  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.
