A Deep Neural Network Model for SARS-CoV-2 Pandemic Forecasting in Brazil
DOI:
https://doi.org/10.31416/rsdv.v9i1.21Keywords:
Deep Neural Networks, Time Series, Prediction, COVID-19Abstract
The World Health Organization has declared coronavirus disease 2019 (COVID-19) as an unparalleled pandemic in a hundred years. The etiological agent of COVID-19 is the new severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), which has caused complications of different severity in the human respiratory system, and hence, overburdening global health systems due to excessive demand for hospitalizations in intensive care units. In this way, due to the absence of effective drugs and licensed vaccines to wrestle against the COVID-19 and its variants, quarantine measures and social distancing have been employed in an attempt to slow the accelerated spread of COVID-19. However, it caused a break in numerous economic activities. In this scenario, predicting the pandemic’s dynamic is essential to guide the strategy to deal simultaneously with the rise in the demand for health support and effects on the economy. Therefore, this work presents a deep neural network model designed by a gradient-based learning process to predict the spread of COVID-19, using a time series-based approach. In order to assess the model’s prediction performance, we use COVID-19 time series, with daily frequency, in Brazil. Achieved results show effectiveness, in terms of predictive performance, of the proposed model to estimate the COVID-19 pandemic’s dynamics.
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