MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
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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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 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/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 |
language |
eng |
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 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
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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SCI |
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SciELO Preprints |
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SciELO Preprints |
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SciELO Preprints - SciELO |
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scielo.submission@scielo.org |
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