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Deep learning-derived optimal aviation strategies to control pandemics
Syed Rizvi1, Akash Awasthi2, Maria J Peláez3
1Department of Computer Science, Yale University, New Haven, CT, 06511, USA.
Scientific Reports
|October 2, 2024
Summary
International flights significantly spread COVID-19 globally. A graph neural network model identified key regions and proposed air traffic strategies to control the pandemic with minimal disruption.
Area of Science:
- Epidemiology
- Network Science
- Artificial Intelligence
Background:
- The COVID-19 pandemic necessitated global public health interventions, leading to economic consequences.
- Understanding the role of human mobility, particularly international air travel, is crucial for pandemic control.
Purpose of the Study:
- To investigate the impact of international commercial flights on COVID-19 infection dynamics.
- To develop a deep learning framework for analyzing spatiotemporal disease spread influenced by air traffic.
Main Methods:
- Developed a graph neural network (GNN) framework, Dynamic Weighted GraphSAGE (DWSAGE), for spatiotemporal graph analysis.
- Utilized daily updated flight data and conducted local sensitivity analysis through perturbation experiments.
Main Results:
- Identified Western Europe, the Middle East, and North America as major contributors to global pandemic spread due to high air traffic.
- Demonstrated the model's capability to learn relationships between air traffic and infection spread.
Conclusions:
- Air traffic reduction strategies in key regions can significantly control the pandemic.
- The DWSAGE framework offers a valuable tool for policymakers to manage future outbreaks through informed air traffic restrictions.
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