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COVID-19 spread algorithm in the international airport network-DetArpds
Cesar Guevara1,2, Dennys Coronel2, Byron Eduardo Salazar Maldonado3
1DataLab, The Institute of Mathematical Sciences (ICMAT-CSIC), Madrid, Spain.
Peerj. Computer Science
|June 22, 2023
Summary
This study introduces a new algorithm, DetARPDS, to track COVID-19 spread through international airports. The algorithm achieved 93.46% accuracy in mapping disease transmission routes, improving detection and processing time.
Area of Science:
- Epidemiology
- Computational Science
- Public Health
Background:
- Air transport facilitated rapid global COVID-19 spread.
- Machine learning models have shown promise in predicting disease transmission.
- Understanding disease spread in airport networks is crucial for public health.
Purpose of the Study:
- To analyze COVID-19 transmission within the international airport network.
- To develop a computational algorithm (DetARPDS) for determining disease spread routes.
- To enhance disease prevention strategies in air travel.
Main Methods:
- Literature review of 80 papers on route generation algorithms (2017-2021).
- Analysis of international airport data and COVID-19 spread information (2020-2022).
- Development of the DetARPDS algorithm using graph theory, Euclidean distance, and Dijkstra's algorithm for route simulation.
Main Results:
- The DetARPDS algorithm accurately mapped COVID-19 transmission routes with 93.46% accuracy.
- The algorithm improved computational aspects like processing time.
- Identified airports with high infection concentration more effectively.
Conclusions:
- The DetARPDS algorithm is an effective tool for tracking airborne disease spread in airport networks.
- This approach offers significant improvements over existing methods for disease transmission analysis.
- Findings can inform public health policies and interventions for air travel.
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