Multidimensional Analysis of the Influence of Socioeconomic and Political Indicators on the Spread of COVID-19: A Case Study of Brazilian Cities (2020-2024)
Resumo
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.
Palavras-chave:
COVID-19, Data Analysis, Politics
Referências
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Aron, J. and Muellbauer, J. (2022). Excess mortality versus COVID-19 death rates: A spatial analysis of socioeconomic disparities and political allegiance across U.S. States. Review of Income and Wealth, 68(2):348–392.
Ayifah, R. N. Y. and Ayifah, E. (2023). COVID-19 lockdown policy and national elections: A quasi-experimental analysis of Ghana’s 2020 election. International Social Science Journal, 73(248):685–704.
Barberia, L., Moreira, N. d. P., Carvalho, R. d. J., Oliveira, M. L. C., Rosa, I. S. C., and Zamudio, M. (2022). The relationship between ideology and COVID-19 deaths: what we know and what we still need to know. Brazilian Political Science Review, 16:e0002.
Bolognesi, B., Ribeiro, E., and Codato, A. (2020). Esquerda, centro ou direita? Como classificar os partidos no Brasil. Observatório das Eleições.
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Breiman, L. (2001). Random forests. Machine learning, 45:5–32.
Cipullo, D. and Le Moglie, M. (2022). To vote, or not to vote? Electoral campaigns and the spread of COVID-19. European Journal of Political Economy, 72:102118.
Constantino, S. M., Cooperman, A. D., and Moreira, T. M. (2021). Voting in a global pandemic: assessing dueling influences of COVID-19 on turnout. Social Science Quarterly, 102(5):2210–2235.
Desmet, K. and Wacziarg, R. (2022). JUE insight: Understanding spatial variation in COVID-19 across the United States. Journal of urban economics, 127:103332.
Fernandes, G. A. d. A. L. and de Almeida Lopes Fernandes, I. F. (2022). Populism and health. An evaluation of the effects of right-wing populism on the COVID-19 pandemic in Brazil. PLoS One, 17(12):e0269349.
Hartigan, J. A., Wong, M. A., et al. (1979). A k-means clustering algorithm. Applied statistics, 28(1):100–108.
Lima, E. E. C. d., Costa, L. C. C. d., Souza, R. F., Rocha, C. O. d. E., and Ichihara, M. Y. T. (2024). Presidential election results in 2018-2022 and its association with excess mortality during the 2020-2021 COVID-19 pandemic in brazilian municipalities. Cadernos de Saúde Pública, 40:e00194723.
Menuzzo, V. A., Santanchè, A., and Gomes-Jr, L. (2021). Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados, pages 295–300. SBC.
Rennó, L. R. (2020). The Bolsonaro voter: issue positions and vote choice in the 2018 brazilian presidential elections. Latin American Politics and Society, 62(4):1–23.
Rönn, M. M., Menzies, N. A., and Salomon, J. A. (2023). Vaccination and voting patterns in the U.S.: analysis of COVID-19 and flu surveys from 2010 to 2022. American Journal of Preventive Medicine, 65(3):458–466.
Sott, M. K., Bender, M. S., and da Silva Baum, K. (2022). COVID-19 outbreak in Brazil: health, social, political, and economic implications. International Journal of Health Services, 52(4):442–454.
Tiwari, S., Chanak, P., and Singh, S. K. (2022). A review of the machine learning algorithms for COVID-19 case analysis. IEEE Transactions on Artificial Intelligence, 4(1):44–59.
Wu, S. (2023). The spatial data analysis of determinants of U.S. presidential voting results in the rustbelt states during the COVID-19 pandemic. ISPRS International Journal of Geo-Information, 12(6):212.
Xavier, D. R., e Silva, E. L., Lara, F. A., e Silva, G. R., Oliveira, M. F., Gurgel, H., and Barcellos, C. (2022). Involvement of political and socio-economic factors in the spatial and temporal dynamics of COVID-19 outcomes in Brazil: a population-based study. The Lancet Regional Health–Americas, 10.
Amaral, O. E. d. (2020). The victory of Jair Bolsonaro according to the Brazilian electoral study of 2018. Brazilian Political Science Review, 14:e0004.
