Dynamic adaptive spatio–temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space–time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting. However, in previous deep learning models for epidemic forecasting, spatial and temporal variations are captured separately. A unified model is developed to cover all spatio–temporal relations. However, this measure is insufficient for modelling the complex spatio–temporal relations of infectious disease transmission. A dynamic adaptive spatio–temporal graph network (DASTGN) is proposed based on attention mechanisms to improve prediction accuracy. In DASTGN, complex spatio–temporal relations are depicted by adaptively fusing the mixed space–time effects and dynamic space–time dependency structure. This dual-scale model considers the time-specific, space-specific, and direct effects of the propagation process at the fine-grained level. Furthermore, the model characterises impacts from various space–time neighbour blocks under time-varying interventions at the coarse-grained level. The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092% in the root mean-square error and 11.563% in the mean absolute error. Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19. The spatio–temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios. In conclusion, DASTGN has successfully captured the dynamic spatio–temporal variations of COVID-19, and considering multiple dynamic space–time relationships is essential in epidemic forecasting.