Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s11071-022-07865-x http://hdl.handle.net/11449/248024 |
Resumo: | The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting. |
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Repositório Institucional da UNESP |
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Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecastingApproximate Bayesian computationCovid-19Epidemic forecastingSEIRD modelThe long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)Instituto de Física Teórica Universidade Estadual Paulista, R. Dr. Bento Teobaldo Ferraz, 271, Bloco 2, Barra Funda, SPInstituto de Física Gleb Wataghin Universidade Estadual de Campinas, Rua Sérgio Buarque de Holanda, 777, SPLaboratório Nacional de Computção Científica, Av. Getulio Vargas, 333, RJDepartment of Computational Modeling Polytechnic Institute Rio de Janeiro State UniversityInstituto de Física de São Carlos Universidade de São Paulo, Av. Trab. São Carlense, 400 - Parque Arnold Schimidt, SPDepartment of Physics University of Alberta, 116 St & 85 AveInstituto de Física Teórica Universidade Estadual Paulista, R. Dr. Bento Teobaldo Ferraz, 271, Bloco 2, Barra Funda, SPFAPESP: 2020/14169-0FAPESP: 2021/02027-0CAPES: 88887.625345/2021-00FAPERJ: E-26/200.347/2021Universidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Laboratório Nacional de Computção CientíficaRio de Janeiro State UniversityUniversidade de São Paulo (USP)University of AlbertaValeriano, João Pedro [UNESP]Cintra, Pedro HenriqueLibotte, GustavoReis, IgorFontinele, FelipeSilva, RenatoMalta, Sandra2023-07-29T13:32:25Z2023-07-29T13:32:25Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article549-558http://dx.doi.org/10.1007/s11071-022-07865-xNonlinear Dynamics, v. 111, n. 1, p. 549-558, 2023.1573-269X0924-090Xhttp://hdl.handle.net/11449/24802410.1007/s11071-022-07865-x2-s2.0-85143814919Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengNonlinear Dynamicsinfo:eu-repo/semantics/openAccess2023-07-29T13:32:25Zoai:repositorio.unesp.br:11449/248024Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:32:25Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
title |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
spellingShingle |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting Valeriano, João Pedro [UNESP] Approximate Bayesian computation Covid-19 Epidemic forecasting SEIRD model |
title_short |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
title_full |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
title_fullStr |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
title_full_unstemmed |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
title_sort |
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting |
author |
Valeriano, João Pedro [UNESP] |
author_facet |
Valeriano, João Pedro [UNESP] Cintra, Pedro Henrique Libotte, Gustavo Reis, Igor Fontinele, Felipe Silva, Renato Malta, Sandra |
author_role |
author |
author2 |
Cintra, Pedro Henrique Libotte, Gustavo Reis, Igor Fontinele, Felipe Silva, Renato Malta, Sandra |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Estadual de Campinas (UNICAMP) Laboratório Nacional de Computção Científica Rio de Janeiro State University Universidade de São Paulo (USP) University of Alberta |
dc.contributor.author.fl_str_mv |
Valeriano, João Pedro [UNESP] Cintra, Pedro Henrique Libotte, Gustavo Reis, Igor Fontinele, Felipe Silva, Renato Malta, Sandra |
dc.subject.por.fl_str_mv |
Approximate Bayesian computation Covid-19 Epidemic forecasting SEIRD model |
topic |
Approximate Bayesian computation Covid-19 Epidemic forecasting SEIRD model |
description |
The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:32:25Z 2023-07-29T13:32:25Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s11071-022-07865-x Nonlinear Dynamics, v. 111, n. 1, p. 549-558, 2023. 1573-269X 0924-090X http://hdl.handle.net/11449/248024 10.1007/s11071-022-07865-x 2-s2.0-85143814919 |
url |
http://dx.doi.org/10.1007/s11071-022-07865-x http://hdl.handle.net/11449/248024 |
identifier_str_mv |
Nonlinear Dynamics, v. 111, n. 1, p. 549-558, 2023. 1573-269X 0924-090X 10.1007/s11071-022-07865-x 2-s2.0-85143814919 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Nonlinear Dynamics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
549-558 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803046261020950528 |