Article: Study of novel COVID-19 data using graph energy centrality: a soft computing approach Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2022 Vol.14 No.3 pp.282 - 294 Abstract: The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A social network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse social networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behaviour. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using graph energy centrality. This measure of centrality allows us to recognise the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel_Corona_Virus_2019_Dataset from Johns Hopkins University to analyse epidemiological data around the world. Inderscience Publishers - linking academia, business and industry through research

Title: Study of novel COVID-19 data using graph energy centrality: a soft computing approach

Authors: S. Mahadevi; Shyam S. Kamath; D. Pushparaj Shetty

Addresses: Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, Mangalore, India ' Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, Mangalore, India ' Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, Mangalore, India

Abstract: The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A social network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse social networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behaviour. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using graph energy centrality. This measure of centrality allows us to recognise the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel_Corona_Virus_2019_Dataset from Johns Hopkins University to analyse epidemiological data around the world.

Keywords: coronavirus; SARS-CoV-2; centrality measures; graph energy; data analysis; visualisation; social network analysis.

DOI: 10.1504/IJMEI.2022.122287

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.3, pp.282 - 294

Received: 13 Apr 2020
Accepted: 12 Aug 2020
Published online: 19 Apr 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Study of novel COVID-19 data using graph energy centrality: a soft computing approach

Keep up-to-date