Spatial Analysis: A Socioeconomic View on the Incidence of the New Coronavirus in Paraná-Brazil

Elizabeth Giron Cima (), Miguel Angel Uribe Opazo, Marcos Roberto Bombacini, Weimar Freire da Rocha Junior and Luciana Pagliosa Carvalho Guedes
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Elizabeth Giron Cima: Centro de Ciências Exatas e Tecnológicas (CCET), Western Paraná State University (UNIOESTE), Cascavel 85819-110, PR, Brazil
Miguel Angel Uribe Opazo: Centro de Ciências Exatas e Tecnológicas (CCET), Western Paraná State University (UNIOESTE), Cascavel 85819-110, PR, Brazil
Marcos Roberto Bombacini: Coordenação do Curso de Graduação em Engenharia Eletrônica (COELT), Federal University of Technology—Paraná (UTFPR), Curitiba 85902-490, PR, Brazil
Weimar Freire da Rocha Junior: Centro de Ciências Exatas e Tecnológicas (CCET), Western Paraná State University (UNIOESTE), Cascavel 85819-110, PR, Brazil
Luciana Pagliosa Carvalho Guedes: Centro de Ciências Exatas e Tecnológicas (CCET), Western Paraná State University (UNIOESTE), Cascavel 85819-110, PR, Brazil

Stats, 2022, vol. 5, issue 4, 1-15

Abstract: This paper presents a spatial analysis of the incidence rate of COVID-19 cases in the state of Paraná, Brazil, from June to December 2020, and a study of the incidence rate of COVID-19 cases associated with socioeconomic variables, such as the Gini index, Theil-L index, and municipal human development index (MHDI). The data were provided from the Paraná State Health Department and Paraná Institute for Economic and Social Development. For the study of spatial autocorrelation, the univariate global Moran index (I), local univariate Moran (LISA), global Geary (c), and univariate local Geary ( c i ) were calculated. For the analysis of the spatial correlation, the global bivariate Moran index ( I x y ) , the local multivariate Geary indices ( C i M ) , and the bivariate Lee index ( L x y ) were calculated. There is significant positive spatial autocorrelation between the incidence rate of COVID-19 cases and correlations between the incidence rate of COVID-19 cases and the Gini index, Theil-L index, and MHDI in the regions under study. The highest risk areas were concentrated in the macro-regions: east and west. Understanding the spatial distribution of COVID-19, combined with economic and social factors, can contribute to greater efficiency in preventive actions and the control of new viral epidemics.

Keywords: risk areas; virus spread; correlation indexes; preventive measures; decision making (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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