Open AccessAgent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis
Author(s) -
Jing Tang,
Sukrit Vinayavekhin,
Manapat Weeramongkolkul,
Chanakan Suksa,
Kantapat Pattarapremcharoen,
Sasinat Thiwathittayanuphap,
Natt Leelawat,
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Publication year - 2022
Publication title -
journal of disaster research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.332
H-Index - 18
eISSN - 1883-8030
pISSN - 1881-2473
DOI - 10.20965/jdr.2022.p0093
Subject(s) - pandemic , computer science , covid-19 , data science , management science , coronavirus , operations research , risk analysis (engineering) , infectious disease (medical specialty) , engineering , business , disease , medicine , pathology
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.