Preprint Article Version 1 This version is not peer-reviewed

An Integrated Geographical Approach of the Spatiotemporal Distribution of COVID-19 in Minas Gerais State, Brazil

Elivelton Da Silva Fonseca
* ORCID logo ,
Laira Cristina da Silva
,
Igor Silva
,
Izabela Perrisato Borges
,
Jean Ezequiel Limongi
,
Stefan Vilges de Oliveira
,
Samuel do Carmo Lima
,
Kefyalew Addis Alene
ORCID logo
Version 1 : Received: 23 May 2023 / Approved: 25 May 2023 / Online: 25 May 2023 (10:12:34 CEST)

How to cite: Da Silva Fonseca, E.; Cristina da Silva, L.; Silva, I.; Perrisato Borges, I.; Ezequiel Limongi, J.; Vilges de Oliveira, S.; do Carmo Lima, S.; Addis Alene, K. An Integrated Geographical Approach of the Spatiotemporal Distribution of COVID-19 in Minas Gerais State, Brazil. Preprints.org 2023, 2023051793. https://doi.org/10.20944/preprints202305.1793.v1 Da Silva Fonseca, E.; Cristina da Silva, L.; Silva, I.; Perrisato Borges, I.; Ezequiel Limongi, J.; Vilges de Oliveira, S.; do Carmo Lima, S.; Addis Alene, K. An Integrated Geographical Approach of the Spatiotemporal Distribution of COVID-19 in Minas Gerais State, Brazil. Preprints.org 2023, 2023051793. https://doi.org/10.20944/preprints202305.1793.v1

Abstract

Abstract. Background: COVID-19's first victim was announced by Chinese health authorities on the 11th of January 2020. On January 13, the first official case was reported outside China, in Thailand. On January 25, the same occurred in São Paulo and on March 8, the first case was recorded in Minas Gerais. From that point onwards until the 3rd of October 2020, a total of 370,911 cases and 9,204 deaths were recorded in the state. This study aims to investigate spatiotemporal patterns of COVID-19 with incidence from March 22 to October 3 of 2020. Methods: The database itself was obtained from Health Division of Minas Gerais state. The vulnerability index was calculated using a principal component analysis. Moran's I autocorrelation was tested, z-score and P-value < 0.05. Results: From March 22 to October 3 of 2020 the incidence level varied from 45.680/100,000 to 312.130/100,000. The most influential variables were: illiteracy, gross domestic product and breath apparel per municipality. The clusters were concentrated in the metropolitan area of Belo Horizonte, Zona da Mata and Triangulo Mineiro. Conclusion: The spatial distribution of COVID-19 from week 13 until week 40 showed that different levels of endemicity and mesoregional vulnerabilities were represented in these maps.

Keywords

COVID-19; Clusters; Multivariate geospatial model; Minas Gerais state

Subject

Public Health and Healthcare, Public Health and Health Services

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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