  In this work, we examine a novel forecasting approach for COVID-19 case
prediction that uses Graph Neural Networks and mobility data. In contrast to
existing time series forecasting models, the proposed approach learns from a
single large-scale spatio-temporal graph, where nodes represent the
region-level human mobility, spatial edges represent the human mobility based
inter-region connectivity, and temporal edges represent node features through
time. We evaluate this approach on the US county level COVID-19 dataset, and
demonstrate that the rich spatial and temporal information leveraged by the
graph neural network allows the model to learn complex dynamics. We show a 6%
reduction of RMSLE and an absolute Pearson Correlation improvement from 0.9978
to 0.998 compared to the best performing baseline models. This novel source of
information combined with graph based deep learning approaches can be a
powerful tool to understand the spread and evolution of COVID-19. We encourage
others to further develop a novel modeling paradigm for infectious disease
based on GNNs and high resolution mobility data.
