dynamics of transmission within a population by suitably accounting for the connections between individuals in the considered community. In this paper, we describe a stochastic cellular automata disease model based on an extension of the traditional Susceptible-Infected-Recovered (SIR) compartmentalization of the population, namely, the Susceptible-Hospitalized-Asymptomatic-Recovered (SHAR) formulation, in which infected individuals either present a severe form of the disease, thus requiring hospitalization, or belong to the so-called mild/asymptomatic class. The critical transmission threshold is derived analytically in the nonspatial SHAR formulation, and this generalizes previously obtained theoretical results for the SIR model. We present simulation results discussing the effect of key model parameters and of spatial correlations on model outputs and propose an algorithm for tracking the evolution of infection clusters within the considered population. Focusing on the role of import and criticality on the overall dynamics, we conclude that the current spatial setting increases the critical transmission threshold in comparison to the nonspatial model.
Fil: Knopoff, Damián Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Cusimano, Nicole. Basque Center For Applied Mathematics; España
Fil: Stollenwerk, Nico. Basque Center For Applied Mathematics; España
Fil: Aguiar, Maíra. Basque Center For Applied Mathematics; España
Materia
EPIDEMIOLOGICAL MODELS
SPATIAL MODELS
CELLULAR AUTOMATA
DISEASE TRANSMISSION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/203395
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Spatially extended SHAR epidemiological framework of infectious disease transmissionKnopoff, Damián AlejandroCusimano, NicoleStollenwerk, NicoAguiar, MaíraEPIDEMIOLOGICAL MODELSSPATIAL MODELSCELLULAR AUTOMATADISEASE TRANSMISSIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health questions, e.g., when wanting to differentiate transmission patterns across geographical regions or when considering spatially heterogeneous intervention measures. However, the introduction of spatial effects can have significant consequences on the observed model dynamics and hence must be carefully analyzed and interpreted. Cellular automata epidemiological models typically rely on simplified computational grids but can provide valuable insight into the spatial dynamics of transmission within a population by suitably accounting for the connections between individuals in the considered community. In this paper, we describe a stochastic cellular automata disease model based on an extension of the traditional Susceptible-Infected-Recovered (SIR) compartmentalization of the population, namely, the Susceptible-Hospitalized-Asymptomatic-Recovered (SHAR) formulation, in which infected individuals either present a severe form of the disease, thus requiring hospitalization, or belong to the so-called mild/asymptomatic class. The critical transmission threshold is derived analytically in the nonspatial SHAR formulation, and this generalizes previously obtained theoretical results for the SIR model. We present simulation results discussing the effect of key model parameters and of spatial correlations on model outputs and propose an algorithm for tracking the evolution of infection clusters within the considered population. Focusing on the role of import and criticality on the overall dynamics, we conclude that the current spatial setting increases the critical transmission threshold in comparison to the nonspatial model.Fil: Knopoff, Damián Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Cusimano, Nicole. Basque Center For Applied Mathematics; EspañaFil: Stollenwerk, Nico. Basque Center For Applied Mathematics; EspañaFil: Aguiar, Maíra. Basque Center For Applied Mathematics; EspañaHindawi Publishing Corporation2022-02-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/203395Knopoff, Damián Alejandro; Cusimano, Nicole; Stollenwerk, Nico; Aguiar, Maíra; Spatially extended SHAR epidemiological framework of infectious disease transmission; Hindawi Publishing Corporation; Computational and Mathematical Methods; 3304532; 13-2-2022; 1-142577-7408CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/cmm/2022/3304532/info:eu-repo/semantics/altIdentifier/doi/10.1155/2022/3304532info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:22:16Zoai:ri.conicet.gov.ar:11336/203395instacron: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:34982025-10-15 14:22:16.747CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Spatially extended SHAR epidemiological framework of infectious disease transmission
title Spatially extended SHAR epidemiological framework of infectious disease transmission
spellingShingle Spatially extended SHAR epidemiological framework of infectious disease transmission
Knopoff, Damián Alejandro
EPIDEMIOLOGICAL MODELS
SPATIAL MODELS
CELLULAR AUTOMATA
DISEASE TRANSMISSION
title_short Spatially extended SHAR epidemiological framework of infectious disease transmission
title_full Spatially extended SHAR epidemiological framework of infectious disease transmission
title_fullStr Spatially extended SHAR epidemiological framework of infectious disease transmission
title_full_unstemmed Spatially extended SHAR epidemiological framework of infectious disease transmission
