Implementation of a time series forecasting model to estimate excess deaths in Brazil in 2020
DOI:
https://doi.org/10.59681/2175-4411.v16.2024.1003Palavras-chave:
Time series, Predictive model, Excess deaths, Underreporting of deaths by COVID-19Resumo
Goals: The aim of this paper is to understand the behavior of the Covid-19 pandemic on the national Brazilian scenario and describe how it affected the mortality rate. Methods: Implement a predictor model using ARIMA modeling concepts and data extracted from the Unified Health System database, in order to estimate the number of deaths caused by COVID-19 in Brazil during 2020. Results: COVID-19 is estimated to have contributed, on average, to a surplus of 713 daily deaths. Conclusion: Even considering the records of deaths by COVID-19 on the result of the prediction, it is observed that the combination is below the real curve, which indicates that there is underreporting of deaths caused by this disease during the year 2020 in Brazil.
Referências
PHELAN AL, KATZ R, GOSTIN LO. The novel coronavirus originating in Wuhan, China: challenges for global health governance. JAMA. 2020;323(8):709-10. DOI: https://doi.org/10.1001/jama.2020.1097
CUCINOTTA D, VANELLI M. WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis. 2020;91(1):157.
HALEEM A, JAVAID M, VAISHYA R. Effects of COVID-19 pandemic in daily life. Current medicine research and practice. 2020;10(2):78. DOI: https://doi.org/10.1016/j.cmrp.2020.03.011
Morato MM, Bastos SB, Cajueiro DO, Normey-Rico JE. An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil. Annual re- views in control. 2020;50:417-31. DOI: https://doi.org/10.1016/j.arcontrol.2020.07.001
Orellana JDY, da Cunha GM, Marrero L, Moreira RI, da Costa Leite I, Horta BL. Ex- cesso de mortes durante a pandemia de COVID-19: subnotificação e desigualdades regionais no Brasil. Cadernos de Saúde Pública. 2021;37(1):e00259120. DOI: https://doi.org/10.1590/0102-311x00259120
SHUMWAY RH, STOFFER DS. Time series analysis and its applications. New York: Springer; 2000. DOI: https://doi.org/10.1007/978-1-4757-3261-0
CHATFIELD C. The analysis of time series: an introduction. New York: Chapman and hall/CRC, Routledge, 7th edition; 2019.
Brownlee J. Introduction to time series forecasting with python: how to prepare data and develop models to predict the future. Machine Learning Mastery; 2017.
Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control. Wiley; 5th edition; 2015.
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Direitos de Autor (c) 2024 Lucas F. Mateus, Fabricio Ourique, Analucia Schiaffino Morales, Millena Nayara da Silva
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0.
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