Aplicação de Redes de Deep Learning Recurrent Neural Network, Long Short-Term Memory e Gated Recurrent Unit na Predição da COVID-19 no Cenário Pós-Vacinação
Resumo
Este trabalho propõe a adaptação de três redes neurais artificiais para prever séries temporais da COVID-19 no Brasil, considerando o atual cenário de escassez de dados e ondas recorrentes da doença, mas de menor magnitude comparado a 2020 e 2021. O principal objetivo é avaliar o desempenho de diferentes arquiteturas de redes neurais na previsão dos casos diários de COVID-19 no estado de São Paulo. As arquiteturas exploradas foram: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) e Gated Recurrent Unit (GRU). O desempenho de cada modelo foi analisado quanto à aderência aos dados reais e à capacidade de capturar padrões temporais complexos, em um contexto de aumentos-e-declínios súbitos de casos de menor gravidade devido à vacinação. Os resultados demonstraram alta precisão das redes treinadas e forneceram diferentes perspectivas para melhorar a qualidade das predições, essenciais para estratégias de controle da pandemia, especialmente durante os períodos de recrudescimento da doença. Assim, este trabalho visa contribuir para o avanço de aplicações de redes neurais preditoras da COVID-19, particularmente no contexto pós-vacinação.
Referências
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Amaral, F., Casaca, W., Oishi, C. M., and Cuminato, J. A. (2021b). Towards providing effective data-driven responses to predict the covid-19 in São Paulo and Brazil. Sensors, 21(2):540.
Capomaccio, S. (2024). Casos de covid-19 registram aumento e vacinação ainda é importante. Jornal da USP.
Chandra, R., Jain, A., and Singh Chauhan, D. (2022). Deep learning via lstm models for covid-19 infection forecasting in india. PloS one, 17(1):e0262708.
Cho, K., van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder–decoder approaches. In Wu, D., Carpuat, M., Carreras, X., and Vecchi, E. M., editors, Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 103–111, Doha, Qatar. Association for Computational Linguistics.
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
Davahli, M., Fiok, K., Karwowski, W., Aljuaid, A., and Taiar, R. (2021). Predicting the dynamics of the covid-19 in the united states using graph theory-based neural networks. International Journal of Environmental Research and Public Health, 18(7).
Frazier, P. I. (2018). A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811.
Gomez-Cravioto, D. A., Diaz-Ramos, R. E., Cantu-Ortiz, F. J., and Ceballos, H. G. (2021). Data analysis and forecasting of the covid-19 spread: A comparison of recurrent neural networks and time series models. Cognitive Computation, pages 1–12.
Hawas, M. (2020). Generated time-series prediction data of covid-19’s daily infections in brazil by using recurrent neural networks. Data in Brief, 32:106175.
Hewamalage, H., Bergmeir, C., and Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1):388–427.
Linge, S. and Langtangen, H. P. (2020). Programming for Computations-Python: A Gentle Introduction to Numerical Simulations with Python 3.6. Springer Nature.
Ma, J., Zhao, H., and Schuck, P. (2015). A histogram approach to the quality of fit in sedimentation velocity analyses. Analytical Biochemistry, 483:1–3.
Mendes, V. (2024). Por que covid-19 ainda mata tanta gente no brasil. BBC News Brasil.
Microsoft (2023). Microsoft. [link]. Acesso em 20 de julho de 2023.
Ministério da Saúde (2024). Relatório da situação de saúde diante da pandemia de covid19. Disponível em: [link]. Acesso em: 14 mar. 2024.
Oliveira, C. (2024). Controle sobre a covid-19 ainda é instável no país após quatro anos do 1º caso. Brasil de Fato.
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., Yan, P., and Chowell, G. (2020). Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang, China: February 13–23, 2020. Journal of Clinical Medicine, 9:596.
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c. (2015). Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28.
Snoek, J., Larochelle, H., and Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc.
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R. (2015). Scalable bayesian optimization using deep neural networks. In International conference on machine learning, pages 2171–2180. PMLR.
Worldometer (2024). Covid-19 Coronavirus pandemic. Disponível em: [link]. Acesso em: 14 mar. 2024.
Worobey, M. (2021). Dissecting the early COVID-19 cases in Wuhan. Science, 374(6572):1202–1204.
Zeroual, A., Harrou, F., Dairi, A., and Sun, Y. (2020). Deep learning methods for forecasting covid-19 time-series data: A comparative study. Chaos, Solitons & Fractals, 140:110121.
Publicado
21/07/2024
Como Citar
FERREIRA, Rafaella Silva; CASACA, Wallace; COLNAGO, Marilaine.
Aplicação de Redes de Deep Learning Recurrent Neural Network, Long Short-Term Memory e Gated Recurrent Unit na Predição da COVID-19 no Cenário Pós-Vacinação. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 51. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 145-156.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2024.2562.
