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Covid-19 Case Forecasting Using Neural Network Algorithms (previsão De Casos De Covid-19 Utilizando Algoritmos De Redes Neurais)

Year: 2025

Type: article

Abstract: This paper explores the prediction of new COVID-19 cases using recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Data from the São Paulo State Data Analysis System (SEADE) was utilized. Additionally, a scalable cloud architecture using AWS was employed. The data underwent pre-processing techniques like scaling and moving average. Following processing, the models were evaluated using MAE (Mean Absolute Error) and R² metrics. Results indicate that the GRU model exhibited superior performance, likely attributed to its ability to efficiently handle low-dimensional data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15671163

Authors: Oliveira, Léo Igor Nunes de, Talarico, Americo, Miranda, Marise

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6893403201

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