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The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil
Public 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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790416/ https://www.ncbi.nlm.nih.gov/pubmed/33411768 http://dx.doi.org/10.1371/journal.pone.0245051 |
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author | Fortaleza, Carlos Magno Castelo Branco Guimarães, Raul Borges Catão, Rafael de Castro Ferreira, Cláudia Pio Berg de Almeida, Gabriel Nogueira Vilches, Thomas Pugliesi, Edmur |
author_facet | Fortaleza, Carlos Magno Castelo Branco Guimarães, Raul Borges Catão, Rafael de Castro Ferreira, Cláudia Pio Berg de Almeida, Gabriel Nogueira Vilches, Thomas Pugliesi, Edmur |
author_sort | Fortaleza, Carlos Magno Castelo Branco |
collection | PubMed |
description | Public 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. |
format | Online Article Text |
id | pubmed-7790416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77904162021-01-27 The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil Fortaleza, Carlos Magno Castelo Branco Guimarães, Raul Borges Catão, Rafael de Castro Ferreira, Cláudia Pio Berg de Almeida, Gabriel Nogueira Vilches, Thomas Pugliesi, Edmur PLoS One Research Article Public 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. Public Library of Science 2021-01-07 /pmc/articles/PMC7790416/ /pubmed/33411768 http://dx.doi.org/10.1371/journal.pone.0245051 Text en © 2021 Fortaleza et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fortaleza, Carlos Magno Castelo Branco Guimarães, Raul Borges Catão, Rafael de Castro Ferreira, Cláudia Pio Berg de Almeida, Gabriel Nogueira Vilches, Thomas Pugliesi, Edmur The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title | The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title_full | The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title_fullStr | The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title_full_unstemmed | The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title_short | The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil |
title_sort | use of health geography modeling to understand early dispersion of covid-19 in são paulo, brazil |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790416/ https://www.ncbi.nlm.nih.gov/pubmed/33411768 http://dx.doi.org/10.1371/journal.pone.0245051 |
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