Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing
COVID-19 data from these countries together is a prominent challenge. Under the
sponsorship of NSF REU, this paper describes our experience with a ten-week project that
aims to guide an REU scholar to develop a physics-guided graph attention network to
predict the global COVID- 19 Pandemics. We mainly presented the preparation,
implementation, and dissemination of the addressed project. The COVID-19 situation in a
country could be dramatically different from that of others, which suggests that COVID-19
pandemic data are generated based on different mechanisms, making COVID-19 data in
different countries follow different probability distributions. Learning more than one
hundred underlying probability distributions for countries in the world from large scale
COVID- 19 data is beyond a single machine learning model. To address this challenge, we
proposed two team-learning frameworks for predicting the COVID-19 pandemic trends:
peer learning and layered ensemble learning framework. This addressed framework assigns
an adaptive physics-guided graph attention network (GAT) to each learning agent. All the
learning agents are fabricated in a hierarchical architecture, which enables agents to
collaborate with each other in peer-to-peer and cross-layer way. This layered architecture
shares the burden of large-scale data processing on machine learning models of all units.
Experiments are run to verify the effectiveness of our approaches. The results indicate the
proposed ensemble outperforms baseline methods. Besides being documented on GitHub,
this work has resulted in two journal papers.
