Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

Detalhes bibliográficos
Autor(a) principal: Borges, Marcelo Eduardo
Data de Publicação: 2022
Outros Autores: Ferreira, Leonardo Souto [UNESP], Poloni, Silas [UNESP], Bagattini, Angela Maria, Franco, Caroline [UNESP], da Rosa, Michelle Quarti Machado, Simon, Lorena Mendes, Camey, Suzi Alves, Kuchenbecker, Ricardo de Souza, Prado, Paulo Inácio, Diniz-Filho, José Alexandre Felizola, Kraenkel, Roberto André [UNESP], Coutinho, Renato Mendes, Toscano, Cristiana Maria
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.gloepi.2022.100094
http://hdl.handle.net/11449/247937
Resumo: We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.
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spelling Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in BrazilBrazilCOVID-19Decision support techniquesDynamic transmission modelsNon-pharmaceutical interventionsSchoolsWe simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de GoiásCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Ministério da Ciência, Tecnologia, Inovações e ComunicaçõesUniversidade Federal de Goiás Instituto de Patologia Tropical e Saúde Pública, Rua 235, s/n.°, Setor Leste Universitário, GoiâniaInstituto de Física Teórica - Universidade Estadual Paulista, Rua Dr. Bento Teobaldo Ferraz, 271, Várzea da Barra Funda, SPBig Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford, Old Road CampusDepartamento de Ecologia Instituto de Ciências Biológicas Universidade Federal de Goiás, CP 131, GoiâniaUniversidade Federal do Rio Grande do Sul Instituto de Matemática e Estatística Departamento de Estatística, Avenida Bento Gonçalves, 9500, Agronomia, RSUniversidade Federal do Rio Grande do Sul Programa de Pós-graduação em Epidemiologia Faculdade de Medicina, Campus Saúde, Rua Ramiro Barcelos, 2400, 2° andar, Floresta, RSInstituto de Biociências - Universidade de São Paulo, A101, Tv. 14, Butantã, SPCentro de Matemática Computação e Cognição - Universidade Federal do ABC, Avenida dos Estados, 5001, Santa Terezinha, SPInstituto de Física Teórica - Universidade Estadual Paulista, Rua Dr. Bento Teobaldo Ferraz, 271, Várzea da Barra Funda, SPFAPESP: 2016/01343-7FAPESP: 2017/26770-8FAPESP: 2018/24037-4Fundação de Amparo à Pesquisa do Estado de Goiás: 201810267000023FAPESP: 2019/26310-2CAPES: 305269/2020-8CNPq: 311832/2017-2CNPq: 312378/2019-0CNPq: 313055/2020-3CNPq: 315854/2020-0CNPq: 315866/2020-9CNPq: 402834/2020-8Ministério da Ciência, Tecnologia, Inovações e Comunicações: 465610/2014-5Universidade Federal de Goiás (UFG)Universidade Estadual Paulista (UNESP)University of OxfordInstituto de Matemática e EstatísticaFaculdade de MedicinaUniversidade de São Paulo (USP)Universidade Federal do ABC (UFABC)Borges, Marcelo EduardoFerreira, Leonardo Souto [UNESP]Poloni, Silas [UNESP]Bagattini, Angela MariaFranco, Caroline [UNESP]da Rosa, Michelle Quarti MachadoSimon, Lorena MendesCamey, Suzi AlvesKuchenbecker, Ricardo de SouzaPrado, Paulo InácioDiniz-Filho, José Alexandre FelizolaKraenkel, Roberto André [UNESP]Coutinho, Renato MendesToscano, Cristiana Maria2023-07-29T13:29:58Z2023-07-29T13:29:58Z2022-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.gloepi.2022.100094Global Epidemiology, v. 4.2590-1133http://hdl.handle.net/11449/24793710.1016/j.gloepi.2022.1000942-s2.0-85142498164Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGlobal Epidemiologyinfo:eu-repo/semantics/openAccess2024-11-25T17:23:08Zoai:repositorio.unesp.br:11449/247937Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462024-11-25T17:23:08Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
title Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
spellingShingle Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
Borges, Marcelo Eduardo
Brazil
COVID-19
Decision support techniques
Dynamic transmission models
Non-pharmaceutical interventions
Schools
title_short Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
title_full Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
title_fullStr Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
title_full_unstemmed Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
title_sort Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
author Borges, Marcelo Eduardo
author_facet Borges, Marcelo Eduardo
Ferreira, Leonardo Souto [UNESP]
Poloni, Silas [UNESP]
Bagattini, Angela Maria
Franco, Caroline [UNESP]
da Rosa, Michelle Quarti Machado
Simon, Lorena Mendes
Camey, Suzi Alves
Kuchenbecker, Ricardo de Souza
Prado, Paulo Inácio
Diniz-Filho, José Alexandre Felizola
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Toscano, Cristiana Maria
author_role author
author2 Ferreira, Leonardo Souto [UNESP]
Poloni, Silas [UNESP]
Bagattini, Angela Maria
Franco, Caroline [UNESP]
da Rosa, Michelle Quarti Machado
Simon, Lorena Mendes
Camey, Suzi Alves
Kuchenbecker, Ricardo de Souza
Prado, Paulo Inácio
Diniz-Filho, José Alexandre Felizola
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Toscano, Cristiana Maria
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de Goiás (UFG)
Universidade Estadual Paulista (UNESP)
University of Oxford
Instituto de Matemática e Estatística
Faculdade de Medicina
Universidade de São Paulo (USP)
Universidade Federal do ABC (UFABC)
dc.contributor.author.fl_str_mv Borges, Marcelo Eduardo
Ferreira, Leonardo Souto [UNESP]
Poloni, Silas [UNESP]
Bagattini, Angela Maria
Franco, Caroline [UNESP]
da Rosa, Michelle Quarti Machado
Simon, Lorena Mendes
Camey, Suzi Alves
Kuchenbecker, Ricardo de Souza
Prado, Paulo Inácio
Diniz-Filho, José Alexandre Felizola
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Toscano, Cristiana Maria
dc.subject.por.fl_str_mv Brazil
COVID-19
Decision support techniques
Dynamic transmission models
Non-pharmaceutical interventions
Schools
topic Brazil
COVID-19
Decision support techniques
Dynamic transmission models
Non-pharmaceutical interventions
Schools
description We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-01
2023-07-29T13:29:58Z
2023-07-29T13:29:58Z
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/10.1016/j.gloepi.2022.100094
Global Epidemiology, v. 4.
2590-1133
http://hdl.handle.net/11449/247937
10.1016/j.gloepi.2022.100094
2-s2.0-85142498164
url http://dx.doi.org/10.1016/j.gloepi.2022.100094
http://hdl.handle.net/11449/247937
identifier_str_mv Global Epidemiology, v. 4.
2590-1133
10.1016/j.gloepi.2022.100094
2-s2.0-85142498164
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Global Epidemiology
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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 repositoriounesp@unesp.br
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