O impacto da rejeição da vacina COVID-19 na admissão hospitalar e a disseminação das variantes pelo mundo: implicações para a política de saúde

Autores

DOI:

https://doi.org/10.33448/rsd-v11i11.33435

Palavras-chave:

Coronavírus; Vacina; Modelo ARIMA; Rejeição; Hospitalização.

Resumo

Objetivo. Prever quando diferentes países atingirão 70% da população totalmente vacinada contra o COVID-19 e avaliar os efeitos da rejeição da vacina no número de pacientes internados na UTI e nas taxas de infecções por omicron e outras variantes do SARS-Cov-2. Métodos. Dados sobre o 'número de pacientes com COVID-19 admitidos na UTI', 'taxa de pessoas que receberam pelo menos uma dose da vacina COVID-19' e 'porcentagem da população não vacinada (EUA, Brasil, Europa, África, Ásia) que se recusa a receber a primeira dose da vacina COVID-19' foram coletados de um banco de dados público de dezembro de 2020 a janeiro de 2022. Modelos baseados em séries temporais foram usados ​​para prever quando os países atingirão a taxa de 70% da população totalmente vacinada. Resultados. O modelo ARIMA foi robusto para prever a vacinação COVID-19 em diferentes países. Nos EUA, Brasil, União Europeia e Ásia 70% da população foi vacinada contra a COVID-19 entre setembro de 2021 a abril de 2022. Na África, a previsão é apenas no início de 2024. O percentual da população não vacinada teve um efeito significativo no aumento de internações em UTI e no aumento de casos de variantes ômicron, alfa, delta e gama. Conclusão. Embora o modelo ARIMA tenha apresentado o melhor desempenho para prever os padrões de vacinação, sua acurácia pode diminuir com o tempo, principalmente devido à taxa de rejeição da vacinação. Nesse cenário, estratégias para melhorar a vacinação devem ser implementadas.

Referências

Aguado, M. T., Barratt, J., Beard, J. R., Blomberg, B. B., Chen, W. H., Hickling, J., ... & Ortiz, J. R. (2018). Report on WHO meeting on immunization in older adults: Geneva, Switzerland, 22–23 March 2017. Vaccine, 36(7), 921-931.

Arnold, T. W. (2010). Uninformative parameters and model selection using Akaike's Information Criterion. The Journal of Wildlife Management, 74(6), 1175-1178.

Aw, J., Seng, J. J. B., Seah, S. S. Y., & Low, L. L. (2021). COVID-19 vaccine hesitancy—A scoping review of literature in high-income countries. Vaccines, 9(8), 900.

Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., & Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(29), 16732-16738.

Bhatta, M., Nandi, S., Dutta, S., & Saha, M. K. (2022). Coronavirus (SARS-CoV-2): a systematic review for potential vaccines. Human vaccines & immunotherapeutics, 18(1), 1865774.

Billah, B., King, M. L., Snyder, R. D., & Koehler, A. B. (2006). Exponential smoothing model selection for forecasting. International journal of forecasting, 22(2), 239-247.

Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.

Brown, R. G., & Meyer, R. F. (1961). The fundamental theorem of exponential smoothing. Operations Research, 9(5), 673-685.

Burki, T. (2022). COVID-19 vaccine mandates in Europe. The Lancet Infectious Diseases, 22(1), 27-28.

Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 729, 138817.

Dong, Y., Dai, T., Wei, Y., Zhang, L., Zheng, M., & Zhou, F. (2020). A systematic review of SARS-CoV-2 vaccine candidates. Signal transduction and targeted therapy, 5(1), 1-14.

Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23(4), 586-594.

Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1847979018808673.

Forni, G., & Mantovani, A. (2021). COVID-19 vaccines: where we stand and challenges ahead. Cell Death & Differentiation, 28(2), 626-639.

Gardner Jr, E. S. (1985). Exponential smoothing: The state of the art. Journal of forecasting, 4(1), 1-28.

Graeber, D., Schmidt-Petri, C., & Schröder, C. (2021). Attitudes on voluntary and mandatory vaccination against COVID-19: Evidence from Germany. PloS one, 16(5), e0248372.

Hansun, S. (2016). A new approach of brown’s double exponential smoothing method in time series analysis. Balkan Journal of Electrical and Computer Engineering, 4(2), 75-78.

Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & industrial engineering, 35(1-2), 213-216.

Hodgson, S. H., Mansatta, K., Mallett, G., Harris, V., Emary, K. R., & Pollard, A. J. (2021). What defines an efficacious COVID-19 vaccine? A review of the challenges assessing the clinical efficacy of vaccines against SARS-CoV-2. The lancet infectious diseases, 21(2), e26-e35.

Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.

