Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UERJ |
Texto Completo: | http://www.bdtd.uerj.br/handle/1/20326 |
Resumo: | Outbreaks due infectious diseases has been drawing attention of the scientific community in the last few years. The recognition of the aggressive effects created for the health and economy of the population worldwide made researchers from the most diverse areas of knowledge turn their resources into projects inside the theme. The present work presents and apply a framework for uncertainty quantification in epidemiological models. This is based on use global sensitivity analysis by Polynomial Chaos Expansion-based Sobol indices, combined with the Maximum Entropy Principle. The first allows to identify the most relevant input parameters, while the second one orients the construction of least biased distributions for those inputs. Then, a Monte Carlo simulation is executed to analyze the outcome stochastic process obtained through the model. The framework was applied in the epidemiological scenarios of Zika virus in Brazil and COVID-19 in Rio de Janeiro city, allowing to extract some important statistics about each outbreak. A compartmental model is employed in the first scenario, while the multi-waves dynamics of the second scenario is described by a Beta logistic growth model. Before riding the robustness study, calibration results are performed to put the quantities of interest obtained from theses models in a shape closer to the real data. Additional discussions are made about how to use sensitivity analysis results to update the knowledge about the parameters, and guide model selection |
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Cunha Junior, Americo Barbosa daCoelho, Flávio CodeçoArenas, Zochil GonzálezRochinha, Fernando AlvesRizzi, Rogério Luishttp://lattes.cnpq.br/1995185025141214Caldas, Michel Antonio Tosinmichel.tosin@uerj.br2023-09-21T13:52:43Z2021-08-06CALDAS, Michel Antonio Tosin. Modeling and uncertainty quantification in the nonlinear dynamics of epidemiological phenomena: application to Zika virus and COVID-19 outbreaks. 2021. 118 f. Dissertação (Mestrado em Ciências Computacionais) - Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 2021.http://www.bdtd.uerj.br/handle/1/20326Outbreaks due infectious diseases has been drawing attention of the scientific community in the last few years. The recognition of the aggressive effects created for the health and economy of the population worldwide made researchers from the most diverse areas of knowledge turn their resources into projects inside the theme. The present work presents and apply a framework for uncertainty quantification in epidemiological models. This is based on use global sensitivity analysis by Polynomial Chaos Expansion-based Sobol indices, combined with the Maximum Entropy Principle. The first allows to identify the most relevant input parameters, while the second one orients the construction of least biased distributions for those inputs. Then, a Monte Carlo simulation is executed to analyze the outcome stochastic process obtained through the model. The framework was applied in the epidemiological scenarios of Zika virus in Brazil and COVID-19 in Rio de Janeiro city, allowing to extract some important statistics about each outbreak. A compartmental model is employed in the first scenario, while the multi-waves dynamics of the second scenario is described by a Beta logistic growth model. Before riding the robustness study, calibration results are performed to put the quantities of interest obtained from theses models in a shape closer to the real data. Additional discussions are made about how to use sensitivity analysis results to update the knowledge about the parameters, and guide model selectionSurtos por doenças infecciosas têm tomado atenção da comunidade científica geral nos últimos anos. O reconhecimento dos agressivos efeitos gerados para a saúde e economia das populações ao redor do mundo, fez com que pesquisadores das mais diversas áreas do conhecimento voltassem seus recursos para projetos nesse tema. O presente trabalho apresenta e aplica um framework para quantificação de incertezas em modelos epidemiológicos. Este é baseado em usar análise de sensibilidade global por índicas de Sobol baseados em Expansão em Polinômios Caos, combinado com o Princípio do Máximo de Entropia. O primeiro permite identificar os parâmetros de entrada mais relevantes, enquanto que o segundo orienta a construção de distribuições