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. 2022 May 17;17(5):e0268538.
doi: 10.1371/journal.pone.0268538. eCollection 2022.

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

Arthur Pimentel Gomes de Souza  1 , Caroline Maria de Miranda Mota  1   2 , Amanda Gadelha Ferreira Rosa  1 , Ciro José Jardim de Figueiredo  3 , Ana Lúcia Bezerra Candeias  4
Affiliations

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

Arthur Pimentel Gomes de Souza et al. PLoS One. .

Abstract

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Ordered steps of the present study.
Fig 2
Fig 2. Location of the study area.
Sources: Brazilian Institute of Geography and Statistics (IBGE) 2021, and software ArcGIS 10.4.1.
Fig 3
Fig 3. Evolution of daily COVID-19 cases in Recife from March 12th to July 8th, 2020.
Fig 4
Fig 4. Spatial distribution of cumulative reported COVID-19 cases in Recife at the neighbourhood-level.
Sources: Brazilian Institute of Geography and Statistics (IBGE) 2021, and software ArcGIS 10.4.1.
Fig 5
Fig 5. Spatial distribution of case-fatality in Recife as a result of cluster analysis.
Fig 6
Fig 6. Performance of local R2 across the neighborhoods of Recife city.
Sources: Brazilian Institute of Geography and Statistics (IBGE) 2021, and software ArcGIS 10.4.1.
Fig 7
Fig 7. Effects of determinants on prediction of COVID-19 cases according to their coefficients in GWR regression.
Sources: Brazilian Institute of Geography and Statistics (IBGE) 2021, and software ArcGIS 10.4.1.
See this image and copyright information in PMC

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