Kaur, S. P., & Gupta, V. (2020). COVID-19 Vaccine: A comprehensive status report. Virus research, 288, 198114.

Khubchandani, J., Sharma, S., Price, J. H., Wiblishauser, M. J., Sharma, M., & Webb, F. J. (2021). COVID-19 vaccination hesitancy in the United States: a rapid national assessment. Journal of community health, 46(2), 270-277.

Konarasinghe, K. M. U. B. (2020). Modeling COVID-19 epidemic of USA, UK and Russia. Journal of New Frontiers in Healthcare and Biological Sciences, 1(1), 1-14.

Leask, J., Seale, H., Williams, J. H., Kaufman, J., Wiley, K., Mahimbo, A., ... & Attwell, K. (2021). Policy considerations for mandatory COVID‐19 vaccination from the Collaboration on Social Science and Immunisation. Medical Journal of Australia, 215(11), 499-503.

Loembé, M. M., & Nkengasong, J. N. (2021). COVID-19 vaccine access in Africa: Global distribution, vaccine platforms, and challenges ahead. Immunity, 54(7), 1353-1362.

Mattos, A. M. D., Costa, I. Z. K., Neto, M., Rafael, R. D. M. R., Carvalho, E. C., & Porto, F. (2021). Fake News in times of COVID-19 and its legal treatment in Brazilian law. Escola Anna Nery, 25.

Mendelson, M., Venter, F., Moshabela, M., Gray, G., Blumberg, L., de Oliveira, T., & Madhi, S. A. (2021). The political theatre of the UK's travel ban on South Africa. The Lancet, 398(10318), 2211-2213.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.

Ortega, F., & Orsini, M. (2020). Governing COVID-19 without government in Brazil: Ignorance, neoliberal authoritarianism, and the collapse of public health leadership. Global public health, 15(9), 1257-1277.

Pan, W. (2001). Akaike's information criterion in generalized estimating equations. Biometrics, 57(1), 120-125.

Phillips, G. B. (1978). Sex hormones, risk factors and cardiovascular disease. The American journal of medicine, 65(1), 7-11.

Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., ... & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our world in data.

Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., ... & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our world in data.

Rubin, R. (2021). COVID-19 vaccines vs variants—determining how much immunity is enough. Jama, 325(13), 1241-1243.

Sahai, A. K., Rath, N., Sood, V., & Singh, M. P. (2020). ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1419-1427.

Sampford, M. R. (1978). Sampling Techniques.

Singh, S., Parmar, K. S., Kumar, J., & Makkhan, S. J. S. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos, Solitons & Fractals, 135, 109866.

Soares, P., Rocha, J. V., Moniz, M., Gama, A., Laires, P. A., Pedro, A. R., ... & Nunes, C. (2021). Factors associated with COVID-19 vaccine hesitancy. Vaccines, 9(3), 300.

Spector, P. E., Fox, S., Penney, L. M., Bruursema, K., Goh, A., & Kessler, S. (2006). The dimensionality of counterproductivity: Are all counterproductive behaviors created equal?. Journal of vocational behavior, 68(3), 446-460.

Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International journal of Forecasting, 19(4), 715-725.

Uddin, M. N., & Roni, M. A. (2021). Challenges of storage and stability of mRNA-based COVID-19 vaccines. Vaccines, 9(9), 1033.

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.

Wang, M., Wu, Q., Xu, W., Qiao, B., Wang, J., Zheng, H., ... & Li, Y. (2020). Clinical diagnosis of 8274 samples with 2019-novel coronavirus in Wuhan. MedRxiv.

WHO, Immunisation coverage. July 15, 2020. https://www.who.int/ news-room/fact-sheets/detail/immunization-coverage, (2021).

Yonar, H., Yonar, A., Tekindal, M. A., & Tekindal, M. (2020). Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods. EJMO, 4(2), 160-165.

Zhang, J. J., Dong, X., Cao, Y. Y., Yuan, Y. D., Yang, Y. B., Yan, Y. Q., ... & Gao, Y. D. (2020). Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China. Allergy, 75(7), 1730-1741.

Downloads

Publicado

19/08/2022

Como Citar

COBRE, A. de F.; STREMEL, D. P.; BÖGER, B.; FACHI, M. M.; BORBA , H. H. L.; TONIN, F. . S. .; PONTAROLO, R. O impacto da rejeição da vacina COVID-19 na admissão hospitalar e a disseminação das variantes pelo mundo: implicações para a política de saúde. Research, Society and Development, [S. l.], v. 11, n. 11, p. e189111133435, 2022. DOI: 10.33448/rsd-v11i11.33435. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/33435. Acesso em: 18 jul. 2024.

Edição

Seção

Ciências da Saúde