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JDR Vol.17 No.1 pp. 93-102
(2022)
doi: 10.20965/jdr.2022.p0093

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Agent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis

Jing Tang*1,*2, Sukrit Vinayavekhin*3,†, Manapat Weeramongkolkul*1, Chanakan Suksanon*1, Kantapat Pattarapremcharoen*1, Sasinat Thiwathittayanuphap*1, and Natt Leelawat*2,*4

*1International School of Engineering, Faculty of Engineering, Chulalongkorn University
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand

†Corresponding author

*2Disaster and Risk Management Information Systems Research Unit, Chulalongkorn University, Bangkok, Thailand

*3Thammasat Business School, Thammasat University, Bangkok, Thailand

*4Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Received:
October 14, 2021
Accepted:
December 10, 2021
Published:
January 30, 2022
Creative Commons License
Keywords:
agent-based modeling, agent-based simulation, COVID-19, bibliometric analysis, literature review
Abstract

The coronavirus disease has caused an ongoing pandemic worldwide since 2019. To slow the rapid spread of the virus, many countries have adopted lockdown measures. To scientifically determine the most appropriate measures and policies, agent-based simulation and modeling techniques have been employed. It can be challenging for researchers to select the appropriate tools and techniques as well as the input and output parameters. This study conducted a bibliometric analysis, especially a co-word network analysis, to classify relevant research articles into five clusters: conceptual, economic-based, organizational, policy-based, and statistical modeling. It then explained each approach and point of concern. Through this, researchers and modelers can identify the optimal approaches for their agent-based models.