menos enviesadas para essas entradas. Assim, uma simulação de Monte Carlo é executada para analisar o processo estocástico de saída obtido através do modelo. O framework foi aplicado nos cenários epidemiológicos de Zika vírus no Brasil e de COVID-19 no município do Rio de Janeiro, permitindo extrair algumas estatísticas importantes sobre cada surto. Um modelo comportamental é empregado no primeiro cenário, enquanto a dinâmica multi ondas do segundo cenário é descrita por um modelo de crescimento Beta logístico. Antes de conduzir os estudos de robustez, resultados de calibração são incluídos para por as quantidades de interesse obtidas por esses modelos numa forma mais próxima dos dados reais. Discussões adicionais são feitas sobre como utilizar resultados de análise de sensibilidade para atualizar o conhecimento sobre os parâmetros, e guiar seleção de modelosSubmitted by Bárbara CTC/A (babalusotnas@gmail.com) on 2023-09-21T13:52:43Z No. of bitstreams: 1 Dissertação - Michel Antonio Tosin Caldas - 2021 -Completa.pdf.pdf: 4237109 bytes, checksum: 528e4586159bb27d0de9c93eeda06d5b (MD5)Made available in DSpace on 2023-09-21T13:52:43Z (GMT). No. of bitstreams: 1 Dissertação - Michel Antonio Tosin Caldas - 2021 -Completa.pdf.pdf: 4237109 bytes, checksum: 528e4586159bb27d0de9c93eeda06d5b (MD5) Previous issue date: 2021-08-06Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro - FAPERJapplication/pdfporUniversidade do Estado do Rio de JaneiroPrograma de Pós-Graduação em Ciências ComputacionaisUERJBrasilCentro de Tecnologia e Ciências::Instituto de Matemática e EstatísticaNonlinear dynamicsModel calibrationUncertainty quantificationEpidemiologia - Modelos matemáticosEpidemiologia - Modelos estatísticosZika VirusCOVID-19 (Doença)Epidemiological modelingGlobal sensitivity analysisModelagem epidemiológicaDinâmica não o linearCalibração de modelosAnálise de sensibilidade globalQuantificação de incertezasCIENCIAS EXATAS E DA TERRA::MATEMATICAModeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaksModelagem e quantificação das incertezas na dinâmica não-linear de fenômenos epidemiológicos: aplicação em surtos de Zika vírus e COVID-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UERJinstname:Universidade do Estado do Rio de Janeiro (UERJ)instacron:UERJORIGINALDissertação - Michel Antonio Tosin Caldas - 2021 -Completa.pdfDissertação - Michel Antonio Tosin Caldas - 2021 -Completa.pdfapplication/pdf4237109http://www.bdtd.uerj.br/bitstream/1/20326/2/Disserta%C3%A7%C3%A3o+-+Michel+Antonio+Tosin+Caldas+-+2021+-Completa.pdf528e4586159bb27d0de9c93eeda06d5bMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82123http://www.bdtd.uerj.br/bitstream/1/20326/1/license.txte5502652da718045d7fcd832b79fca29MD511/203262024-02-27 14:34:50.173oai:www.bdtd.uerj.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.bdtd.uerj.br/PUBhttps://www.bdtd.uerj.br:8443/oai/requestbdtd.suporte@uerj.bropendoar:29032024-02-27T17:34:50Biblioteca Digital de Teses e Dissertações da UERJ - Universidade do Estado do Rio de Janeiro (UERJ)false |
dc.title.por.fl_str_mv |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
dc.title.alternative.por.fl_str_mv |
Modelagem e quantificação das incertezas na dinâmica não-linear de fenômenos epidemiológicos: aplicação em surtos de Zika vírus e COVID-19 |
title |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
spellingShingle |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks Caldas, Michel Antonio Tosin Nonlinear dynamics Model calibration Uncertainty quantification Epidemiologia - Modelos matemáticos Epidemiologia - Modelos estatísticos Zika Virus COVID-19 (Doença) Epidemiological modeling Global sensitivity analysis Modelagem epidemiológica Dinâmica não o linear Calibração de modelos Análise de sensibilidade global Quantificação de incertezas CIENCIAS EXATAS E DA TERRA::MATEMATICA |
title_short |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
title_full |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
title_fullStr |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
title_full_unstemmed |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
title_sort |
Modeling and uncertainty quanti cation in the nonlinear dynamics of epidemiological phenomena: Application to Zika virus and COVID-19 outbreaks |
author |
Caldas, Michel Antonio Tosin |
author_facet |
Caldas, Michel Antonio Tosin michel.tosin@uerj.br |
author_role |
author |
author2 |
michel.tosin@uerj.br |
author2_role |
author |
