Forecasting the COVID-19 Space-time Dynamics in Brazil with Convolutional Graph Neural Networks and Transport Modals


  • Lucas Caldeira de Oliveira Universidade Tecnológica Federal do Paraná
  • Marcelo Teixeira Universidade Tecnológica Federal do Paraná
  • Dalcimar Casanova Universidade Tecnológica Federal do Paraná


Covid-19, Graph Convolutional Network, Forecasting, Deep Learning


This study presents a novel scalable method to forecast the numbers of cases and deaths by SARS-CoV-2 according to the influence that certain (micro) regions exert on others, predicting for specific regions while generalizing for general extents. By exploiting graph convolutional networks with recurrent networks, our approach maps the main access routes to municipalities in Brazil using the modals of transport, and processes this information via neural network algorithms. We compared the performance in forecasting the pandemic daily numbers with three baseline models, with the forecasting horizon varying from 1 to 25 days. Results show that the proposed model overcomes the baselines, being specially suitable for forecasts from 14 to 24 days ahead.


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Como Citar

Caldeira de Oliveira, L., Teixeira, M., & Casanova, D. (2022). Forecasting the COVID-19 Space-time Dynamics in Brazil with Convolutional Graph Neural Networks and Transport Modals. Revista Eletrônica De Iniciação Científica Em Computação, 20(3). Recuperado de



Edição Especial: CTIC/CSBC