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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.
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Future of COVID-19 Treatment Without Vaccine and Painful Needles
Ever since the beginning of COVID-19 Pandemic, researchers from across the world have been investigating different options for the treatment of SARS-COV-2. This is critical even after two years from the start of pandemic due to different variants resulting in new waves of this virus spreading globally. In this regard, December 22 and 23rd, 2021 are landmark days, when FDA granted authorization to Pfizer’s Paxlovid and Merck’s Molnupiravir (COVID-19 Oral Antiviral products) for emergency use (EUA). The director of the FDA’s Center for Drug Evaluation and Research Patrizia Cavazzoni, M.D., described this an enormous step to tackle this global pandemic
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- Award ID(s):
- 2028612
- PAR ID:
- 10443605
- Date Published:
- Journal Name:
- International Supply Chain Technology Journal
- Volume:
- 8
- Issue:
- 5
- ISSN:
- 2380-5730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.20545/isctj.v08.i05.01
