Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations

Detalhes bibliográficos
Autor(a) principal: Zimmermann, Ivan Ricardo
Data de Publicação: 2020
Outros Autores: Sanchez, Mauro, Brant, Jonas, Alves, Domingos
Tipo de documento: preprint
Idioma: por
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/574
Resumo: Objectives: to analyze the impact of social distance policies on the spread of COVID-19 and the need for beds in intensive care units. Methods: based on a dynamic transition compartmental model and Monte Carlo simulations, propagation scenarios were built according to the level of adherence of the social distance measures in the context of the Federal District, Brazil. The parameter values ​​were based on official sources, indexed databases and public data repositories. Results: maintaining adherence to the 58% isolation level was the only favorable scenario, with a peak of up to 792 (IQR: 447 to 1,262) ICU admissions between 11/05/2020 and 1/15/2021. The absence of social distance would imply a peak of up to 7,331 (IQR: 5,427 to 9,696) ICU admissions. Conclusion: the projections corroborate the positive effect of social distance measures and the applicability of indicators in their monitoring.
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spelling Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulationsDemanda de camas de UCI por COVID-19 en el Distrito Federal, Brasil: un análisis del impacto de las medidas de distancia social con simulaciones de Monte CarloDemanda por leitos de UTI pela COVID-19 no Distrito Federal, Brasil: uma análise do impacto das medidas de distanciamento social com simulações de Monte CarloCOVID-19Infecções por CoronavirusPrevisõesCOVID-19Coronavirus InfectionsForecastingCOVID-19Infecciones por CoronavirusPredicciónObjectives: to analyze the impact of social distance policies on the spread of COVID-19 and the need for beds in intensive care units. Methods: based on a dynamic transition compartmental model and Monte Carlo simulations, propagation scenarios were built according to the level of adherence of the social distance measures in the context of the Federal District, Brazil. The parameter values ​​were based on official sources, indexed databases and public data repositories. Results: maintaining adherence to the 58% isolation level was the only favorable scenario, with a peak of up to 792 (IQR: 447 to 1,262) ICU admissions between 11/05/2020 and 1/15/2021. The absence of social distance would imply a peak of up to 7,331 (IQR: 5,427 to 9,696) ICU admissions. Conclusion: the projections corroborate the positive effect of social distance measures and the applicability of indicators in their monitoring.Objetivos: analizar el impacto de las políticas de distancia social en la propagación de COVID-19 y la necesidad de camas en unidades de cuidados intensivos. Métodos: con un modelo de transición dinámica y simulaciones de Monte Carlo, los escenarios de propagación se construyeron de acuerdo con el nivel de adherencia de las medidas de distancia social en el Distrito Federal, Brasil. Los parámetros se basaron en fuentes oficiales, bases de datos indexadas y repositorios de datos. Resultados: mantener la adherencia al nivel de aislamiento del 58% fue el único escenario favorable, con un pico de hasta 792 (IQR: 447 a 1,262) admisiones en la UCI entre el 11/05/2020 y el 15/1/2021. La ausencia de distancia implicaría un pico de 7,331 (IQR: 5,427 a 9,696) admisiones en la UCI. Conclusión: las proyecciones corroboran el efecto positivo de las medidas de distancia social y la aplicabilidad de los indicadores en su seguimiento.Objetivos: analisar o impacto das políticas de distanciamento social sobre a propagação da COVID-19 e a necessidade de leitos de unidades de terapia intensiva. Métodos: com um modelo compartimental de transição dinâmica e simulações de Monte Carlo foram construídos cenários de propagação de acordo com o nível de adesão das medidas de distanciamento social no contexto do Distrito Federal, Brasil. Os valores dos parâmetros foram baseados em fontes oficiais, bases indexadas e repositórios públicos de dados. Resultados: a manutenção da adesão ao nível de 58% de isolamento foi o único cenário favorável, com um pico de até 792 (IQR: 447 a 1.262) internações em UTI entre 05/11/2020 e 15/01/2021. A ausência do distanciamento implicaria um pico de até 7.331 (IQR: 5.427 a 9.696) internações em UTI. Conclusão: as projeções corroboram o efeito positivo das medidas de distanciamento social e a aplicabilidade de indicadores no seu monitoramento.