How limitations in data of health surveillance impact decision making in the Covid-19 pandemic
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Saude em Debate |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-11042020000800206 |
Resumo: | ABSTRACT The Covid-19 pandemic signaled an alert to all countries about controlling transmission of SARS-CoV-2 to have fewer infected individuals, causing 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 regarding the flexibilization of 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 Niterói, 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 R<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. |
id |
CBES-1_ef4ef69871ad342c24c3b8055e01af9a |
---|---|
oai_identifier_str |
oai:scielo:S0103-11042020000800206 |
network_acronym_str |
CBES-1 |
network_name_str |
Saude em Debate |
repository_id_str |
|
spelling |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemicCoronavirus infections2019 novel coronavirus pandemicModels, theoreticalPublic health surveillanceABSTRACT The Covid-19 pandemic signaled an alert to all countries about controlling transmission of SARS-CoV-2 to have fewer infected individuals, causing 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 regarding the flexibilization of 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 Niterói, 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 R<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.Centro Brasileiro de Estudos de Saúde2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-11042020000800206Saúde em Debate v.44 n.spe4 2020reponame:Saude em Debateinstname:Centro Brasileiro de Estudos de Saudeinstacron:CBES10.1590/0103-11042020e413info:eu-repo/semantics/openAccessVillela,Daniel Antunes Macieleng2021-08-19T00:00:00Zoai:scielo:S0103-11042020000800206Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0103-1104&lng=en&nrm=isohttps://old.scielo.br/oai/scielo-oai.phprevista@saudeemdebate.org.br2358-28980103-1104opendoar:2021-08-19T00:00Saude em Debate - Centro Brasileiro de Estudos de Saudefalse |
dc.title.none.fl_str_mv |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
title |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
spellingShingle |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic Villela,Daniel Antunes Maciel Coronavirus infections 2019 novel coronavirus pandemic Models, theoretical Public health surveillance |
title_short |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
title_full |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
title_fullStr |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
title_full_unstemmed |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
title_sort |
How limitations in data of health surveillance impact decision making in the Covid-19 pandemic |
author |
Villela,Daniel Antunes Maciel |
author_facet |
Villela,Daniel Antunes Maciel |
author_role |
author |
dc.contributor.author.fl_str_mv |
Villela,Daniel Antunes Maciel |
dc.subject.por.fl_str_mv |
Coronavirus infections 2019 novel coronavirus pandemic Models, theoretical Public health surveillance |
topic |
Coronavirus infections 2019 novel coronavirus pandemic Models, theoretical Public health surveillance |
description |
ABSTRACT The Covid-19 pandemic signaled an alert to all countries about controlling transmission of SARS-CoV-2 to have fewer infected individuals, causing 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 regarding the flexibilization of 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 Niterói, 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 R<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 |
2020-01-01 |
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=S0103-11042020000800206 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-11042020000800206 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-11042020e413 |
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 |
Centro Brasileiro de Estudos de Saúde |
publisher.none.fl_str_mv |
Centro Brasileiro de Estudos de Saúde |
dc.source.none.fl_str_mv |
Saúde em Debate v.44 n.spe4 2020 reponame:Saude em Debate instname:Centro Brasileiro de Estudos de Saude instacron:CBES |
instname_str |
Centro Brasileiro de Estudos de Saude |
instacron_str |
CBES |
institution |
CBES |
reponame_str |
Saude em Debate |
collection |
Saude em Debate |
repository.name.fl_str_mv |
Saude em Debate - Centro Brasileiro de Estudos de Saude |
repository.mail.fl_str_mv |
revista@saudeemdebate.org.br |
_version_ |
1754209001343549440 |