The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/916 |
Resumo: | The present study seeks to identify possible correlations between the spatial distribution pattern of SARS-CoV-2 (COVID-19) in Brazil, testing a correlation between factors such as climate, different populations (Indigenous, Quilombola and Parda) and the existence of Subnormal Agglomerates, with the purpose of identifying contexts of inequality and social injustice. Based on the premise that the climate interferes with infectious diseases vehicles (as shown by the study by John Snow, in 1854), data were analyzed regarding the dissemination of COVID-19 in Brazil, from various data repositories made available by Brazilian institutions such as IBGE, the Ministry of Health of Brazil and the Oswaldo Cruz Foundation, and other international repository data made available by other sources, of which the platform developed by the synergy created by Johns Hopkins University and ESRI (the most prestigious producer of Geographic Information Systems-GIS) is a greater example. The data were geoprocessed in GIS platforms and allowed the development of thematic cartography on which new geospatial and geostatistical analyzes were carried out to understand patterns. Among the main conclusions, we highlight the need to maintain some prudence with the publication of papers and their conclusions, given the high degree of uncertainty about (almost) everything that involves the spread of the disease and, also, about “what” and “how” it was born. There are already many published studies, but depending on the perspective of approach, it is not uncommon to find studies that are based on contradictory conclusions, depending on the approaches, the scientific sensitivities, the methodologies used or, simply, the quality of the processed data. It is also noteworthy that it is impossible to monitor the movements and mobility of individuals, either part-time or full-time, the correlations made may not be compatible with adequate monitoring and control of the evolution of the pandemic crisis that is taking place worldwide, because promoting the crossing of data on the “fixed space” or objects read as such (individuals) does not necessarily allow the same results to be obtained when studying and analyzing flows; that is, from short time to long time, from short distance to long distance, there are invisible (or difficult to parameterize) variables and factors that clog or atrophy the production of absolutely incontrovertible scientific conclusions. Another topic that deserves a special mention focuses on the idea that, if the climate does not seem to be a determining and unequivocal variable, in terms of classifying the degree of vulnerability and risk associated with the populations, their behavior, age (and gender), composition and racial structure, there seems to be a consistent line of thought that points to the existence of more vulnerable and risk-sensitive territorial and social contexts. These are the cases of the “black”, “brown” and “indigenous” populations and, also, of the individuals that inhabit the so-called Subnormal Agglomerates. |
id |
SCI-1_d08cceb7db8be66c7bac8b4151340c6c |
---|---|
oai_identifier_str |
oai:ops.preprints.scielo.org:preprint/916 |
network_acronym_str |
SCI-1 |
network_name_str |
SciELO Preprints |
repository_id_str |
|
spelling |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian populationA PRIMEIRA FASE PANDÉMICA DA SARS-COV-2 NO BRASIL; APONTAMENTOS PARA UMA ANÁLISE INTEGRADA DE DESIGUALDADES TERRITORIAIS ASSOCIADAS AOS PADRÕES E RITMOS DE PROPAGAÇÃO DA DOENÇA E SEUS IMPACTES NA POPULAÇÃO BRASILEIRASARS-CoV-2 (Covid-19)Desigualdades TerritoriaisClimaPopulação BrasileiraSARS-CoV-2 (Covid-19)Territorial inequalitiesClimateBrazilian populationThe present study seeks to identify possible correlations between the spatial distribution pattern of SARS-CoV-2 (COVID-19) in Brazil, testing a correlation between factors such as climate, different populations (Indigenous, Quilombola and Parda) and the existence of Subnormal Agglomerates, with the purpose of identifying contexts of inequality and social injustice. Based on the premise that the climate interferes with infectious diseases vehicles (as shown by the study by John Snow, in 1854), data were analyzed regarding the dissemination of COVID-19 in Brazil, from various data repositories made available by Brazilian institutions such as IBGE, the Ministry of Health of Brazil and the Oswaldo Cruz Foundation, and other international repository data made available by other sources, of which the platform developed by the synergy created by Johns Hopkins University and ESRI (the most prestigious producer of Geographic Information Systems-GIS) is a greater example. The data were geoprocessed in GIS platforms and allowed the development of thematic cartography on which new geospatial and geostatistical analyzes were carried out to understand patterns. Among the main conclusions, we highlight the need to maintain some prudence with the publication of papers and their conclusions, given the high degree of uncertainty about (almost) everything that involves the spread of the disease and, also, about “what” and “how” it was born. There are already many published studies, but depending on the perspective of approach, it is not uncommon to find studies that are based on