  The recent outbreak of COVID-19 has affected millions of individuals around
the world and has posed a significant challenge to global healthcare. From the
early days of the pandemic, it became clear that it is highly contagious and
that human mobility contributes significantly to its spread. In this paper, we
study the impact of population movement on the spread of COVID-19, and we
capitalize on recent advances in the field of representation learning on graphs
to capture the underlying dynamics. Specifically, we create a graph where nodes
correspond to a country's regions and the edge weights denote human mobility
from one region to another. Then, we employ graph neural networks to predict
the number of future cases, encoding the underlying diffusion patterns that
govern the spread into our learning model. Furthermore, to account for the
limited amount of training data, we capitalize on the pandemic's asynchronous
outbreaks across countries and use a model-agnostic meta-learning based method
to transfer knowledge from one country's model to another's. We compare the
proposed approach against simple baselines and more traditional forecasting
techniques in 3 European countries. Experimental results demonstrate the
superiority of our method, highlighting the usefulness of GNNs in
epidemiological prediction. Transfer learning provides the best model,
highlighting its potential to improve the accuracy of the predictions in case
of secondary waves, if data from past/parallel outbreaks is utilized.
