Skip to main content

Social Network Analysis for the Identification of Key Spreaders During COVID-19

  • 639 Accesses

Part of the Studies in Computational Intelligence book series (SCI,volume 923)


Identifying key spreaders is regarded as one of the fundamental challenging areas in controlling the spread of infections caused due to deadly Coronavirus Disease 2019 (COVID-19). Identification of key spreaders greatly contributes towards the understanding of disease spreading mechanisms during pandemic. The resolution of this problem remains very useful for government agencies to make tactical and actionable plans to counter the rapid spread of COVID-19. Various researchers around the world are adopting computational techniques to recognize key spreaders efficiently. However, the development of such technologies poses a significant challenge and requires regular improvements over time. In this chapter, a method CovidKeySpreader has been proposed that integrates both local and global insight and adopts a random walk algorithm to determine key spreaders in a patient interaction network related to India’s different states. For implementation, a state-wise network is constructed where nodes represent an individual patient and edges show interaction link between them. Each node of the network is assigned to associated communities. Further, communities are analyzed by exploiting both node and community scores. Finally, a random walk algorithm is applied to the weighted network to iteratively rank nodes. The efficacy of the proposed method is established using Susceptible-Infected-Recovered (SIR) spreading model and simulate the process of spreading on networks. Experiments conducted on four state-wise networks. The evaluation metric shows that the key spreaders identified by our proposed algorithm are more significant in comparison to other basic centrality measures.


  • Social network analysis
  • Patient interaction network
  • Key spreaders
  • SIR model
  • COVID-19

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-8534-0_4
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-981-15-8534-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. WHO Timeline—COVID-19. (2020). Retrieved on June 25, 2020, from

  2. COVID-19 CORONAVIRUS PANDEMIC. (2020). Retrieved on June 25, 2020, from

  3. Heymann, D. L. (2020). Data sharing and outbreaks: Best practice exemplified. The Lancet, 395(10223), 469–470.

    Google Scholar 

  4. Yang, X., Yu, Y., Xu, J., Shu, H., Liu, L., Wu, Y., et al. (2020). Clinical course and outcomes of critically ill patients with sars-cov-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respiratory Medicine.

    Google Scholar 

  5. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020). Early transmission dynamics in Wuhan, china, of novel coronavirus—Infected pneumonia. New England Journal of Medicine.

    Google Scholar 

  6. COVID19-India API. (2020). Retrieved on June 25, 2020, from

  7. Countries where COVID-19 has spread (2020). Retrieved on June 25, 2020, from

  8. Sharma, D., & Surolia, A. (2013). Degree centrality. Encyclopedia of Systems Biology, Dubitzky W, 558.

    Google Scholar 

  9. Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Google Scholar 

  10. Shimbel, A. (1953). Structural parameters of communication networks. The Bulletin Mathematics and Biophysics, 15(4), 501–507.

    MathSciNet  CrossRef  Google Scholar 

  11. Shaw, M. E. Group structure and the behavior of individuals in small groups. Journal of Psychology, 38(1), 139–149.

    Google Scholar 

  12. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In International symposium on computer and information sciences (pp. 284–293). Springer.

    Google Scholar 

  13. Tong, H., Faloutsos, C., & Pan, J.-Y. (2008). Random walk with restart: Fast solutions and applications. Knowledge Information Systems, 14, 327–346.

    Google Scholar 

  14. Zhan, J., Guidibande, V., & Parsa, S. P. K. (2016). Identification of top-k influential communities in big networks. Journal of Big Data, 3(1), 16.

    CrossRef  Google Scholar 

  15. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. International Journal of Complex Systems, 1695(5), 1–9.

    Google Scholar 

  16. Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653.

    Google Scholar 

  17. Mao, C., & Xiao, W. (2018). A comprehensive algorithm for evaluating node influences in social networks based on preference analysis and random walk. Complexity.

    Google Scholar 

  18. COVID-19 pandemic in Maharashtra. (2020). Retrieved on June 25, 2020, from

  19. COVID-19 pandemic in Karnataka. (2020). Retrieved on June 25, 2020, from

  20. COVID-19 pandemic in Kerala. (2020). Retrieved on June 25, 2020, from

  21. COVID-19 pandemic in Delhi. (2020). Retrieved on June 25, 2020, from

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ahmad Kamal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Hasan, A., Kamal, A. (2021). Social Network Analysis for the Identification of Key Spreaders During COVID-19. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore.

Download citation