Correlação entre Indicadores Socioeconômicos e a COVID-19: uma revisão sistemática da literatura
Resumo
A pandemia da COVID-19, causada pelo novo coronavírus (SARS-CoV-2), iniciou-se em dezembro de 2019 na China, colocando a saúde da população mundial em grande alerta. Este artigo objetiva apresentar uma Revisão Sistemática da Literatura que análise a correlação entre indicadores socioeconômicos e a pandemia da COVID-19 através de técnicas de aprendizagem de máquina, identificando quais técnicas e software de análise de dados foram utilizados. Foram identificados os estudos mais relevantes publicados no período de 2018 a 2022, que abordam o tema proposto. Após a triagem completa a pesquisa destacou 10 artigos selecionados no total de 814 analisados.
Palavras-chave:
COVID-19, indicadores socioeconômicos, algoritmos de aprendizagem de máquina
Referências
Ahmad, F., Almuayqil, S. N., Humayun, M., Naseem, S., Khan, W. A., and Junaid, K. (2021). Prediction of covid-19 cases using machine learning for effective public health management. volume 66, pages 2265–2282.
Amoroso, N., Cilli, R., Maggipinto, T., Monaco, A., Tangaro, S., and Bellotti, R. (2021). Satellite data and machine learning reveal a significant correlation between no2 and covid-19 mortality. volume 204, page 111970.
Galvao, M. C. B. and Ricarte, I. L. M. (2019). Revisao sistematica da literatura: Conceituacao, producao e publicacao. volume 6, page 57–73.
Hyman, M., Mark, C., Imteaj, A., Ghiaie, H., Rezapour, S., Sadri, A. M., and Amini, M. H. (2021). Data analytics to evaluate the impact of infectious disease on economy: Case study of covid-19 pandemic. volume 2, page 100315.
Kannan, S., Subbaram, K., Ali, S., and Kannan, H. (2020). The role of artificial intelligence and machine learning techniques: Race for covid-19 vaccine. volume 15. Kowsar.
Magazzino, C., Mele, M., and Morelli, G. (2021a). The relationship between renewable energy and economic growth in a time of covid-19: A machine learning experiment on the brazilian economy. volume 13.
Magazzino, C., Mele, M., and Sarkodie, S. A. (2021b). The nexus between covid-19 deaths, air pollution and economic growth in new york state: Evidence from deep machine learning. volume 286, page 112241.
Mele, M. and Magazzino, C. (2021). Pollution, economic growth, and covid-19 deaths in india: a machine learning evidence. volume 28, pages 2669–2677. Springer.
Mihoub, A., Snoun, H., Krichen, M., Salah, R. B. H., and Kahia, M. (2020). Predicting covid-19 spread level using socio-economic indicators and machine learning techniques. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pages 128–133.
Paul, A., Englert, P., and Varga, M. (2021). Socio-economic disparities and COVID-19 in the USA. volume 2, page 035017. IOP Publishing.
Punn, N. S., Sonbhadra, S. K., and Agarwal, S. (2020). Covid-19 epidemic analysis using machine learning and deep learning algorithms. Cold Spring Harbor Laboratory Press.
Siddiqui, M. K., Morales-Menendez, R., Gupta, P. K., Iqbal, H. M., Hussain, F., Khatoon, K., and Ahmad, S. (2020). Correlation between temperature and covid-19 (suspected, confirmed and death) cases based on machine learning analysis.
Zaremba, A., Kizys, R., Tzouvanas, P., Aharon, D. Y., and Demir, E. (2021). The quest for multidimensional financial immunity to the covid-19 pandemic: Evidence from international stock markets. volume 71, page 101284
Amoroso, N., Cilli, R., Maggipinto, T., Monaco, A., Tangaro, S., and Bellotti, R. (2021). Satellite data and machine learning reveal a significant correlation between no2 and covid-19 mortality. volume 204, page 111970.
Galvao, M. C. B. and Ricarte, I. L. M. (2019). Revisao sistematica da literatura: Conceituacao, producao e publicacao. volume 6, page 57–73.
Hyman, M., Mark, C., Imteaj, A., Ghiaie, H., Rezapour, S., Sadri, A. M., and Amini, M. H. (2021). Data analytics to evaluate the impact of infectious disease on economy: Case study of covid-19 pandemic. volume 2, page 100315.
Kannan, S., Subbaram, K., Ali, S., and Kannan, H. (2020). The role of artificial intelligence and machine learning techniques: Race for covid-19 vaccine. volume 15. Kowsar.
Magazzino, C., Mele, M., and Morelli, G. (2021a). The relationship between renewable energy and economic growth in a time of covid-19: A machine learning experiment on the brazilian economy. volume 13.
Magazzino, C., Mele, M., and Sarkodie, S. A. (2021b). The nexus between covid-19 deaths, air pollution and economic growth in new york state: Evidence from deep machine learning. volume 286, page 112241.
Mele, M. and Magazzino, C. (2021). Pollution, economic growth, and covid-19 deaths in india: a machine learning evidence. volume 28, pages 2669–2677. Springer.
Mihoub, A., Snoun, H., Krichen, M., Salah, R. B. H., and Kahia, M. (2020). Predicting covid-19 spread level using socio-economic indicators and machine learning techniques. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pages 128–133.
Paul, A., Englert, P., and Varga, M. (2021). Socio-economic disparities and COVID-19 in the USA. volume 2, page 035017. IOP Publishing.
Punn, N. S., Sonbhadra, S. K., and Agarwal, S. (2020). Covid-19 epidemic analysis using machine learning and deep learning algorithms. Cold Spring Harbor Laboratory Press.
Siddiqui, M. K., Morales-Menendez, R., Gupta, P. K., Iqbal, H. M., Hussain, F., Khatoon, K., and Ahmad, S. (2020). Correlation between temperature and covid-19 (suspected, confirmed and death) cases based on machine learning analysis.
Zaremba, A., Kizys, R., Tzouvanas, P., Aharon, D. Y., and Demir, E. (2021). The quest for multidimensional financial immunity to the covid-19 pandemic: Evidence from international stock markets. volume 71, page 101284
Publicado
16/05/2022
Como Citar
SOUZA, Lucelia Lima; BORCHARTT, Tiago Bonini.
Correlação entre Indicadores Socioeconômicos e a COVID-19: uma revisão sistemática da literatura. In: TEMAS EMERGENTES: TRANSFORMAÇÃO DIGITAL E PROBLEMAS SOCIO-URBANOS - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 18. , 2022, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 277-284.
DOI: https://doi.org/10.5753/sbsi_estendido.2022.222864.
