Time Series Forecasting of COVID-19 Cases in Brazil with GNN and Mobility Networks

• Fernando Henrique Oliveira Duarte UFOP https://orcid.org/0009-0003-0228-0712
• Gladston J. P. Moreira UFOP https://orcid.org/0000-0001-7747-5926
• Eduardo J. S. Luz UFOP https://orcid.org/0000-0001-5249-1559
• Leonardo B. L. Santos Cemaden https://orcid.org/0000-0002-3129-772X
• Vander L. S. Freitas UFOP https://orcid.org/0000-0001-7989-0816

Resumo

In this study, we examine the impact of human mobility on the transmission of COVID-19, a highly contagious disease that has rapidly spread worldwide. To investigate this, we construct a mobility network that captures movement patterns between Brazilian cities and integrate it with time series data of COVID-19 infection records. Our approach considers the interplay between people’s movements and the spread of the virus. We employ two neural networks based on Graph Convolutional Network (GCN), which leverage spatial and temporal data inputs, to predict time series at each city while accounting for the influence of neighboring cities. In comparison, we evaluate LSTM and Prophet models that do not capture time series dependencies. By utilizing RMSE (Root Mean Square Error), we quantify the discrepancy between the actual number of COVID-19 cases and the predicted number of cases by the model among the models. Prophet achieves the best average RMSE of 482.95 with a minimum of 1.49, while LSTM performs the least despite having a low minimum RMSE. The GCRN and GCLSTM models exhibit mean RMSE error values of 3059.5 and 3583.88, respectively, with the lowest standard deviation values for RMSE errors at 500.39 and 452.59. Although the Prophet model demonstrates superior performance, its maximum RMSE value of 52,058.21 is ten times higher than the highest value observed in the Graph Convolutional Networks (GCNs) models. Based on our findings, we conclude that GCNs models yield more stable results compared to the evaluated models.