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: | https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558 |
Resumo: | 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 AnalysisUsing 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).Usando los datos recogidos por la Encuesta Nacional por Muestra de Domicilios - COVID-19 (PNAD-COVID19) y un modelado semibayesiano desarrollado por Wu et al., hemos estimado el efecto del subregistro de casos de COVID-19 en Brasil en diciembre de 2020. El número total de individuos infectados es de entre 3 a 8 veces más el número de casos informados, dependiendo del estado. Los casos confirmados son un 3,1% del total de población y nuestra estimación del total de casos es al menos un 15% de aproximadamente 212 millones de brasileños en 2020. El método que se tomó fue el de Wu et al., con leves modificaciones en las especificaciones previas, es aplicable a las correcciones de sesgo para tener en cuenta los test incompletos y la imprecisión de los tests. Nuestras estimaciones, que son comparables a los resultados obtenidos por Wu et al. para los Estados Unidos, indican las proyecciones de los modelos compartimentales (tales como los modelos SEIR), que tienden a sobreestimar el número de infecciones, así como la considerable heterogeneidad regional (los resultados se presentan por estado).Estimamos o efeito da subnotificação de casos de COVID-19 no Brasil até dezembro de 2020, com base nos dados coletados pela Pesquisa Nacional de Amostra de Domicílios sobre COVID-19 (PNAD-COVID19) e a modelagem semi-bayesiana desenvolvida por Wu et al. O número total de indivíduos infectados é cerca de 3 a 8 vezes o número de casos notificados, a depender do estado do país. No final de 2020, os casos confirmados representavam 3,1% da população total, enquanto nossa estimativa aponta para quase 15% dos cerca de 212 milhões de brasileiros no mesmo período. O método de Wu et al., que adotamos com pequenas modificações nas especificações, aplica correções de vieses para compensar pela testagem incompleta e pela acurácia imperfeita dos testes. Nossas estimativas, que são comparáveis aos resultados obtidos por Wu et al. para os Estados Unidos, indicam que projeções a partir de modelos compartimentais (tais como modelos SEIR) tendem a superestimar o número de infecções, e que há uma heterogeneidade regional considerável (resultados apresentados por estado).Reports in Public HealthCadernos de Saúde Pública2021-10-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558Reports in Public Health; Vol. 37 No. 9 (2021): SeptemberCadernos de Saúde Pública; v. 37 n. 9 (2021): Setembro1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558/16808https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558/16809Erik Alencar de FigueiredoDémerson André PolliBernardo Borba de Andradeinfo:eu-repo/semantics/openAccess2024-03-06T15:30:01Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/7558Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:08:42.049392Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true |
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 Erik Alencar de Figueiredo 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 |
Erik Alencar de Figueiredo |
author_facet |
Erik Alencar de Figueiredo Démerson André Polli Bernardo Borba de Andrade |
author_role |
author |
author2 |
Démerson André Polli Bernardo Borba de Andrade |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Erik Alencar de Figueiredo Démerson André Polli Bernardo Borba de Andrade |
dc.subject.por.fl_str_mv |
Herd Immunity Selection Bias Quantitative Analysis |
topic |
Herd Immunity Selection Bias Quantitative Analysis |
description |
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 |
2021-10-18 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558 |
url |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558/16808 https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7558/16809 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html application/pdf |
dc.publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
dc.source.none.fl_str_mv |
Reports in Public Health; Vol. 37 No. 9 (2021): September Cadernos de Saúde Pública; v. 37 n. 9 (2021): Setembro 1678-4464 0102-311X 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_ |
1798943393170063360 |