  COVID-19 as a global pandemic causes a massive disruption to social stability
that threatens human life and the economy. Policymakers and all elements of
society must deliver measurable actions based on the pandemic's severity to
minimize the detrimental impact of COVID-19. A proper forecasting system is
arguably important to provide an early signal of the risk of COVID-19 infection
so that the authorities are ready to protect the people from the worst.
However, making a good forecasting model for infection risks in different
cities or regions is not an easy task, because it has a lot of influential
factors that are difficult to be identified manually. To address the current
limitations, we propose a deep graph learning model, called PANDORA, to predict
the infection risks of COVID-19, by considering all essential factors and
integrating them into a geographical network. The framework uses geographical
position relations and transportation frequency as higher-order structural
properties formulated by higher-order network structures (i.e., network
motifs). Moreover, four significant node attributes (i.e., multiple features of
a particular area, including climate, medical condition, economy, and human
mobility) are also considered. We propose three different aggregators to better
aggregate node attributes and structural features, namely, Hadamard, Summation,
and Connection. Experimental results over real data show that PANDORA
outperforms the baseline method with higher accuracy and faster convergence
speed, no matter which aggregator is chosen. We believe that PANDORA using deep
graph learning provides a promising approach to get superior performance in
infection risk level forecasting and help humans battle the COVID-19 crisis.