contradictory conclusions, depending on the approaches, the scientific sensitivities, the methodologies used or, simply, the quality of the processed data. It is also noteworthy that it is impossible to monitor the movements and mobility of individuals, either part-time or full-time, the correlations made may not be compatible with adequate monitoring and control of the evolution of the pandemic crisis that is taking place worldwide, because promoting the crossing of data on the “fixed space” or objects read as such (individuals) does not necessarily allow the same results to be obtained when studying and analyzing flows; that is, from short time to long time, from short distance to long distance, there are invisible (or difficult to parameterize) variables and factors that clog or atrophy the production of absolutely incontrovertible scientific conclusions. Another topic that deserves a special mention focuses on the idea that, if the climate does not seem to be a determining and unequivocal variable, in terms of classifying the degree of vulnerability and risk associated with the populations, their behavior, age (and gender), composition and racial structure, there seems to be a consistent line of thought that points to the existence of more vulnerable and risk-sensitive territorial and social contexts. These are the cases of the “black”, “brown” and “indigenous” populations and, also, of the individuals that inhabit the so-called Subnormal Agglomerates.O estudo que se apresenta, procura identificar eventuais correlações entre o padrão de distribuição espacial da SARS-CoV-2 (COVID-19), no Brasil, ensaiando uma correlação entre factores como o clima, as diferentes populações (Indígena, Quilombola e Parda) e a existência de Aglomerados Subnormais, com o propósito de identificar contextos de desigualdade e injustiça social. Partindo da premissa de que o clima interfere com os veículos transmissores de doenças infecto-contagiosas (como demonstrou o estudo de John Snow, em 1854), foram analisados dados relativos à disseminação da COVID-19 no Brasil, a partir de diversos repositórios de dados oficiais, disponibilizados por instituições brasileiras como o IBGE, o Ministério da Saúde do Brasil e a Fundação Oswaldo Cruz, e a partir de dados disponibilizados por outras fontes de que é exemplo maior a plataforma desenvolvida pela sinergia criada pela Johns Hopkins University e a ESRI (a maior empresa produtora de Sistemas de Informação Geográfica-SIG). Os dados foram geoprocessados em SIG e permitiram desenvolver cartografia temática sobre a qual foram efectuadas novas análises geoespaciais e geoestatísticas para a compreensão de padrões. Entre as conclusões principais, destaca-se a necessidade de manter alguma prudência e cuidado com a publicação de trabalhos e com as conclusões apresentadas, dado o elevado grau de incerteza sobre (quase) tudo que envolve a disseminação da doença e, ainda, sobre o que a originou. Existem já muitos estudos publicados mas, em função das perspectivas de abordagem, não é incomum encontrarmos estudos que se alicerçam sobre conclusões contraditórias, dependendo dos enfoques, das sensibilidades científicas, das metodologias utilizadas ou, simplesmente, da qualidade dos dados processados. Destaca-se, também, o facto de que sendo impossível monitorizar os movimentos e a mobilidade dos indivíduos, nem a tempo parcial nem a tempo integral, as correlações efectuadas podem não ser compagináveis com um acompanhamento e controlo adequados da evolução da crise pandémica que se instalou, a nível mundial, porque promover o cruzamento de dados sobre o “espaço dos fixos” ou objectos lidos como tal (os indivíduos) não permite chegar necessariamente aos mesmos resultados quando se estuda e analisa os fluxos; ou seja, do tempo curto ao tempo longo, da curta distância à longa distância, existem variáveis e factores invisíveis ou de difícil parametrização que entopem ou atrofiam a produção de conclusões científicas absolutamente incontroversas. Uma outra conclusão que merece nota de destaque centra-se na ideia de que, se o clima não parece ser uma variável determinante e inequívoca, em termos de classificação do grau de vulnerabilidade e de risco associado às populações, aos seus comportamentos mas, também, à sua composição etária (e de género) e estrutura racial, parece existir uma linha de pensamento consistente que aponta para a existência de contextos territoriais e sociais mais vulneráveis e susceptíveis ao risco. São os casos das populações “preta”, “parda” e “indígena” e, também, dos indivíduos que habitam os designados Aglomerados Subnormais.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-07-07info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/91610.1590/SciELOPreprints.916porhttps://preprints.scielo.org/index.php/scielo/article/view/916/1281Copyright (c) 2020 José Gomes Santoshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantos, José Gomesreponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-07-06T16:01:53Zoai:ops.preprints.scielo.org:preprint/916Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-07-06T16:01:53SciELO Preprints - SciELOfalse |
dc.title.none.fl_str_mv |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population A PRIMEIRA FASE PANDÉMICA DA SARS-COV-2 NO BRASIL; APONTAMENTOS PARA UMA ANÁLISE INTEGRADA DE DESIGUALDADES TERRITORIAIS ASSOCIADAS AOS PADRÕES E RITMOS DE PROPAGAÇÃO DA DOENÇA E SEUS IMPACTES NA POPULAÇÃO BRASILEIRA |
