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


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

October 14, 2021
December 10, 2021
January 30, 2022
agent-based modeling, agent-based simulation, COVID-19, bibliometric analysis, literature review

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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