Controlling COVID-19 transmission with isolation of influential nodes

To understand the transmission dynamics of any infectious disease outbreak, identification of influential nodes plays a crucial role in a complex network. In most infectious disease outbreaks, activities of some key nodes can trigger rapid disease transmission in the population. Identification and i...

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Main Authors: Chaharborj, Sarkhosh Seddighi, Nabi, Khondoker Nazmoon, Koo, Lee Feng, Chaharborj, Shahriar Seddighi, Pei, See Phang
Format: Article
Published: Elsevier 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100806/
https://www.sciencedirect.com/science/article/pii/S0960077922002454?via%3Dihub
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Institution: Universiti Putra Malaysia
id my.upm.eprints.100806
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spelling my.upm.eprints.1008062023-08-23T03:37:46Z http://psasir.upm.edu.my/id/eprint/100806/ Controlling COVID-19 transmission with isolation of influential nodes Chaharborj, Sarkhosh Seddighi Nabi, Khondoker Nazmoon Koo, Lee Feng Chaharborj, Shahriar Seddighi Pei, See Phang To understand the transmission dynamics of any infectious disease outbreak, identification of influential nodes plays a crucial role in a complex network. In most infectious disease outbreaks, activities of some key nodes can trigger rapid disease transmission in the population. Identification and immediate isolation of those influential nodes can impede the disease transmission effectively. In this paper, the technique for order of preference by similarity to ideal solution (TOPSIS) method with a novel formula has been proposed to detect the influential and top ranked nodes in a complex social network, which involves analyzing and studying of structural organization of a network. In the proposed TOPSIS method, several centrality measures have been used as multi-attributes of a complex social network. A new formula has been designed for calculating the transmission probability of an epidemic disease to identify the impact of isolating influential nodes. To verify the robustness of the proposed method, we present a comprehensive comparison with five node-ranking methods, which are being used currently for assessing the importance of nodes. The key nodes can be considered as a person, community, cluster or a particular area. The Susceptible-infected-recovered (SIR) epidemic model is exploited in two real networks to examine the spreading ability of the nodes, and the results illustrate the effectiveness of the proposed method. Our findings have unearthed that quarantine or isolation of influential nodes following proper health protocols can play a pivotal role in curbing the transmission rate of COVID-19. Elsevier 2022-06 Article PeerReviewed Chaharborj, Sarkhosh Seddighi and Nabi, Khondoker Nazmoon and Koo, Lee Feng and Chaharborj, Shahriar Seddighi and Pei, See Phang (2022) Controlling COVID-19 transmission with isolation of influential nodes. Chaos, Solitons & Fractals, 159. art. no. 112035. pp. 1-11. ISSN 0960-0779; ESSN: 1873-2887 https://www.sciencedirect.com/science/article/pii/S0960077922002454?via%3Dihub 10.1016/j.chaos.2022.112035
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description To understand the transmission dynamics of any infectious disease outbreak, identification of influential nodes plays a crucial role in a complex network. In most infectious disease outbreaks, activities of some key nodes can trigger rapid disease transmission in the population. Identification and immediate isolation of those influential nodes can impede the disease transmission effectively. In this paper, the technique for order of preference by similarity to ideal solution (TOPSIS) method with a novel formula has been proposed to detect the influential and top ranked nodes in a complex social network, which involves analyzing and studying of structural organization of a network. In the proposed TOPSIS method, several centrality measures have been used as multi-attributes of a complex social network. A new formula has been designed for calculating the transmission probability of an epidemic disease to identify the impact of isolating influential nodes. To verify the robustness of the proposed method, we present a comprehensive comparison with five node-ranking methods, which are being used currently for assessing the importance of nodes. The key nodes can be considered as a person, community, cluster or a particular area. The Susceptible-infected-recovered (SIR) epidemic model is exploited in two real networks to examine the spreading ability of the nodes, and the results illustrate the effectiveness of the proposed method. Our findings have unearthed that quarantine or isolation of influential nodes following proper health protocols can play a pivotal role in curbing the transmission rate of COVID-19.
format Article
author Chaharborj, Sarkhosh Seddighi
Nabi, Khondoker Nazmoon
Koo, Lee Feng
Chaharborj, Shahriar Seddighi
Pei, See Phang
spellingShingle Chaharborj, Sarkhosh Seddighi
Nabi, Khondoker Nazmoon
Koo, Lee Feng
Chaharborj, Shahriar Seddighi
Pei, See Phang
Controlling COVID-19 transmission with isolation of influential nodes
author_facet Chaharborj, Sarkhosh Seddighi
Nabi, Khondoker Nazmoon
Koo, Lee Feng
Chaharborj, Shahriar Seddighi
Pei, See Phang
author_sort Chaharborj, Sarkhosh Seddighi
title Controlling COVID-19 transmission with isolation of influential nodes
title_short Controlling COVID-19 transmission with isolation of influential nodes
title_full Controlling COVID-19 transmission with isolation of influential nodes
title_fullStr Controlling COVID-19 transmission with isolation of influential nodes
title_full_unstemmed Controlling COVID-19 transmission with isolation of influential nodes
title_sort controlling covid-19 transmission with isolation of influential nodes
publisher Elsevier
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100806/
https://www.sciencedirect.com/science/article/pii/S0960077922002454?via%3Dihub
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