Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cadernos de Saúde Pública |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2021000905008 |
Resumo: | Abstract: Using data collected by the Brazilian National Household Sample Survey - COVID-19 (PNAD-COVID19) and semi-Bayesian modelling developed by Wu et al., we have estimated the effect of underreporting of COVID-19 cases in Brazil as of December 2020. The total number of infected individuals is about 3 to 8 times the number of cases reported, depending on the state. Confirmed cases are at 3.1% of the total population and our estimate of total cases is at almost 15% of the approximately 212 million Brazilians as of 2020. The method we adopted from Wu et al., with slight modifications in prior specifications, applies bias corrections to account for incomplete testing and imperfect test accuracy. Our estimates, which are comparable to results obtained by Wu et al. for the United States, indicate that projections from compartmental models (such as SEIR models) tend to overestimate the number of infections and that there is considerable regional heterogeneity (results are presented by state). |
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Estimated prevalence of COVID-19 in Brazil with probabilistic bias correctionHerd ImmunitySelection BiasQuantitative AnalysisAbstract: Using data collected by the Brazilian National Household Sample Survey - COVID-19 (PNAD-COVID19) and semi-Bayesian modelling developed by Wu et al., we have estimated the effect of underreporting of COVID-19 cases in Brazil as of December 2020. The total number of infected individuals is about 3 to 8 times the number of cases reported, depending on the state. Confirmed cases are at 3.1% of the total population and our estimate of total cases is at almost 15% of the approximately 212 million Brazilians as of 2020. The method we adopted from Wu et al., with slight modifications in prior specifications, applies bias corrections to account for incomplete testing and imperfect test accuracy. Our estimates, which are comparable to results obtained by Wu et al. for the United States, indicate that projections from compartmental models (such as SEIR models) tend to overestimate the number of infections and that there is considerable regional heterogeneity (results are presented by state).Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2021000905008Cadernos de Saúde Pública v.37 n.9 2021reponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZ10.1590/0102-311x00290120info:eu-repo/semantics/openAccessFigueiredo,Erik Alencar dePolli,Démerson AndréAndrade,Bernardo Borba deeng2021-10-14T00:00:00Zoai:scielo:S0102-311X2021000905008Revistahttp://cadernos.ensp.fiocruz.br/csp/https://old.scielo.br/oai/scielo-oai.phpcadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2021-10-14T00:00Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)false |
dc.title.none.fl_str_mv |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
title |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
spellingShingle |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction Figueiredo,Erik Alencar de Herd Immunity Selection Bias Quantitative Analysis |
title_short |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
title_full |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
title_fullStr |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
title_full_unstemmed |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
title_sort |
Estimated prevalence of COVID-19 in Brazil with probabilistic bias correction |
author |
Figueiredo,Erik Alencar de |
author_facet |
Figueiredo,Erik Alencar de Polli,Démerson André Andrade,Bernardo Borba de |
author_role |
author |
author2 |
Polli,Démerson André Andrade,Bernardo Borba de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Figueiredo,Erik Alencar de Polli,Démerson André Andrade,Bernardo Borba de |
dc.subject.por.fl_str_mv |
Herd Immunity Selection Bias Quantitative Analysis |
topic |
Herd Immunity Selection Bias Quantitative Analysis |
description |
Abstract: Using data collected by the Brazilian National Household Sample Survey - COVID-19 (PNAD-COVID19) and semi-Bayesian modelling developed by Wu et al., we have estimated the effect of underreporting of COVID-19 cases in Brazil as of December 2020. The total number of infected individuals is about 3 to 8 times the number of cases reported, depending on the state. Confirmed cases are at 3.1% of the total population and our estimate of total cases is at almost 15% of the approximately 212 million Brazilians as of 2020. The method we adopted from Wu et al., with slight modifications in prior specifications, applies bias corrections to account for incomplete testing and imperfect test accuracy. Our estimates, which are comparable to results obtained by Wu et al. for the United States, indicate that projections from compartmental models (such as SEIR models) tend to overestimate the number of infections and that there is considerable regional heterogeneity (results are presented by state). |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2021000905008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2021000905008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0102-311x00290120 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz |
publisher.none.fl_str_mv |
Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz |
dc.source.none.fl_str_mv |
Cadernos de Saúde Pública v.37 n.9 2021 reponame:Cadernos de Saúde Pública instname:Fundação Oswaldo Cruz (FIOCRUZ) instacron:FIOCRUZ |
instname_str |
Fundação Oswaldo Cruz (FIOCRUZ) |
instacron_str |
FIOCRUZ |
institution |
FIOCRUZ |
reponame_str |
Cadernos de Saúde Pública |
collection |
Cadernos de Saúde Pública |
repository.name.fl_str_mv |
Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ) |
repository.mail.fl_str_mv |
cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br |
_version_ |
1754115742533419008 |