Análise da sazonalidade do Vírus Sincicial Respiratório no Brasil usando Clustering
Resumo
O Vírus Sincicial Respiratório (VSR) é um dos principais agentes de infecção do trato respiratório inferior em populações vulneráveis. A pandemia de COVID-19 e as intervenções não farmacológicas alteraram temporariamente sua sazonalidade conhecida. Este trabalho investiga se as hospitalizações por VSR no Brasil apresentam padrões regionais distintos e avalia sua organização antes, durante e após a pandemia da COVID-19. Utilizando dados do SIVEP-Gripe (2015-2025), aplicou-se decomposição STL e aprendizado não supervisionado. Resultados indicam mudanças na sazonalidade, duração e amplitude dos surtos epidêmicos nos períodos analisados.
Referências
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Falsey, A. R. and Walsh, E. E. (2005). Respiratory syncytial virus infection in elderly adults. Drugs & aging, 22(7):577–587.
Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley.
Rago, A. R., D’Arrigo, S. F., Osmani, M., Espinosa, C. M., and Torres, C. M. (2024). Respiratory syncytial virus: Epidemiology, burden of disease, and clinical update. Advances in Pediatrics, 71(1):107–118. Advances In Pediatrics.
Sah, R., Zaman, K., Mohanty, A., Al-Ahdal, T., Awad, H., Padhi, B. K., and Bhargava, A. (2023). Respiratory syncytial virus with ongoing covid-19: is it an emerging threat? Annals of Medicine and Surgery, 85(1):67–70.
Sakoe, H. and Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1):43–49.
Sanz-Muñoz, I., Tamames-Gómez, S., Castrodeza-Sanz, J., Eiros-Bouza, J. M., and de Lejarazu-Leonardo, R. O. (2021). Social distancing, lockdown and the wide use of mask; a magic solution or a double-edged sword for respiratory viruses epidemiology? Vaccines, 9(6):595.
Shi, T., Vennard, S., Mahdy, S., and Nair, H. (2022). Risk factors for poor outcome or death in young children with respiratory syncytial virus–associated acute lower respiratory tract infection: A systematic review and meta-analysis. The Journal of infectious diseases, 226(Supplement 1):S10–S16.
Silva, R., Xavier, F., Saraiva, A., and Cugnasca, C. (2020). Unsupervised machine learning and pandemics spread: the case of covid-19. In Anais do XX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 506–511, Porto Alegre, RS, Brasil. SBC.
Thindwa, D., Li, K., Cooper-Wootton, D., Zheng, Z., Pitzer, V. E., and Weinberger, D. M. (2024). Global patterns of rebound to normal rsv dynamics following covid-19 suppression. BMC Infectious Diseases, 24:635.
Zheng, Z., Pitzer, V. E., Shapiro, E. D., Bont, L. J., and Weinberger, D. M. (2021). Estimation of the timing and intensity of reemergence of respiratory syncytial virus following the covid-19 pandemic in the us. JAMA Network Open, 4(12):e2141779–e2141779.
Falsey, A. R. and Walsh, E. E. (2005). Respiratory syncytial virus infection in elderly adults. Drugs & aging, 22(7):577–587.
Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley.
Rago, A. R., D’Arrigo, S. F., Osmani, M., Espinosa, C. M., and Torres, C. M. (2024). Respiratory syncytial virus: Epidemiology, burden of disease, and clinical update. Advances in Pediatrics, 71(1):107–118. Advances In Pediatrics.
Sah, R., Zaman, K., Mohanty, A., Al-Ahdal, T., Awad, H., Padhi, B. K., and Bhargava, A. (2023). Respiratory syncytial virus with ongoing covid-19: is it an emerging threat? Annals of Medicine and Surgery, 85(1):67–70.
Sakoe, H. and Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1):43–49.
Sanz-Muñoz, I., Tamames-Gómez, S., Castrodeza-Sanz, J., Eiros-Bouza, J. M., and de Lejarazu-Leonardo, R. O. (2021). Social distancing, lockdown and the wide use of mask; a magic solution or a double-edged sword for respiratory viruses epidemiology? Vaccines, 9(6):595.
Shi, T., Vennard, S., Mahdy, S., and Nair, H. (2022). Risk factors for poor outcome or death in young children with respiratory syncytial virus–associated acute lower respiratory tract infection: A systematic review and meta-analysis. The Journal of infectious diseases, 226(Supplement 1):S10–S16.
Silva, R., Xavier, F., Saraiva, A., and Cugnasca, C. (2020). Unsupervised machine learning and pandemics spread: the case of covid-19. In Anais do XX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 506–511, Porto Alegre, RS, Brasil. SBC.
Thindwa, D., Li, K., Cooper-Wootton, D., Zheng, Z., Pitzer, V. E., and Weinberger, D. M. (2024). Global patterns of rebound to normal rsv dynamics following covid-19 suppression. BMC Infectious Diseases, 24:635.
Zheng, Z., Pitzer, V. E., Shapiro, E. D., Bont, L. J., and Weinberger, D. M. (2021). Estimation of the timing and intensity of reemergence of respiratory syncytial virus following the covid-19 pandemic in the us. JAMA Network Open, 4(12):e2141779–e2141779.
Publicado
01/06/2026
Como Citar
FREITAS, Alejandro Martins de; SOUZA, Fernanda Sumika Hojo de.
Análise da sazonalidade do Vírus Sincicial Respiratório no Brasil usando Clustering. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 60-65.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2026.21685.
