MOBILITY RESTRICTIONS FOR THE CONTROL OF COVID-19 EPIDEMIC

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  Starling

doi:10.1590/SciELOPreprints.717

##article.authors##

Bráulio Roberto Gonçalves Marinho Couto Universitário de Belo Horizonte https://orcid.org/0000-0002-5314-5161
Joaquim José da Cunha Junior Grupo Afya Educacional
Cristóvão de Deus Martins Oliveira Universitário de Belo Horizonte
Gregory Lauar e Souza Universitário de Belo Horizonte
Handerson Dias Duarte de Carvalho Universitário de Belo Horizonte
Rhayssa Fernanda Andrade Rocha Universitário de Belo Horizonte
André Luiz Alvim Universidade Federal de Minas Gerais
Carlos Ernesto Ferreira Starling Lifecenter Hospital

DOI:

https://doi.org/10.1590/SciELOPreprints.717

Keywords:

coronavirus infections, agent based modeling, 2019-nCoV pandemic, prevention and control, social distance

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 11^th. The Community Mobility Reports from Google Maps (<https://www.google.com/covid19/mobility>) provided mobility changes on April 5^th compared to the baseline (Jan 3^thto Feb 6^th). 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 11^th. 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.