Cite this article as:
J. Tang, S. Vinayavekhin, M. Weeramongkolkul, C. Suksanon, K. Pattarapremcharoen, S. Thiwathittayanuphap, and N. Leelawat, “Agent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis,” J. Disaster Res., Vol.17 No.1, pp. 93-102, 2022.
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References
  1. [1] D. Cucinotta and M. Vanelli, “WHO Declares COVID-19 a Pandemic,” Acta Biomed, Vol.91, pp. 157-160, 2020.
  2. [2] S. Vinayavekhin, R. Phaal, T. Thanamaitreejit, and K. Asatani, “Emerging trends in roadmapping research: A bibliometric literature review,” Technol. Anal. Strateg. Manag., doi: 10.1080/09537325.2021.1979210, 2021.
  3. [3] S. Vinayavekhin and S. Chanplakorn, “A review of management studies published in Journal of Business Administration: Bibliometric and co-word analysis,” J. Bus. Adm., Vol.44, No.170, pp. 55-77, doi: 10.14456/jba.2021.10, 2021.
  4. [4] P. Chinotaikul and S. Vinayavekhin, “Digital Transformation in Business and Management Research: Bibliometric and Co-word Network Analysis,” 2020 1st Int. Conf. on Big Data Analytics and Practices (IBDAP), doi: 10.1109/IBDAP50342.2020.9245456, 2020.
  5. [5] N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, Vol.84, No.2, pp. 523-538, 2010.
  6. [6] M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field,” J. Informetr., Vol.5, No.1, pp. 146-166, 2011.
  7. [7] L. Waltman, N. J. van Eck, and E. C. M. Noyons, “A unified approach to mapping and clustering of bibliometric networks,” J. Informetr., Vol.4, No.4, pp. 629-635, 2010.
  8. [8] K. Charoenthammachoke, N. Leelawat, J. Tang, and A. Kodaka, “Business continuity management: A preliminary systematic literature review based on ScienceDirect database,” J. Disaster Res., Vol.15, No.5, pp. 546-555, doi: 10.20965/jdr.2020.p0546, 2020.
  9. [9] K. K. F. Li, S. A. Jarvis, and F. Minhas, “Elementary effects analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of social distancing and mask usage,” Comput. Biol. Med., Vol.134, Article No.104369, doi: 10.1016/j.compbiomed.2021.104369, 2021.
  10. [10] L. Durán-Polanco and M. Siller, “Crowd management COVID-19,” Annu. Rev. Control., Vol.52, pp. 465-478, doi: 10.1016/j.arcontrol.2021.04.006, 2021.
  11. [11] B. M. Castro, Y. D. A. de Melo, N. F. dos Santos, A. L. da Costa Barcellos, R. Choren, and R. M. Salles, “Multi-agent simulation model for the evaluation of COVID-19 transmission,” Comput. Biol. Med., Vol.136, Article No.104645, doi: 10.1016/j.compbiomed.2021.104645, 2021.
  12. [12] K. Zhang, T. N. Vilches, M. Tariq, A. P. Galvani, and S. M. Moghadas, “The impact of mask-wearing and shelter-in-place on COVID-19 outbreaks in the United States,” Int. J. Infect. Dis., Vol.101, pp. 334-341, 2020.
  13. [13] N. M. Gharakhanlou and N. Hooshangi, “Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (Case study: Urmia, Iran),” Inform. Med. Unlocked, Vol.20, Article No.100403, doi: 10.1016/j.imu.2020.100403, 2020.
  14. [14] E. Du, E. Chen, J. Liu, and C. Zheng, “How do social media and individual behaviors affect epidemic transmission and control?,” Sci. Total. Environ, Vol.761, Article No.144114, doi: 10.1016/j.scitotenv.2020.144114, 2021.
  15. [15] S. Lu, W. Wang, Y. Cheng, C. Yang, Y. Jiao, M. Xu, Y. Bai, J. Yang, H. B. Song, L. Wang, J. Wang, B. Rong, and J. Xu, “The food-trade associated spreading of COVID-19 outbreak from a contaminated super wholesale food market in Beijing,” SSRN Preprints with The Lancet, Article No.3726178, doi: 10.2139/ssrn.3726178, 2020.
  16. [16] A. Bouchnita and A. Jebrane, “A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions,” Chaos Solitics Fractals, Vol.138, Article No.109941, doi: 10.1016/j.chaos.2020.109941, 2020.
  17. [17] Y. Tatsukawa, M. R. Arefin, M. Tanaka, and J. Tanimoto, “Free ticket, discount ticket or intermediate of the best of two worlds – Which subsidy policy is socially optimal to suppress the disease spreading?,” J. Theor. Biol., Vol.520, Article No.110682, doi: 10.1016/j.jtbi.2021.110682, 2021.
  18. [18] P. C. L. Silva, P. V. C. Batista, H. S. Lima, M. A. Alves, F. G. Guimarães, and R. C. P. Silva, “COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions,” Chaos Solitons Fractals, Vol.139, Article No.110088, doi: 10.1016/j.chaos.2020.1100882020.
  19. [19] Y. Wang, B. Li, R. Gouripeddi, and J. C. Facelli, “Human activity pattern implications for modeling SARS-CoV-2 transmission,” Comput. Meth. Prog. Biomed., Vol.199, Article No.105896, doi: 10.1016/j.cmpb.2020.105896, 2021.
  20. [20] N. Zhang, P. T. J. Chan, W. Jia, W., C. H. Dung, P. Zhao, H. Lei, B. Su, P. Xue, W. Zhang, J. Xie, and Y. Li, “Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong,” Environ. Int., Article No.106723, doi: 10.1016/j.envint.2021.106723, 2021.
  21. [21] M. D’Orazio, G. Bernardini, and E. Quagliarini, “Sustainable and resilient strategies for touristic cities against COVID-19: An agent-based approach,” Saf. Sci., Vol.142, Article No.105399, 2021.
  22. [22] T. Gwizdałła, “Viral disease spreading in grouped population,” Comput. Meth. Prog. Biomed., Vol.197, Article No.105715, 2020.
  23. [23] E. Cuevas, “An agent-based model to evaluate the COVID-19 transmission risks in facilities,” Comput. Biol. Med., Vol.121, Article No.103827, 2020.
  24. [24] Y. Ding, S. Wandelt, and X. Sun, “TLQP: Early-stage transportation lock-down and quarantine problem,” Transp. Res. Part C: Emerg. Technol., Vol.129, Article No.103218, 2021.
  25. [25] G. Hernandez-Mejia and E. A. Hernandez-Vargas, “When is SARS-CoV-2 in your shopping list?,” Math. Biosci., Vol.328, Article No.108434, 2020.