Aron, J. and Muellbauer, J. (2022). Excess mortality versus COVID-19 death rates: A spatial analysis of socioeconomic disparities and political allegiance across U.S. States. Review of Income and Wealth, 68(2):348–392.
Ayifah, R. N. Y. and Ayifah, E. (2023). COVID-19 lockdown policy and national elections: A quasi-experimental analysis of Ghana’s 2020 election. International Social Science Journal, 73(248):685–704.
Barberia, L., Moreira, N. d. P., Carvalho, R. d. J., Oliveira, M. L. C., Rosa, I. S. C., and Zamudio, M. (2022). The relationship between ideology and COVID-19 deaths: what we know and what we still need to know. Brazilian Political Science Review, 16:e0002.
Bolognesi, B., Ribeiro, E., and Codato, A. (2020). Esquerda, centro ou direita? Como classificar os partidos no Brasil. Observatório das Eleições.
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Breiman, L. (2001). Random forests. Machine learning, 45:5–32.
Cipullo, D. and Le Moglie, M. (2022). To vote, or not to vote? Electoral campaigns and the spread of COVID-19. European Journal of Political Economy, 72:102118.
Constantino, S. M., Cooperman, A. D., and Moreira, T. M. (2021). Voting in a global pandemic: assessing dueling influences of COVID-19 on turnout. Social Science Quarterly, 102(5):2210–2235.
Desmet, K. and Wacziarg, R. (2022). JUE insight: Understanding spatial variation in COVID-19 across the United States. Journal of urban economics, 127:103332.
Fernandes, G. A. d. A. L. and de Almeida Lopes Fernandes, I. F. (2022). Populism and health. An evaluation of the effects of right-wing populism on the COVID-19 pandemic in Brazil. PLoS One, 17(12):e0269349.
Hartigan, J. A., Wong, M. A., et al. (1979). A k-means clustering algorithm. Applied statistics, 28(1):100–108.
Lima, E. E. C. d., Costa, L. C. C. d., Souza, R. F., Rocha, C. O. d. E., and Ichihara, M. Y. T. (2024). Presidential election results in 2018-2022 and its association with excess mortality during the 2020-2021 COVID-19 pandemic in brazilian municipalities. Cadernos de Saúde Pública, 40:e00194723.
Menuzzo, V. A., Santanchè, A., and Gomes-Jr, L. (2021). Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados, pages 295–300. SBC.
Rennó, L. R. (2020). The Bolsonaro voter: issue positions and vote choice in the 2018 brazilian presidential elections. Latin American Politics and Society, 62(4):1–23.
Rönn, M. M., Menzies, N. A., and Salomon, J. A. (2023). Vaccination and voting patterns in the U.S.: analysis of COVID-19 and flu surveys from 2010 to 2022. American Journal of Preventive Medicine, 65(3):458–466.
Sott, M. K., Bender, M. S., and da Silva Baum, K. (2022). COVID-19 outbreak in Brazil: health, social, political, and economic implications. International Journal of Health Services, 52(4):442–454.
Tiwari, S., Chanak, P., and Singh, S. K. (2022). A review of the machine learning algorithms for COVID-19 case analysis. IEEE Transactions on Artificial Intelligence, 4(1):44–59.
Wu, S. (2023). The spatial data analysis of determinants of U.S. presidential voting results in the rustbelt states during the COVID-19 pandemic. ISPRS International Journal of Geo-Information, 12(6):212.
Xavier, D. R., e Silva, E. L., Lara, F. A., e Silva, G. R., Oliveira, M. F., Gurgel, H., and Barcellos, C. (2022). Involvement of political and socio-economic factors in the spatial and temporal dynamics of COVID-19 outcomes in Brazil: a population-based study. The Lancet Regional Health–Americas, 10.
Publicado
14/10/2024
Como Citar
REIS, Rôney; BRAYNER, Angelo; ÂNGELO, Miguel; MENEZES, Ronaldo.
Multidimensional Analysis of the Influence of Socioeconomic and Political Indicators on the Spread of COVID-19: A Case Study of Brazilian Cities (2020-2024). In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 394-405.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240824.