Reviewed by Lexie CornerOct 25 2024
A team of engineers from the University of Houston has published a study in Nature examining the role of international air travel in the global spread of COVID-19. Using a newly developed AI tool, the researchers identified infection hotspots linked to air traffic, pinpointing key areas that significantly contribute to disease transmission.
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The analysis revealed that Western Europe, the Middle East, and North America are the primary regions contributing to the pandemic. This is attributed to the high volume of outgoing international flights originating or transiting through these locations.
Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policymakers for making informed decisions regarding air traffic restrictions during future outbreaks.
Hien Van Nguyen, Lead Researcher and Associate Professor, Electrical And Computer Engineering, University of Houston
The Tools
Nguyen and his team created a computer program named Dynamic Weighted GraphSAGE, designed to analyze large networks of continuously changing data, such as flight schedules, to identify patterns and trends.
It looks at spatiotemporal graphs, or how things are linked across both space (different locations) and time, to better understand how this affects things like the spread of diseases or transportation patterns.
Hien Van Nguyen, Lead Researcher and Associate Professor, Electrical and Computer Engineering, University of Houston
To investigate the impact of air travel on the spread of infections, Van Nguyen and graduate students Akash Awasthi and Syed Rizvi conducted a perturbation analysis to assess the sensitivity of their model to various factors and examined flight connections between different regions and countries.
This approach allowed the researchers to identify which aspects of air traffic most strongly impact the virus's spread and to determine which flight reductions in critical areas could effectively reduce projected global case numbers.
The Strategies
We propose air traffic reduction strategies that can significantly impact controlling the pandemic with minimal disruption to human mobility. Policies including stringent reduction in the number of Western European flights are predicted to cause larger reductions in global COVID-19 cases. This work represents a novel usage of perturbation analysis on spatiotemporal graph neural networks to gain insight on pandemic forecasting.
Hien Van Nguyen, Lead Researcher and Associate Professor, Electrical and Computer Engineering, University of Houston
Nguyen stated that while the findings are based on the context of COVID-19, the insights gained are applicable to any pandemic.
Additional researchers involved in the project are from the Houston Methodist Research Institute.
Journal Reference:
Rizvi, S., (2024) Deep learning-derived optimal aviation strategies to control pandemics. Scientific Reports. doi.org/10.1038/s41598-024-73639-7.