  26. [26] F. Araya, “Modeling the spread of COVID-19 on construction workers: An agent-based approach,” Saf. Sci., Vol.133, Article No.105022, 2021.
  27. [27] R. J. Milne, C. Delcea, and L.-A. Cotfas, “Airplane boarding methods that reduce risk from COVID-19,” Saf. Sci., Vol.134, Article No.105061, 2021.
  28. [28] P. T. Gressman and J. R. Peck, “Simulating COVID-19 in a university environment,” Math. Biosci., Vol.328, Article No.109436, 2020.
  29. [29] A. K. Kaisera, D. Kretschmerb, and L. Leszczensky, “Social network-based cohorting to reduce the spread of SARS-CoV-2 in secondary schools: A simulation study in classrooms of four European countries,” Lancet Reg. Health Eur., Vol.8, Article No.100166, 2021.
  30. [30] R. Zafarnejad and P. M. Griffin, “Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk,” Comput. Biol. Med., Article No.104518, 2021.
  31. [31] J. Panovska-Griffiths, C. C. Kerr, R. M. Stuart, D. Mistry, D. J. Klein, R. M. Viner, and C. Bonell, “Determining the optimal strategy for reopening schools, the impact of test and trace interventions, and the risk of occurrence of a second COVID-19 epidemic wave in the UK: a modelling study,” Lancet Child Adol Health, Vol.4, No.11, pp. 817-827, 2020.
  32. [32] M. S. Shamil, F. Farheen, N. Ibtehaz, I. M. Khan, and M. S. Rahman, “An agent-based Modeling of COVID-19: Validation, analysis, and recommendations,” Cogn. Comput., doi: 10.1007/s12559-020-09801-w, 2021.
  33. [33] G. Wallentin, D. Kaziyeva, and E. Reibersdorfer-Adelsberger, “COVID-19 Intervention Scenarios for a Long-term Disease Management,” Int. J. Health Policy Manag. Vol.9, No.12, pp. 508-516, 2020.
  34. [34] Q. D. Pham, R. M. Stuart, T. V. Nguyen, Q. C. Luong, Q. D. Tran, T. Q. Pham, L. T. Phan, T. Q. Dang, D. N. Tran, H. T. Do, D. Mistry, D. J. Klein, R. G. Abeysuriya, A. P. Oron, and C. C. Kerr, “Estimating and mitigating the risk of COVID-19 epidemic rebound associated with reopening of international borders in Vietnam: a modelling study,” Lancet Glob. Health, Vol.9, No.7, pp. e916-e924, 2021.
  35. [35] N. Zhang, P. Cheng, W. Jia, C. H. Dung, L. Liu, W. Chen, H. Lei, C. Kan, X. Han, B. Su, S. Xiao, H. Qian, B. Lin, and Y. Li, “Impact of intervention methods on COVID-19 transmission in Shenzhen,” Build. Environ., Vol.180, Article No.107106, 2020.
  36. [36] H. Tatapudi, R. Das, and T. K. Das, “Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region,” Glob. Epidemiol., Vol.2, Article No.100036, 2020.
  37. [37] N. M. Ferguson, D. A. T. Cummings, C. Fraser, J. C. Cajka, P. C. Cooley, and D. S. Burke, “Strategies for mitigating an influenza pandemic,” Nature, Vol.442, pp. 448-452, 2006.
  38. [38] Y. Vyklyuk, M. Manylich, M. Škoda, M. M. Radovanović, and M. D. Petrović, “Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems – evidence from the selected countries,” Results Phys. Vol.20, Article No.103662, 2021.
  39. [39] G. V. Bobashev, D. M. Goedecke, F. Yu, and J. M. Epstein, “A hybrid epidemic model: Combining the advantages of agent-based and equation-based approaches,” 2007 Winter Simulation Conf., pp. 1532-1537, 2007.
  40. [40] L. J. S. Allen, “Some discrete-time SI, SIR, and SIS epidemic models,” Math. Biosci., Vol.124, pp. 83-105, 1994.
  41. [41] V. Volpert, M. Banerjee, and S. Petrovskii, “On a quarantine model of coronavirus infection and data analysis,” Math. Model. Nat. Phenom., Vol.15, Article No.24, 2020.
  42. [42] B. L. Dickens, J. R. Koo, J. T. Lim, M. Park, S. Quaye, H. Sun, Y. Sun, R. Pung, A. Wilder-Smith, L. Y. A. Chai, V. J. Lee, and A. R. Cook, “Modelling lockdown and exit strategies for COVID-19 in Singapore,” Lancet Reg. Health West. Pac., Vol.1, Article No.100004, 2020.
  43. [43] T. N. Vilches, S. Nourbakhsh, K. Zhang, L. Juden-Kelly, L. E. Cipriano, J. M. Langley, P. Sah, A. P. Galvani, and S. M. Moghadas, “Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada,” Prev. Med., Vol.148, Article No.106564, 2021.
  44. [44] D. Champredon, M. Najafi, M. Laskowski, A. Chit, and S. M. Moghadas, “Individual movements and contact patterns in a Canadian long-term care facility,” AIMS Public Health, Vol.5, pp. 111-121, 2018.
  45. [45] M. Najafi, M. Laskowski, P. T. de Boer, E. Williams, A. Chit, and S. M. Moghadas, “The effect of individual movements and interventions on the spread of influenza in long-term care facilities,” Med. Decis. Mak., Vol.37, pp. 871-881, 2017.
  46. [46] A. Chiba, “The effectiveness of mobility control, shortening of restaurants’ opening hours, and working from home on control of COVID-19 spread in Japan,” Health Place, Vol.70, Article No.102622, 2021.
  47. [47] A. Bisina and A. Moro. “JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses,” J. Urban Econ., doi: 10.1016/j.jue.2021.103368, 2021.
  48. [48] W. Qian, S. Bhowmick, M. O’Neill, S. Ramisetty-Mikler, and A. R. Mikler, “Applying a probabilistic infection model for studying contagion processes in contact networks,” J. Comput. Sci., Vol.54, Article No.101419, 2021.
  49. [49] S. Winkelmann, J. Zonker, C. Schütte, and N. D. Conrad, “Mathematical modeling of spatio-temporal population dynamics and application to epidemic spreading,” Math. Biosci., Vol.336, Article No.108619, 2021.
  50. [50] R. Markovi, M. Sterk, M. Marhl, M. Perc, and M. Gosak, “Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment,” Results Phys., Vol.26, Article No.104433, 2021.
  51. [51] C. Zachresona, S. L. Changa, O. M. Cliff, and M. Prokopenkoa, “How will mass-vaccination change COVID-19 lockdown requirements in Australia?,” Results Phys., Vol.26, Article No.104433, 2021.

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