MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC

Detalhes bibliográficos
Autor(a) principal: Couto, Bráulio Roberto Gonçalves Marinho
Data de Publicação: 2020
Outros Autores: Cunha Junior, Joaquim José da, Oliveira, Cristóvão de Deus Martins, Souza, Gregory Lauar e, Carvalho, Handerson Dias Duarte de, Rocha, Rhayssa Fernanda Andrade, Alvim, André Luiz, Starling, Carlos Ernesto Ferreira
Tipo de documento: preprint
Idioma: eng
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/717
Resumo: Objective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country. Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control. Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.
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spelling MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMICcoronavirus infectionsagent based modeling2019-nCoV pandemicprevention and controlsocial distanceObjective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country. Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control. Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-06-09info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/71710.1590/SciELOPreprints.717enghttps://preprints.scielo.org/index.php/scielo/article/view/717/996Copyright (c) 2020 Bráulio Roberto Gonçalves Marinho Couto, Joaquim José da Cunha Junior, Cristóvão de Deus Martins Oliveira, Gregory Lauar e Souza, Handerson Dias Duarte de Carvalho, Rhayssa Fernanda Andrade Rocha, André Luiz Alvim, Carlos Ernesto Ferreira Starlinghttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCouto, Bráulio Roberto Gonçalves MarinhoCunha Junior, Joaquim José da Oliveira, Cristóvão de Deus Martins Souza, Gregory Lauar eCarvalho, Handerson Dias Duarte deRocha, Rhayssa Fernanda Andrade Alvim, André Luiz Starling, Carlos Ernesto Ferreira reponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-06-08T20:33:56Zoai:ops.preprints.scielo.org:preprint/717Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-06-08T20:33:56SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
title MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
spellingShingle MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
Couto, Bráulio Roberto Gonçalves Marinho
coronavirus infections
agent based modeling
2019-nCoV pandemic
prevention and control
social distance
title_short MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
title_full MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
title_fullStr MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
title_full_unstemmed MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
title_sort MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
author Couto, Bráulio Roberto Gonçalves Marinho
author_facet Couto, Bráulio Roberto Gonçalves Marinho
Cunha Junior, Joaquim José da
Oliveira, Cristóvão de Deus Martins
Souza, Gregory Lauar e
Carvalho, Handerson Dias Duarte de
Rocha, Rhayssa Fernanda Andrade
Alvim, André Luiz
Starling, Carlos Ernesto Ferreira
author_role author
author2 Cunha Junior, Joaquim José da
Oliveira, Cristóvão de Deus Martins
Souza, Gregory Lauar e
Carvalho, Handerson Dias Duarte de
Rocha, Rhayssa Fernanda Andrade
Alvim, André Luiz
Starling, Carlos Ernesto Ferreira
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Couto, Bráulio Roberto Gonçalves Marinho
Cunha Junior, Joaquim José da
Oliveira, Cristóvão de Deus Martins
Souza, Gregory Lauar e
Carvalho, Handerson Dias Duarte de
Rocha, Rhayssa Fernanda Andrade
Alvim, André Luiz
Starling, Carlos Ernesto Ferreira
dc.subject.por.fl_str_mv coronavirus infections
agent based modeling
2019-nCoV pandemic
prevention and control
social distance
topic coronavirus infections
agent based modeling
2019-nCoV pandemic
prevention and control
social distance
description Objective: To determine whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Study design and setting: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until April 11th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was calculated by logistic regression models. COVID-19 control was defined by SEIR model R0<1.0 in a country. Results: The ECDC has registered 1,653,204 COVID-19 worldwide on April 11th. Sixteen countries presented 78% of all cases. Of the six Google Maps mobility parameters, the “Stay at home” parameter was the strongest one to control COVID-19 in a country: an increase of 50% in mobility trends for places of residence has a 99% chance of outbreak control. Conclusions: Residential mobility restriction presented itself as the most effective measure. The SEIR model associated with mobility parameters proved to be a useful tool in determining the chance of COVID-19 outbreak control.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-09
dc.type.driver.fl_str_mv info:eu-repo/semantics/preprint
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dc.identifier.uri.fl_str_mv https://preprints.scielo.org/index.php/scielo/preprint/view/717
10.1590/SciELOPreprints.717
url https://preprints.scielo.org/index.php/scielo/preprint/view/717
identifier_str_mv 10.1590/SciELOPreprints.717
dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/717/996
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
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rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
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SciELO Preprints
SciELO Preprints
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