Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , |
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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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 |
_version_ |
1826304495403925504 |