Um Modelo de Rede Neural Profunda para Previsão da Pandemia do SARS-COV-2 no Brasil
DOI:
https://doi.org/10.31416/rsdv.v9i1.21Palavras-chave:
Redes Neurais Profundas, Séries Temporais, Previsão, COVID-19Resumo
A Organização Mundial de Saúde declarou a doença do coronavírus 2019 (COVID-19) como uma pandemia sem precedentes nos tempos modernos. O agente etiológico da COVID-19 é um novo coronavírus conhecido como \emph{severe acute respiratory syndrome coronavirus 2} (SARS-Cov-2), que tem causado complicações de diferentes gravidades no sistema respiratório humano e, consequentemente, sobrecarregando os sistemas de saúde mundiais devido à demanda excessiva de internações em unidades de terapia intensiva. Desta forma, devido a ausência de remédios efetivos e vacinas licenciadas para combater a COVID-19 e suas variantes, medidas de quarentena e distaciamento social têm sido empregados na tentativa de retardar a disseminação acelerada da COVID-19. No entanto, tais medidas causaram uma forte retração em diversas atividades econômicas. Neste cenário, prever a dinâmica da pandemia é essencial para nortear a estratégia para lidar simultaneamente com o crescimento da demanda por suporte à saúde e os reflexos na economia. Portanto, este trabalho apresenta um modelo de rede neural profunda projetado por um processo de aprendizagem baseado em gradiente para prever o a disseminação da COVID-19, utilizando uma abordagem baseada em séries temporais. A fim de avaliar o desempenho preditivo do modelo, foram utilizadas séries temporais da COVID-19, com frequência diária, no Brasil. Os resultados obtidos mostram efetividade, em termos de desempenho preditivo, do modelo proposto para estimar a dinâmica da pandemia da COVID-19.
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