Inference in epidemiological agent-based models using ensemble-based data assimilation

Autores
Cocucci, Tadeo Javier; Pulido, Manuel Arturo; Aparicio, Juan Pablo; Ruíz, Juan; Simoy, Mario Ignacio; Rosa, Santiago
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Cocucci, Tadeo Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Cocucci, Tadeo Javier. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Fil: Pulido, Manuel Arturo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco-Argentino sobre el Estudio del Clima y sus Impactos; Argentina.
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Modelado e Innovación Tecnológica; Argentina.
Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.
Fil: Aparicio, Juan Pablo. Arizona State University. Simon A. Levin Mathematical, Computational and Modeling Sciences Center; Estados Unidos de America.
Fil: Ruíz, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones del Mar y la Atmósfera; Argentina.
Fil: Ruíz, Juan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Simoy, Mario Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.
Fil: Simoy, Mario Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
Fil: Rosa, Santiago. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Rosa, Santiago. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Torepresent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agentbased models candescribe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 andassess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Auto´noma de Buenos Aires, Argentina.
Fuente
Plos One, 2022, vol. 17, no. 3, p. 1-28.
Materia
Agent-based modeling
COVID 19
Infectious disease epidemiology
Epidemiological modeling
Ensemble Kalman filter
Data assimilation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
Institución
Universidad Nacional del Nordeste
OAI Identificador
oai:repositorio.unne.edu.ar:123456789/60141
Acceder
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network_acronym_str RIUNNE
repository_id_str 4871
network_name_str Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
spelling Inference in epidemiological agent-based models using ensemble-based data assimilationCocucci, Tadeo JavierPulido, Manuel ArturoAparicio, Juan PabloRuíz, JuanSimoy, Mario IgnacioRosa, SantiagoAgent-based modelingCOVID 19Infectious disease epidemiologyEpidemiological modelingEnsemble Kalman filterData assimilationFil: Cocucci, Tadeo Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Cocucci, Tadeo Javier. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.Fil: Pulido, Manuel Arturo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco-Argentino sobre el Estudio del Clima y sus Impactos; Argentina.Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Modelado e Innovación Tecnológica; Argentina.Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.Fil: Aparicio, Juan Pablo. Arizona State University. Simon A. Levin Mathematical, Computational and Modeling Sciences Center; Estados Unidos de America.Fil: Ruíz, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones del Mar y la Atmósfera; Argentina.Fil: Ruíz, Juan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Simoy, Mario Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.Fil: Simoy, Mario Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.Fil: Rosa, Santiago. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Rosa, Santiago. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.Torepresent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agentbased models candescribe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 andassess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Auto´noma de Buenos Aires, Argentina.Public Library of Science2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfp. 1-28application/pdfCocucci, Tadeo Javier, et al., 2022. Inference in epidemiological agent-based models using ensemble-based data assimilation. Plos One. San Francisco: Public Library of Science, vol. 17, no. 3, p. 1-28. E-ISSN 1932-6203. DOI https://doi.org/10.1371/journal.pone.0264892http://repositorio.unne.edu.ar/handle/123456789/60141Plos One, 2022, vol. 17, no. 3, p. 1-28.reponame:Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)instname:Universidad Nacional del Nordesteenghttps://doi.org/10.1371/journal.pone.0264892info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/Atribución-NoComercial-SinDerivadas 2.5 Argentina2026-09-24T12:44:53Zoai:repositorio.unne.edu.ar:123456789/60141instacron:UNNEInstitucionalhttp://repositorio.unne.edu.ar/Universidad públicaNo correspondehttp://repositorio.unne.edu.ar/oaiososa@bib.unne.edu.ar;sergio.alegria@unne.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:48712026-09-24 12:44:54.178Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) - Universidad Nacional del Nordestefalse
