IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5326-d1099821.html
   My bibliography  Save this article

Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques

Author

Listed:
  • Rauf Ahmed Shams Malick

    (FAST School of Computing, National University Computer and Emerging Sciences, Karachi 54700, Pakistan)

  • Syed Kashir Hasan

    (FAST School of Computing, National University Computer and Emerging Sciences, Karachi 54700, Pakistan)

  • Fahad Samad

    (FAST School of Computing, National University Computer and Emerging Sciences, Karachi 54700, Pakistan)

  • Nadeem Kafi Khan

    (FAST School of Computing, National University Computer and Emerging Sciences, Karachi 54700, Pakistan)

  • Hassan Jamil Syed

    (Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
    Cyber Security Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia)

Abstract

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus’s spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

Suggested Citation

  • Rauf Ahmed Shams Malick & Syed Kashir Hasan & Fahad Samad & Nadeem Kafi Khan & Hassan Jamil Syed, 2023. "Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5326-:d:1099821
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5326/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Bedoya-Maya, Felipe & Calatayud, Agustina & Giraldez, Francisca & Sánchez González, Santiago, 2022. "Urban mobility patterns and the spatial distribution of infections in Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 43-54.
    3. Sarah F Poole & Jessica Gronsbell & Dale Winter & Stefanie Nickels & Roie Levy & Bin Fu & Maximilien Burq & Sohrab Saeb & Matthew D Edwards & Michael K Behr & Vignesh Kumaresan & Alexander R Macalalad, 2021. "A holistic approach for suppression of COVID-19 spread in workplaces and universities," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-12, August.
    4. Fabio Milner & Ruijun Zhao, 2008. "S-I-R Model with Directed Spatial Diffusion," Mathematical Population Studies, Taylor & Francis Journals, vol. 15(3), pages 160-181.
    5. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    6. Julie Fournet & Alain Barrat, 2014. "Contact Patterns among High School Students," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-17, September.
    7. Yutaka Okabe & Akira Shudo, 2021. "Microscopic Numerical Simulations of Epidemic Models on Networks," Mathematics, MDPI, vol. 9(9), pages 1-19, April.
    8. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    9. Zhou, Yirong & Liu, Xiaoyue Cathy & Grubesic, Tony, 2021. "Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit," Journal of Transport Geography, Elsevier, vol. 97(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fatma Lestari & Margaret Cook & Kelly Johnstone & Miranda Surya Wardhany & Robiana Modjo & Baiduri Widanarko & Devie Fitri Octaviani, 2022. "COVID-19 in the Workplace in Indonesia," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Wang, Richard & Ye, Zhongnan & Lu, Miaojia & Hsu, Shu-Chien, 2022. "Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling," Applied Energy, Elsevier, vol. 322(C).
    4. Shahadat Uddin & Arif Khan & Haohui Lu & Fangyu Zhou & Shakir Karim, 2022. "Suburban Road Networks to Explore COVID-19 Vulnerability and Severity," IJERPH, MDPI, vol. 19(4), pages 1-9, February.
    5. Jing Liu & Huapu Lu & Mingyu Chen & Jianyu Wang & Ying Zhang, 2020. "Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    6. Oude Groeniger, Joost & Noordzij, Kjell & van der Waal, Jeroen & de Koster, Willem, 2021. "Dutch COVID-19 lockdown measures increased trust in government and trust in science: A difference-in-differences analysis," Social Science & Medicine, Elsevier, vol. 275(C).
    7. 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).
    8. Almoghathawi, Yasser & Barker, Kash & Rocco, Claudio M. & Nicholson, Charles D., 2017. "A multi-criteria decision analysis approach for importance identification and ranking of network components," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 142-151.
    9. Valerio Basile & Francesco Cauteruccio & Giorgio Terracina, 2021. "How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit," Future Internet, MDPI, vol. 13(2), pages 1-32, January.
    10. Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar, 2022. "Spatiotemporal evolving patterns of bike-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    11. Viktoriia Shubina & Sylvia Holcer & Michael Gould & Elena Simona Lohan, 2020. "Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era," Data, MDPI, vol. 5(4), pages 1-40, September.
    12. Khan, Syed Abdul Rehman & Razzaq, Asif & Yu, Zhang & Shah, Adeel & Sharif, Arshian & Janjua, Laeeq, 2022. "Disruption in food supply chain and undernourishment challenges: An empirical study in the context of Asian countries," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    13. Jay Joseph van Bavel & Aleksandra Cichocka & Valerio Capraro & Hallgeir Sjåstad & John Nezlek & Tomislav Pavlović & Mark Alfano & Michele Gelfand & Flavio Azevedo & Michèle Birtel & Aleksandra Cislak , 2022. "National identity predicts public health support during a global pandemic: Results from 67 nations," Post-Print hal-03543504, HAL.
    14. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
    15. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    16. Agha Mohammad Ali Kermani, Mehrdad & Fatemi Ardestani, Seyed Farshad & Aliahmadi, Alireza & Barzinpour, Farnaz, 2017. "A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 570-582.
    17. Zhang, Qiang & Pu, Shunhao & Luo, Lihua & Liu, Zhichao & Xu, Jie, 2022. "Revisiting important ports in container shipping networks: A structural hole-based approach," Transport Policy, Elsevier, vol. 126(C), pages 239-248.
    18. Zhang, Qi & Luo, Chuanhai & Li, Meizhu & Deng, Yong & Mahadevan, Sankaran, 2015. "Tsallis information dimension of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 707-717.
    19. Chenghe Guan & Junjie Tan & Brian Hall & Chao Liu & Ying Li & Zhichang Cai, 2022. "The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    20. Mattia Mazzoli & Riccardo Gallotti & Filippo Privitera & Pere Colet & José J. Ramasco, 2023. "Spatial immunization to abate disease spreading in transportation hubs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5326-:d:1099821. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.