title |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
spellingShingle |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population Santos, José Gomes SARS-CoV-2 (Covid-19) Desigualdades Territoriais Clima População Brasileira SARS-CoV-2 (Covid-19) Territorial inequalities Climate Brazilian population |
title_short |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
title_full |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
title_fullStr |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
title_full_unstemmed |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
title_sort |
The first SARS-CoV-2 outbreak strike in Brazil; notes from an integrated analysis of territorial inequalities associated with patterns of spatial dissemination and ritms of the disease and its impact on the brazilian population |
author |
Santos, José Gomes |
author_facet |
Santos, José Gomes |
author_role |
author |
dc.contributor.author.fl_str_mv |
Santos, José Gomes |
dc.subject.por.fl_str_mv |
SARS-CoV-2 (Covid-19) Desigualdades Territoriais Clima População Brasileira SARS-CoV-2 (Covid-19) Territorial inequalities Climate Brazilian population |
topic |
SARS-CoV-2 (Covid-19) Desigualdades Territoriais Clima População Brasileira SARS-CoV-2 (Covid-19) Territorial inequalities Climate Brazilian population |
description |
The present study seeks to identify possible correlations between the spatial distribution pattern of SARS-CoV-2 (COVID-19) in Brazil, testing a correlation between factors such as climate, different populations (Indigenous, Quilombola and Parda) and the existence of Subnormal Agglomerates, with the purpose of identifying contexts of inequality and social injustice. Based on the premise that the climate interferes with infectious diseases vehicles (as shown by the study by John Snow, in 1854), data were analyzed regarding the dissemination of COVID-19 in Brazil, from various data repositories made available by Brazilian institutions such as IBGE, the Ministry of Health of Brazil and the Oswaldo Cruz Foundation, and other international repository data made available by other sources, of which the platform developed by the synergy created by Johns Hopkins University and ESRI (the most prestigious producer of Geographic Information Systems-GIS) is a greater example. The data were geoprocessed in GIS platforms and allowed the development of thematic cartography on which new geospatial and geostatistical analyzes were carried out to understand patterns. Among the main conclusions, we highlight the need to maintain some prudence with the publication of papers and their conclusions, given the high degree of uncertainty about (almost) everything that involves the spread of the disease and, also, about “what” and “how” it was born. There are already many published studies, but depending on the perspective of approach, it is not uncommon to find studies that are based on contradictory conclusions, depending on the approaches, the scientific sensitivities, the methodologies used or, simply, the quality of the processed data. It is also noteworthy that it is impossible to monitor the movements and mobility of individuals, either part-time or full-time, the correlations made may not be compatible with adequate monitoring and control of the evolution of the pandemic crisis that is taking place worldwide, because promoting the crossing of data on the “fixed space” or objects read as such (individuals) does not necessarily allow the same results to be obtained when studying and analyzing flows; that is, from short time to long time, from short distance to long distance, there are invisible (or difficult to parameterize) variables and factors that clog or atrophy the production of absolutely incontrovertible scientific conclusions. Another topic that deserves a special mention focuses on the idea that, if the climate does not seem to be a determining and unequivocal variable, in terms of classifying the degree of vulnerability and risk associated with the populations, their behavior, age (and gender), composition and racial structure, there seems to be a consistent line of thought that points to the existence of more vulnerable and risk-sensitive territorial and social contexts. These are the cases of the “black”, “brown” and “indigenous” populations and, also, of the individuals that inhabit the so-called Subnormal Agglomerates. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-07 |
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/916 10.1590/SciELOPreprints.916 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/916 |
identifier_str_mv |
10.1590/SciELOPreprints.916 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/916/1281 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 José Gomes Santos https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 José Gomes Santos https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
dc.source.none.fl_str_mv |
reponame:SciELO Preprints instname:SciELO instacron:SCI |
instname_str |
SciELO |
instacron_str |
SCI |
institution |
SCI |
reponame_str |
SciELO Preprints |
collection |
SciELO Preprints |
repository.name.fl_str_mv |
SciELO Preprints - SciELO |
repository.mail.fl_str_mv |
scielo.submission@scielo.org |
_version_ |
1797047819336417280 |