Mobility Networks and GNNs in Forecasting COVID-19 Cases in Brazil and their Relationship with Socioeconomic Factors
Palabras clave:
COVID-19, Forecasting, Mobility Networks, Graph Neural Networks, Socioeconomic FactorsResumen
This summary explores predicting COVID-19 case time series in Brazil using GCN (Graph Convolutional Network) based models, a type of Graph Neural Networks (GNN), along with mobility networks. Individual city predictions are made by incorporating city-specific time series data and leveraging subgraphs derived from the connections in the mobility network to evaluate the temporal COVID-19 data. Additionally, the study employs two other models dedicated solely to time series prediction: Prophet and Long Short-Term Memory (LSTM). The Root Mean Square Error (RMSE) values of COVID-19 forecast models applied in the Brazilian context are summarized, showing the performance and variability of different models.
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