COV19-Dijkstra: A COVID-19 Propagation Model Based on Dijkstra’s Algorithm
Arnaud Watusadisi Mavakala1, Wilfried Yves Hamilton Adoni2, Najib Ben Aoun3,4, Tarik Nahhal2, Moez Krichen3,5, Mohammed Y. Alzahrani3 & Franck Mutombo Kalala6 Show affiliations
- African Institute for Mathematical Sciences, Senegal
- LIMSAD Laboratory, Hassan II University of Casablanca, Morocco
- College of Computer Science and Information Technology, Al Baha University, Saudi Arabia
- REGIM-Lab: Research Groups in Intelligent Machines, University of Sfax, Tunisia
- ReDCAD Laboratory, University of Sfax, Tunisia
- University of Lubumbashi, Lubumbashi, Democratic Republic Of Congo
Journal of Computer Science, Volume 19, Issue 1, pp. 75–86 · doi.org/10.3844/jcssp.2023.75.86
Abstract
The presence of the coronavirus, known as COVID-19, has prompted several researchers to study the mode of spread and the different defense mechanisms of the virus. As a reminder, obtaining a vaccine, for which much research is being conducted around the world, is a long and expensive process and it is unlikely that the pandemic can be treated in time. In this article, we present a new way to assess and limit the spread of the virus while trying to answer the following important questions: How to use the shortest path algorithm in a graph to analyze and better understand the spread of the virus? How to use the predictive power of the graph using the shortest path algorithm to find the relationships of a person who might be most at risk? The designed algorithm simulates how the virus spreads and infects people through the graph. Since the size of the collected COVID-19 data can reach a large volume over time and speaking of the graph concept, the NOSQL database including Neo4j which is a graph oriented NOSQL database is used for data collection, storage and processing. To enable the design and optimization of virus defense systems, this study proposes a feasible approach to quantify and predict the danger of a virus infection within a community.
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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
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Mavakala, A. W., Adoni, W. Y. H., Ben Aoun, N., Nahhal, T., Krichen, M., Alzahrani, M. Y. & Kalala, F. M. (2023). COV19-Dijkstra: A COVID-19 Propagation Model Based on Dijkstra’s Algorithm. Journal of Computer Science, 19(1), 75–86. https://doi.org/10.3844/jcssp.2023.75.86
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