title_sort Spatially extended SHAR epidemiological framework of infectious disease transmission
dc.creator.none.fl_str_mv Knopoff, Damián Alejandro
Cusimano, Nicole
Stollenwerk, Nico
Aguiar, Maíra
author Knopoff, Damián Alejandro
author_facet Knopoff, Damián Alejandro
Cusimano, Nicole
Stollenwerk, Nico
Aguiar, Maíra
author_role author
author2 Cusimano, Nicole
Stollenwerk, Nico
Aguiar, Maíra
author2_role author
author
author
dc.subject.none.fl_str_mv EPIDEMIOLOGICAL MODELS
SPATIAL MODELS
CELLULAR AUTOMATA
DISEASE TRANSMISSION
topic EPIDEMIOLOGICAL MODELS
SPATIAL MODELS
CELLULAR AUTOMATA
DISEASE TRANSMISSION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health questions, e.g., when wanting to differentiate transmission patterns across geographical regions or when considering spatially heterogeneous intervention measures. However, the introduction of spatial effects can have significant consequences on the observed model dynamics and hence must be carefully analyzed and interpreted. Cellular automata epidemiological models typically rely on simplified computational grids but can provide valuable insight into the spatial dynamics of transmission within a population by suitably accounting for the connections between individuals in the considered community. In this paper, we describe a stochastic cellular automata disease model based on an extension of the traditional Susceptible-Infected-Recovered (SIR) compartmentalization of the population, namely, the Susceptible-Hospitalized-Asymptomatic-Recovered (SHAR) formulation, in which infected individuals either present a severe form of the disease, thus requiring hospitalization, or belong to the so-called mild/asymptomatic class. The critical transmission threshold is derived analytically in the nonspatial SHAR formulation, and this generalizes previously obtained theoretical results for the SIR model. We present simulation results discussing the effect of key model parameters and of spatial correlations on model outputs and propose an algorithm for tracking the evolution of infection clusters within the considered population. Focusing on the role of import and criticality on the overall dynamics, we conclude that the current spatial setting increases the critical transmission threshold in comparison to the nonspatial model.
Fil: Knopoff, Damián Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Cusimano, Nicole. Basque Center For Applied Mathematics; España
Fil: Stollenwerk, Nico. Basque Center For Applied Mathematics; España
Fil: Aguiar, Maíra. Basque Center For Applied Mathematics; España
description Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health questions, e.g., when wanting to differentiate transmission patterns across geographical regions or when considering spatially heterogeneous intervention measures. However, the introduction of spatial effects can have significant consequences on the observed model dynamics and hence must be carefully analyzed and interpreted. Cellular automata epidemiological models typically rely on simplified computational grids but can provide valuable insight into the spatial dynamics of transmission within a population by suitably accounting for the connections between individuals in the considered community. In this paper, we describe a stochastic cellular automata disease model based on an extension of the traditional Susceptible-Infected-Recovered (SIR) compartmentalization of the population, namely, the Susceptible-Hospitalized-Asymptomatic-Recovered (SHAR) formulation, in which infected individuals either present a severe form of the disease, thus requiring hospitalization, or belong to the so-called mild/asymptomatic class. The critical transmission threshold is derived analytically in the nonspatial SHAR formulation, and this generalizes previously obtained theoretical results for the SIR model. We present simulation results discussing the effect of key model parameters and of spatial correlations on model outputs and propose an algorithm for tracking the evolution of infection clusters within the considered population. Focusing on the role of import and criticality on the overall dynamics, we conclude that the current spatial setting increases the critical transmission threshold in comparison to the nonspatial model.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-13
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/11336/203395
Knopoff, Damián Alejandro; Cusimano, Nicole; Stollenwerk, Nico; Aguiar, Maíra; Spatially extended SHAR epidemiological framework of infectious disease transmission; Hindawi Publishing Corporation; Computational and Mathematical Methods; 3304532; 13-2-2022; 1-14
2577-7408
CONICET Digital
CONICET
url http://hdl.handle.net/11336/203395
identifier_str_mv Knopoff, Damián Alejandro; Cusimano, Nicole; Stollenwerk, Nico; Aguiar, Maíra; Spatially extended SHAR epidemiological framework of infectious disease transmission; Hindawi Publishing Corporation; Computational and Mathematical Methods; 3304532; 13-2-2022; 1-14
2577-7408
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/cmm/2022/3304532/
info:eu-repo/semantics/altIdentifier/doi/10.1155/2022/3304532
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
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 13.22299

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