2023
DOI: 10.3844/jcssp.2023.75.86
|View full text |Cite
|
Sign up to set email alerts
|

COV19-Dijkstra: A COVID-19 Propagation Model Based on Dijkstra’s Algorithm

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 shorte… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 32 publications
0
0
0
Order By: Relevance
“…The authors also utilized several sources from the journal of computer science to strengthen their research. First, a study finding the shortest path for public transportation to solve single-source using Dijkstra's algorithm (Wongso et al, 2018) and calculate the most efficient route for COVID-19 transmission within extensive community networks, aiming to analyze and forecast the progression of the transmission chain (Mavakala et al, 2023).…”
Section: Related Workmentioning
confidence: 99%
“…The authors also utilized several sources from the journal of computer science to strengthen their research. First, a study finding the shortest path for public transportation to solve single-source using Dijkstra's algorithm (Wongso et al, 2018) and calculate the most efficient route for COVID-19 transmission within extensive community networks, aiming to analyze and forecast the progression of the transmission chain (Mavakala et al, 2023).…”
Section: Related Workmentioning
confidence: 99%
“…These applications have demonstrated highly encouraging outcomes in the evaluation of such images in this context (Lin et al, 2020 ; Azam et al, 2022 ). Radiological examinations of the chest X-ray are often performed, and huge datasets have been used extensively in research to train algorithms that combine Recurrent Neural Networks (RNNs) for text interpretation and Convolutional Neural Networks (CNNs) for image analysis (Mavakala et al, 2023 ). Research on nodule recognition, description, and classification in radiography and thoracic computed tomography (CT) is still ongoing.…”
Section: Introductionmentioning
confidence: 99%