dc.title.none.fl_str_mv Inference in epidemiological agent-based models using ensemble-based data assimilation
title Inference in epidemiological agent-based models using ensemble-based data assimilation
spellingShingle Inference in epidemiological agent-based models using ensemble-based data assimilation
Cocucci, Tadeo Javier
Agent-based modeling
COVID 19
Infectious disease epidemiology
Epidemiological modeling
Ensemble Kalman filter
Data assimilation
title_short Inference in epidemiological agent-based models using ensemble-based data assimilation
title_full Inference in epidemiological agent-based models using ensemble-based data assimilation
title_fullStr Inference in epidemiological agent-based models using ensemble-based data assimilation
title_full_unstemmed Inference in epidemiological agent-based models using ensemble-based data assimilation
title_sort Inference in epidemiological agent-based models using ensemble-based data assimilation
dc.creator.none.fl_str_mv Cocucci, Tadeo Javier
Pulido, Manuel Arturo
Aparicio, Juan Pablo
Ruíz, Juan
Simoy, Mario Ignacio
Rosa, Santiago
author Cocucci, Tadeo Javier
author_facet Cocucci, Tadeo Javier
Pulido, Manuel Arturo
Aparicio, Juan Pablo
Ruíz, Juan
Simoy, Mario Ignacio
Rosa, Santiago
author_role author
author2 Pulido, Manuel Arturo
Aparicio, Juan Pablo
Ruíz, Juan
Simoy, Mario Ignacio
Rosa, Santiago
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Agent-based modeling
COVID 19
Infectious disease epidemiology
Epidemiological modeling
Ensemble Kalman filter
Data assimilation
topic Agent-based modeling
COVID 19
Infectious disease epidemiology
Epidemiological modeling
Ensemble Kalman filter
Data assimilation
dc.description.none.fl_txt_mv Fil: Cocucci, Tadeo Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Cocucci, Tadeo Javier. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Fil: Pulido, Manuel Arturo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco-Argentino sobre el Estudio del Clima y sus Impactos; Argentina.
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Modelado e Innovación Tecnológica; Argentina.
Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.
Fil: Aparicio, Juan Pablo. Arizona State University. Simon A. Levin Mathematical, Computational and Modeling Sciences Center; Estados Unidos de America.
Fil: Ruíz, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones del Mar y la Atmósfera; Argentina.
Fil: Ruíz, Juan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil: Simoy, Mario Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional; Argentina.
Fil: Simoy, Mario Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
Fil: Rosa, Santiago. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Rosa, Santiago. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Torepresent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agentbased models candescribe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 andassess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Auto´noma de Buenos Aires, Argentina.
description Fil: Cocucci, Tadeo Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
publishDate 2022
dc.date.none.fl_str_mv 2022
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 Cocucci, Tadeo Javier, et al., 2022. Inference in epidemiological agent-based models using ensemble-based data assimilation. Plos One. San Francisco: Public Library of Science, vol. 17, no. 3, p. 1-28. E-ISSN 1932-6203. DOI https://doi.org/10.1371/journal.pone.0264892
http://repositorio.unne.edu.ar/handle/123456789/60141
identifier_str_mv Cocucci, Tadeo Javier, et al., 2022. Inference in epidemiological agent-based models using ensemble-based data assimilation. Plos One. San Francisco: Public Library of Science, vol. 17, no. 3, p. 1-28. E-ISSN 1932-6203. DOI https://doi.org/10.1371/journal.pone.0264892
url http://repositorio.unne.edu.ar/handle/123456789/60141
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://doi.org/10.1371/journal.pone.0264892
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Atribución-NoComercial-SinDerivadas 2.5 Argentina
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Atribución-NoComercial-SinDerivadas 2.5 Argentina
dc.format.none.fl_str_mv application/pdf
p. 1-28
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv Plos One, 2022, vol. 17, no. 3, p. 1-28.
reponame:Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
instname:Universidad Nacional del Nordeste
reponame_str Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
collection Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
instname_str Universidad Nacional del Nordeste
repository.name.fl_str_mv Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) - Universidad Nacional del Nordeste
repository.mail.fl_str_mv ososa@bib.unne.edu.ar;sergio.alegria@unne.edu.ar
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score 13.24418

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