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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/203395
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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 Técnicas2025-09-29T09:34:04Zoai: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:34982025-09-29 09:34:04.549CONICET 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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1844613052249407488 |
score |
13.070432 |