Modelagem preditiva de surtos epidêmicos usando redes neurais LSTM: uma aplicação para a covid-19

• Ano de defesa: 2024
• Autor(a) principal: Fontoura, Marco Antonio Russi
• Orientador(a): Schimit, Pedro Henrique Triguis
• Banca de defesa: Schimit, Pedro Henrique Triguis , Monteiro, Luiz Henrique Alves , Pereira, Fabio Henrique
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação em Informática e Gestão do Conhecimento
• Departamento: Informática
• País: Brasil
• Palavras-chave em Português: análise de séries temporais; aprendizado de máquina; COVID-19; modelagem epidemiológica; redes LSTM
• Palavras-chave em Inglês: COVID-19 forecasting; epidemiological modelling; LSTM networks; machine learning; time series analysis
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/3620

The COVID-19 pandemic, in its initial three years, resulted in a time series that was difficult to predict due to public policy actions and the emergence of new variants. Mobility restrictions, new variants, vaccines, and cultural differences led to distinct stages of virus spread, making it challenging for a single epidemiological prediction model to forecast the entire time series, thus necessitating different models. Based on the application of neural networks in epidemiological studies for time series prediction, this work aims to develop a methodology based on a multi-layer Long Short-Term Memory (LSTM) neural network, capable of being applied to nearly three years of pandemic data to predict the number of new daily cases of the disease. The methodology applies a series of tests, combining different network input data, such as daily cases and vaccinations to predict the number of cases for several days into the future. The experiments were conducted for Brazil and other countries, showing good results for predictions up to fifteen days in advance, with the potential to identify trend changes in the daily case timeline. This capability is useful for detecting the onset of new waves of infection, contributing to public health alert systems and immediate decision-making.