AUT Journal of Modeling and Simulation

AUT Journal of Modeling and Simulation

Graph Embedding-based Smart Vaccination Using Mobile Data

Document Type : Research Article

Authors
Saeed Jamshidiha
MohammadMohsen Jadidi
Iman Masroori
Pegah Moslemi
Abbas Mohammadi
Vahid Pourahamdi
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
10.22060/miscj.2021.20177.5251
Abstract
A novel smart vaccination method is proposed in this paper to distribute a limited number of vaccines among the people of a large community, such as a country, consisting of smaller communities like cities or provinces. The proposed method is comprised of two phases; A vaccine allocation phase and a targeted vaccination phase. In the first phase, the available vaccines are allocated to the communities based on demographics and the effectiveness of each type of vaccine. In the second phase, each community is modelled as a contact graph, and the vaccines available to the community are administered to the individuals whose vaccination has the greatest impact on breaking the chain of transmission. As a result of utilizing the Node2Vec graph embedding algorithm, the complexity of the proposed method increases linearly with the number of people in the community, as opposed to common centrality based methods, the complexities of which increase with the square or cube of the number of individuals. Furthermore, the proposed method can distribute multiple types of vaccines with different probabilities of effectiveness. The performance of the proposed method is comparable to the common centrality based vaccination methods, while its complexity is lower. The results of the simulation show a 20% decrease in the peak number of infected individuals.
Keywords
Smart Vaccination
Vaccine allocation
Targeted Vaccination
Node2Vec
SIR Model
Subjects
  1. World health organization website. (2020, October 5, 2020). Available: https://www.who.int
  2. Iranian Ministry of Health and Medical Education Website. (2020). Available: https://behdasht.gov.ir
  3.  N. Saeed, A. Bader, T. Y. Al-Naffouri, and M.-S. Alouini, “When wireless communication faces COVID-19:Combating the pandemic and saving the economy,” arXiv preprint arXiv:2005.06637, 2020.
  4. Sharma, G. Singh, R. Sharma, P. Jones, S. Kraus, and Y. K. Dwivedi, “Digital health innovation: exploring adoption of COVID-19 digital contact tracing apps,” IEEE Transactions on Engineering Management, 2020.
  5. M. Kissler, P. Klepac, M. Tang, A. J. Conlan, and J. R. Gog, “Sparking” The BBC Four Pandemic”: Leveraging citizen science and mobile phones to model the spread of disease,” bioRxiv, p. 479154, 2020.
  6. Klepac, S. Kissler, and J. Gog, “Contagion! the bbc four pandemic–the model behind the documentary,” Epidemics, vol. 24, pp. 49-59, 2018.
  7. afroj Moon and C. Scoglio, “Contact Tracing Evaluation for COVID-19 Transmission during the Reopening Phase in a Rural College Town,” medRxiv, 2020.
  8. Stehlé et al., “High-resolution measurements of faceto-face contact patterns in a primary school,” PloS one, vol. 6, no. 8, p. e23176, 2011.
  9. C. Ng, P. Spachos, and K. Plataniotis, “COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing,” arXiv preprint arXiv:2005.13754, 2020.
  10. J. Leith and S. Farrell, “Coronavirus contact tracing: Evaluating the potential of using bluetooth received signal strength for proximity detection,” ACM SIGCOMM Computer Communication Review, vol. 50, no. 4, pp. 66-74, 2020.
  11. Grekousis and Y. Liu, “Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review,” Sustainable cities and society, vol. 71, p. 102995, 2021.
  12. Lu, Y. Wen, and G. Cao, “Community detection in weighted networks: Algorithms and applications,” in 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2013, pp. 179-184: IEEE.
  13. Mones, A. Stopczynski, A. S. Pentland, N. Hupert, and S. Lehmann, “Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation study,” Journal of The Royal Society Interface, vol. 15, no. 138, p. 20170783, 2018.
  14. Sun, Z. Lu, X. Zhang, M. Salathé, and G. Cao, “Targeted vaccination based on a wireless sensor system,” in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2015, pp. 215-220: IEEE.
  15. Sun, Z. Lu, X. Zhang, M. Salathé, and G. Cao, “Infectious disease containment based on a wireless sensor system,” Ieee Access, vol. 4, pp. 1558-1569, 2016.
  16. Lu, X. Sun, Y. Wen, G. Cao, and T. La Porta, “Algorithms and applications for community detection in weighted networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 11, pp. 2916-2926, 2014.
  17. A. Prakash, L. Adamic, T. Iwashyna, H. Tong, and C. Faloutsos, “Fractional immunization in networks,” in Proceedings of the 2013 SIAM International Conference on Data Mining, 2013, pp. 659-667: SIAM.
  18. Fu, M. Small, D. M. Walker, and H. Zhang, “Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization,” Physical Review E, vol. 77, no. 3, p. 036113, 2008.
  19. Chen, G. Paul, S. Havlin, F. Liljeros, and H. E. Stanley, “Finding a better immunization strategy,” Physical review letters, vol. 101, no. 5, p. 058701, 2008.
  20. Zhu, G. Cao, S. Zhu, S. Ranjan, and A. Nucci, “A social network based patching scheme for worm containment in cellular networks,” in Handbook of optimization in complex networks: Springer, 2012, pp. 505-533.
  21. Li, Y. Yang, and J. Wu, “CPMC: An efficient proximity malware coping scheme in smartphone-based mobile networks,” in 2010 Proceedings IEEE INFOCOM, 2010, pp. 1-9: IEEE.
  22. Holme, “Efficient local strategies for vaccination and network attack,” EPL (Europhysics Letters), vol. 68, no. 6, p. 908, 2004.
  23. Jadidi, S. Jamshidiha, I. Masroori, P. Moslemi, A. Mohammadi, and V. Pourahmadi, “A two-step vaccination technique to limit COVID-19 spread using mobile data,” Sustainable Cities and Society, vol. 70, p. 102886, 2021.
  24. M. Jadidi, P. Moslemi, S. Jamshidiha, I. Masroori, A. Mohammadi, and V. Pourahmadi, “Targeted Vaccination for COVID-19 Using Mobile Communication Networks,” in 2020 11th International Conference on Information and Knowledge Technology (IKT), 2020, pp. 93-97: IEEE.
  25. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855-864.
  26. J. Keeling and P. Rohani, Modeling infectious diseases in humans and animals. Princeton University Press, 2011.
  27. Z. Kiss, J. C. Miller, and P. L. Simon, “Mathematics of epidemics on networks,” Cham: Springer, vol. 598, 2017.
  28. -C. Chen, P.-E. Lu, C.-S. Chang, and T.-H. Liu, “A time-dependent SIR model for COVID-19 with undetectable infected persons,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 32793294, 2020.
  29. Cooper, A. Mondal, and C. G. Antonopoulos, “A SIR model assumption for the spread of COVID-19 in different communities,” Chaos, Solitons & Fractals, vol. 139, p. 110057, 2020.
  30. Westerink-Duijzer, Mathematical Optimization in Vaccine Allocation. 2017.
  31. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701710.
  32. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” arXiv preprint arXiv:1310.4546, 2013
AUT Journal of Modeling and Simulation
Volume 53, Issue 1
June 2021
Pages 67-78