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-05-28info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/57410.1590/SciELOPreprints.574porhttps://preprints.scielo.org/index.php/scielo/article/view/574/810Copyright (c) 2020 Ivan Zimmermann, Mauro Sanchez, Jonas Brant, Domingos Alveshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessZimmermann, Ivan RicardoSanchez, MauroBrant, JonasAlves, Domingosreponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-05-26T18:02:08Zoai:ops.preprints.scielo.org:preprint/574Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-05-26T18:02:08SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
Demanda de camas de UCI por COVID-19 en el Distrito Federal, Brasil: un análisis del impacto de las medidas de distancia social con simulaciones de Monte Carlo
Demanda por leitos de UTI pela COVID-19 no Distrito Federal, Brasil: uma análise do impacto das medidas de distanciamento social com simulações de Monte Carlo
title Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
spellingShingle Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
Zimmermann, Ivan Ricardo
COVID-19
Infecções por Coronavirus
Previsões
COVID-19
Coronavirus Infections
Forecasting
COVID-19
Infecciones por Coronavirus
Predicción
title_short Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
title_full Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
title_fullStr Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
title_full_unstemmed Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
title_sort Demand for ICU beds by COVID-19 in the Federal District, Brazil: an analysis of the impact of social distance measures with Monte Carlo simulations
author Zimmermann, Ivan Ricardo
author_facet Zimmermann, Ivan Ricardo
Sanchez, Mauro
Brant, Jonas
Alves, Domingos
author_role author
author2 Sanchez, Mauro
Brant, Jonas
Alves, Domingos
author2_role author
author
author
dc.contributor.author.fl_str_mv Zimmermann, Ivan Ricardo
Sanchez, Mauro
Brant, Jonas
Alves, Domingos
dc.subject.por.fl_str_mv COVID-19
Infecções por Coronavirus
Previsões
COVID-19
Coronavirus Infections
Forecasting
COVID-19
Infecciones por Coronavirus
Predicción
topic COVID-19
Infecções por Coronavirus
Previsões
COVID-19
Coronavirus Infections
Forecasting
COVID-19
Infecciones por Coronavirus
Predicción
description Objectives: to analyze the impact of social distance policies on the spread of COVID-19 and the need for beds in intensive care units. Methods: based on a dynamic transition compartmental model and Monte Carlo simulations, propagation scenarios were built according to the level of adherence of the social distance measures in the context of the Federal District, Brazil. The parameter values ​​were based on official sources, indexed databases and public data repositories. Results: maintaining adherence to the 58% isolation level was the only favorable scenario, with a peak of up to 792 (IQR: 447 to 1,262) ICU admissions between 11/05/2020 and 1/15/2021. The absence of social distance would imply a peak of up to 7,331 (IQR: 5,427 to 9,696) ICU admissions. Conclusion: the projections corroborate the positive effect of social distance measures and the applicability of indicators in their monitoring.
publishDate 2020
dc.date.none.fl_str_mv 2020-05-28
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/574
10.1590/SciELOPreprints.574
url https://preprints.scielo.org/index.php/scielo/preprint/view/574
identifier_str_mv 10.1590/SciELOPreprints.574
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/574/810
dc.rights.driver.fl_str_mv Copyright (c) 2020 Ivan Zimmermann, Mauro Sanchez, Jonas Brant, Domingos Alves
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 Ivan Zimmermann, Mauro Sanchez, Jonas Brant, Domingos Alves
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
dc.source.none.fl_str_mv reponame:SciELO Preprints
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instacron:SCI
instname_str SciELO
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institution SCI
reponame_str SciELO Preprints
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repository.name.fl_str_mv SciELO Preprints - SciELO
repository.mail.fl_str_mv scielo.submission@scielo.org
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