dc.contributor.advisor1.fl_str_mv |
Cunha Junior, Americo Barbosa da |
dc.contributor.advisor-co1.fl_str_mv |
Coelho, Flávio Codeço |
dc.contributor.referee1.fl_str_mv |
Arenas, Zochil González |
dc.contributor.referee2.fl_str_mv |
Rochinha, Fernando Alves |
dc.contributor.referee3.fl_str_mv |
Rizzi, Rogério Luis |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1995185025141214 |
dc.contributor.author.fl_str_mv |
Caldas, Michel Antonio Tosin michel.tosin@uerj.br |
contributor_str_mv |
Cunha Junior, Americo Barbosa da Coelho, Flávio Codeço Arenas, Zochil González Rochinha, Fernando Alves Rizzi, Rogério Luis |
dc.subject.eng.fl_str_mv |
Nonlinear dynamics Model calibration Uncertainty quantification |
topic |
Nonlinear dynamics Model calibration Uncertainty quantification Epidemiologia - Modelos matemáticos Epidemiologia - Modelos estatísticos Zika Virus COVID-19 (Doença) Epidemiological modeling Global sensitivity analysis Modelagem epidemiológica Dinâmica não o linear Calibração de modelos Análise de sensibilidade global Quantificação de incertezas CIENCIAS EXATAS E DA TERRA::MATEMATICA |
dc.subject.por.fl_str_mv |
Epidemiologia - Modelos matemáticos Epidemiologia - Modelos estatísticos Zika Virus COVID-19 (Doença) Epidemiological modeling Global sensitivity analysis Modelagem epidemiológica Dinâmica não o linear Calibração de modelos Análise de sensibilidade global Quantificação de incertezas |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::MATEMATICA |
description |
Outbreaks due infectious diseases has been drawing attention of the scientific community in the last few years. The recognition of the aggressive effects created for the health and economy of the population worldwide made researchers from the most diverse areas of knowledge turn their resources into projects inside the theme. The present work presents and apply a framework for uncertainty quantification in epidemiological models. This is based on use global sensitivity analysis by Polynomial Chaos Expansion-based Sobol indices, combined with the Maximum Entropy Principle. The first allows to identify the most relevant input parameters, while the second one orients the construction of least biased distributions for those inputs. Then, a Monte Carlo simulation is executed to analyze the outcome stochastic process obtained through the model. The framework was applied in the epidemiological scenarios of Zika virus in Brazil and COVID-19 in Rio de Janeiro city, allowing to extract some important statistics about each outbreak. A compartmental model is employed in the first scenario, while the multi-waves dynamics of the second scenario is described by a Beta logistic growth model. Before riding the robustness study, calibration results are performed to put the quantities of interest obtained from theses models in a shape closer to the real data. Additional discussions are made about how to use sensitivity analysis results to update the knowledge about the parameters, and guide model selection |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-08-06 |
dc.date.accessioned.fl_str_mv |
2023-09-21T13:52:43Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CALDAS, Michel Antonio Tosin. Modeling and uncertainty quantification in the nonlinear dynamics of epidemiological phenomena: application to Zika virus and COVID-19 outbreaks. 2021. 118 f. Dissertação (Mestrado em Ciências Computacionais) - Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 2021. |
dc.identifier.uri.fl_str_mv |
http://www.bdtd.uerj.br/handle/1/20326 |
identifier_str_mv |
CALDAS, Michel Antonio Tosin. Modeling and uncertainty quantification in the nonlinear dynamics of epidemiological phenomena: application to Zika virus and COVID-19 outbreaks. 2021. 118 f. Dissertação (Mestrado em Ciências Computacionais) - Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 2021. |
url |
http://www.bdtd.uerj.br/handle/1/20326 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Universidade do Estado do Rio de Janeiro |
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Programa de Pós-Graduação em Ciências Computacionais |
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UERJ |
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Brasil |
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Centro de Tecnologia e Ciências::Instituto de Matemática e Estatística |
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Universidade do Estado do Rio de Janeiro |
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