Integrating LSTMs and GNNs for COVID-19 Forecasting
This work addresses pandemic forecasting for policy-making, but it is incremental as it builds on existing methods for graph time series models.
The authors tackled COVID-19 forecasting by integrating LSTMs and GNNs with a skip connection to capture spatial and temporal patterns, achieving superior performance on daily new cases data from 37 European nations over 472 days based on mean absolute scaled error.
The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short Term Memory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE). This area of research has important applications to policy-making and we analyze its potential for pandemic resource control.