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Publicação:
The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil

dc.contributor.authorBranco Fortaleza, Carlos Magno Castelo [UNESP]
dc.contributor.authorGuimarães, Raul Borges [UNESP]
dc.contributor.authorde Castro Catão, Rafael
dc.contributor.authorFerreira, Cláudia Pio [UNESP]
dc.contributor.authorde Almeida, Gabriel Berg [UNESP]
dc.contributor.authorVilches, Thomas Nogueira
dc.contributor.authorPugliesi, Edmur [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionFederal University of Espírito Santo
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2021-06-25T11:10:15Z
dc.date.available2021-06-25T11:10:15Z
dc.date.issued2021-01-01
dc.description.abstractPublic health policies to contain the spread of COVID-19 rely mainly on non-pharmacological measures. Those measures, especially social distancing, are a challenge for developing countries, such as Brazil. In São Paulo, the most populous state in Brazil (45 million inhabitants), most COVID-19 cases up to April 18th were reported in the Capital and metropolitan area. However, the inner municipalities, where 20 million people live, are also at risk. As governmental authorities discuss the loosening of measures for restricting population mobility, it is urgent to analyze the routes of dispersion of COVID-19 in São Paulo territory. We hypothesize that urban hierarchy is the main responsible for the disease spreading, and we identify the hotspots and the main routes of virus movement from the metropolis to the inner state. In this ecological study, we use geographic models of population mobility to check for patterns for the spread of SARS-CoV-2 infection. We identify two patterns based on surveillance data: one by contiguous diffusion from the capital metropolitan area, and the other hierarchical with long-distance spread through major highways that connects São Paulo city with cities of regional relevance. This knowledge can provide real-time responses to support public health strategies, optimizing the use of resources in order to minimize disease impact on population and economy.en
dc.description.affiliationDepartment of Infectious Diseases Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationDepartment of Geography Faculty of Science and Technology São Paulo State University (UNESP)
dc.description.affiliationDepartment of Geography Federal University of Espírito Santo
dc.description.affiliationInstitute of Biosciences São Paulo State University (UNESP)
dc.description.affiliationInstitute of Mathematics Statistics and Scientific Computation University of Campinas (UNICAMP)
dc.description.affiliationUnespDepartment of Infectious Diseases Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationUnespDepartment of Geography Faculty of Science and Technology São Paulo State University (UNESP)
dc.description.affiliationUnespInstitute of Biosciences São Paulo State University (UNESP)
dc.identifierhttp://dx.doi.org/10.1371/journal.pone.0245051
dc.identifier.citationPLoS ONE, v. 16, n. 1 January, 2021.
dc.identifier.doi10.1371/journal.pone.0245051
dc.identifier.issn1932-6203
dc.identifier.lattes8022527468369459
dc.identifier.lattes2052749698204617
dc.identifier.orcid0000-0002-9925-5374
dc.identifier.orcid0000-0002-9404-6098
dc.identifier.scopus2-s2.0-85099395838
dc.identifier.urihttp://hdl.handle.net/11449/208320
dc.language.isoeng
dc.relation.ispartofPLoS ONE
dc.sourceScopus
dc.titleThe use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazilen
dc.typeArtigo
dspace.entity.typePublication
unesp.author.lattes8022527468369459[2]
unesp.author.lattes2052749698204617[4]
unesp.author.orcid0000-0002-9925-5374[2]
unesp.author.orcid0000-0002-9404-6098[4]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências, Botucatupt
unesp.departmentGeografia - FCTpt
unesp.departmentBioestatística - IBBpt

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