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Controlling COVID-19 transmission with isolation of influential nodes

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  • Chaharborj, Sarkhosh Seddighi
  • Nabi, Khondoker Nazmoon
  • Feng, Koo Lee
  • Chaharborj, Shahriar Seddighi
  • Phang, Pei See

Abstract

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.

Suggested Citation

  • Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:chsofr:v:159:y:2022:i:c:s0960077922002454
    DOI: 10.1016/j.chaos.2022.112035
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    1. Sheng, Jinfang & Dai, Jinying & Wang, Bin & Duan, Guihua & Long, Jun & Zhang, Junkai & Guan, Kerong & Hu, Sheng & Chen, Long & Guan, Wanghao, 2020. "Identifying influential nodes in complex networks based on global and local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    3. Konstantinos Demertzis & Dimitrios Tsiotas & Lykourgos Magafas, 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
    4. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    5. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    6. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    7. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    8. Nabi, Khondoker Nazmoon & Abboubakar, Hamadjam & Kumar, Pushpendra, 2020. "Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    9. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    10. Nabi, Khondoker Nazmoon & Kumar, Pushpendra & Erturk, Vedat Suat, 2021. "Projections and fractional dynamics of COVID-19 with optimal control strategies," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    11. Yuanzhi Yang & Lei Yu & Xing Wang & Siyi Chen & You Chen & Yipeng Zhou, 2020. "A novel method to identify influential nodes in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-14, February.
    12. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    13. Yakup Çelikbilek & Fatih Tüysüz, 2020. "An in-depth review of theory of the TOPSIS method: An experimental analysis," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 281-300, April.
    14. Nabi, Khondoker Nazmoon, 2020. "Forecasting COVID-19 pandemic: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    15. Yang, Xu-Hua & Xiong, Zhen & Ma, Fangnan & Chen, Xiaoze & Ruan, Zhongyuan & Jiang, Peng & Xu, Xinli, 2021. "Identifying influential spreaders in complex networks based on network embedding and node local centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    16. Ye, Ye & Hang, Xiao Rong & Koh, Jin Ming & Miszczak, Jarosław Adam & Cheong, Kang Hao & Xie, Neng-gang, 2020. "Passive network evolution promotes group welfare in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    17. Lv, Zhiwei & Zhao, Nan & Xiong, Fei & Chen, Nan, 2019. "A novel measure of identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 488-497.
    18. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
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