How limitations in data of health surveillance impact decision making in the COVID-19 epidemic
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/1313 |
Resumo: | The pandemic of COVID-19 signaled an alert to all countries about controlling transmission of SARS-Cov-2 to have fewer infected individuals, making less stress to all health systems and saving lives. As a result, multiple governments, including national and local levels of government, went through several degrees of social distancing measures. The decision process about when to lift social distancing measures requires evidence of incidence decrease, available capacity in the health systems to absorb eventual epidemic waves, and serological prevalence studies designed to estimate the proportion of individuals with antibody protection. The trend criterium usually given by the effective reproduction number might be misguided if there are significant delays for reporting cases. For instance, the reproduction number for Niteroi, in the state of Rio de Janeiro, went down from a value of approximately 3 to little more than 1. Even with all measures, the reproduction number did not get below R0<1, which would demonstrate a more controlled scenario. Finally, a prediction method permits adjusting the notification delay and analyzing the current status of the epidemics. |
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How limitations in data of health surveillance impact decision making in the COVID-19 epidemicAs limitações nos dados de notificação de COVID-19 e implicações para avaliações baseadas em critériosCOVID-19Epidemia de 2019 nCovModelos matamáticosVigilância epidemiológicaCOVID-192019-nCov EpidemicMathematical ModelsHealth SurveillanceThe pandemic of COVID-19 signaled an alert to all countries about controlling transmission of SARS-Cov-2 to have fewer infected individuals, making less stress to all health systems and saving lives. As a result, multiple governments, including national and local levels of government, went through several degrees of social distancing measures. The decision process about when to lift social distancing measures requires evidence of incidence decrease, available capacity in the health systems to absorb eventual epidemic waves, and serological prevalence studies designed to estimate the proportion of individuals with antibody protection. The trend criterium usually given by the effective reproduction number might be misguided if there are significant delays for reporting cases. For instance, the reproduction number for Niteroi, in the state of Rio de Janeiro, went down from a value of approximately 3 to little more than 1. Even with all measures, the reproduction number did not get below R0<1, which would demonstrate a more controlled scenario. Finally, a prediction method permits adjusting the notification delay and analyzing the current status of the epidemics.A emergência do vírus SARS-Cov-2 e a pandemia de COVID-19 geraram um alerta a várias nações para controlar a transmissão do vírus a fim de ter menor número de indivíduos infectados, com menor demanda ao sistema de saúde e salvar vidas. Como resultado, vários países e governos locais impuseram medidas de distanciamento social em diferentes graus. O processo de decisão quanto à flexibilização de medidas de distanciamento social requer evidência de diminuição de incidência, capacidade disponível no sistema de saúde para absorver novas ondas de transmissão e testagem ampla a fim de conhecer a soroprevalência. Este trabalho mostra a limitação em indicadores como o número de reprodução face limitações como os atrasos para notificação de registros de casos confirmados. No município de Niterói, o número de reprodução com valor aproximadamente 3 no início da epidemia reduziu para valores pouco superiores a 1, o que ainda não seria suficiente para controle efetivo. Mas evidencia-se que a análise de semanas mais recentes sofre com efeito de atraso das notificações. A análise com o número de óbitos também apresenta este efeito. Finalmente, se apresenta proposta para analisar o quadro de vigilância epidemiológica com uso de técnica de predição, usando curva de crescimento característicoSciELO PreprintsSciELO PreprintsSciELO Preprints2020-10-09info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/131310.1590/SciELOPreprints.1313porhttps://preprints.scielo.org/index.php/scielo/article/view/1313/2064Copyright (c) 2020 Daniel Villelahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessVillela, Daniel reponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-10-08T22:35:33Zoai:ops.preprints.scielo.org:preprint/1313Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-10-08T22:35:33SciELO Preprints - SciELOfalse |
dc.title.none.fl_str_mv |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic As limitações nos dados de notificação de COVID-19 e implicações para avaliações baseadas em critérios |
title |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
spellingShingle |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic Villela, Daniel COVID-19 Epidemia de 2019 nCov Modelos matamáticos Vigilância epidemiológica COVID-19 2019-nCov Epidemic Mathematical Models Health Surveillance |
title_short |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
title_full |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
title_fullStr |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
title_full_unstemmed |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
title_sort |
How limitations in data of health surveillance impact decision making in the COVID-19 epidemic |
author |
Villela, Daniel |
author_facet |
Villela, Daniel |
author_role |
author |
dc.contributor.author.fl_str_mv |
Villela, Daniel |
dc.subject.por.fl_str_mv |
COVID-19 Epidemia de 2019 nCov Modelos matamáticos Vigilância epidemiológica COVID-19 2019-nCov Epidemic Mathematical Models Health Surveillance |
topic |
COVID-19 Epidemia de 2019 nCov Modelos matamáticos Vigilância epidemiológica COVID-19 2019-nCov Epidemic Mathematical Models Health Surveillance |
description |
The pandemic of COVID-19 signaled an alert to all countries about controlling transmission of SARS-Cov-2 to have fewer infected individuals, making less stress to all health systems and saving lives. As a result, multiple governments, including national and local levels of government, went through several degrees of social distancing measures. The decision process about when to lift social distancing measures requires evidence of incidence decrease, available capacity in the health systems to absorb eventual epidemic waves, and serological prevalence studies designed to estimate the proportion of individuals with antibody protection. The trend criterium usually given by the effective reproduction number might be misguided if there are significant delays for reporting cases. For instance, the reproduction number for Niteroi, in the state of Rio de Janeiro, went down from a value of approximately 3 to little more than 1. Even with all measures, the reproduction number did not get below R0<1, which would demonstrate a more controlled scenario. Finally, a prediction method permits adjusting the notification delay and analyzing the current status of the epidemics. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-09 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/1313 10.1590/SciELOPreprints.1313 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/1313 |
identifier_str_mv |
10.1590/SciELOPreprints.1313 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/1313/2064 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Daniel Villela https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Daniel Villela https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
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application/pdf |
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SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO Preprints SciELO Preprints SciELO Preprints |
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reponame:SciELO Preprints instname:SciELO instacron:SCI |
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SciELO |
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SciELO Preprints |
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SciELO Preprints |
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SciELO Preprints - SciELO |
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