Multidimensional Analysis of the Influence of Socioeconomic and Political Indicators on the Spread of COVID-19: A Case Study of Brazilian Cities (2020-2024)

This paper explores the influence of socioeconomic indicators and political decisions on the spread of COVID-19 across Brazilian cities from 2020 to 2024. Leveraging data on COVID-19 cases, deaths, electoral outcomes from 2020 and 2022, and the Human Development Index (HDI) from 2010, we employ a multidimensional analytical framework encompassing temporal, spatial, and statistical dimensions to uncover the correlations among these variables. Time series models, such as ARIMA, were employed to detect trends over time, while spatial correlation analyses and machine learning techniques were applied to reveal geographical variations in virus spread. Our findings highlight significant regional disparities in COVID-19 proliferation, carrying crucial implications for the formulation of targeted public policies.

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Published
2024-10-14
DOI
https://doi.org/10.5753/sbbd.2024.240824
Primary Topic
COVID-19 Pandemic Impacts
Type
article
Field-Weighted Citation Impact
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article

Multidimensional Analysis of the Influence of Socioeconomic and Political Indicators on the Spread of COVID-19: A Case Study of Brazilian Cities (2020-2024)

Miguel Ângelo, Raúl Reis, Ângelo Brayner, Ronaldo Menezes
COVID-19 Pandemic Impacts
article

Multidimensional Analysis of the Influence of Socioeconomic and Political Indicators on the Spread of COVID-19: A Case Study of Brazilian Cities (2020-2024)

Miguel Ângelo, Raúl Reis, Ângelo Brayner, Ronaldo Menezes
article en

Abstract

This paper explores the influence of socioeconomic indicators and political decisions on the spread of COVID-19 across Brazilian cities from 2020 to 2024. Leveraging data on COVID-19 cases, deaths, electoral outcomes from 2020 and 2022, and the Human Development Index (HDI) from 2010, we employ a multidimensional analytical framework encompassing temporal, spatial, and statistical dimensions to uncover the correlations among these variables. Time series models, such as ARIMA, were employed to detect trends over time, while spatial correlation analyses and machine learning techniques were applied to reveal geographical variations in virus spread. Our findings highlight significant regional disparities in COVID-19 proliferation, carrying crucial implications for the formulation of targeted public policies.

Universidade Federal do Ceará (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Sustainable cities and communities
Openalex Percentile: Top 77%
COVID-19 Pandemic Impacts
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