ABSTRACT
Agent-based modeling has been widely used in the simulation of global pandemics, which provides useful policy implications and helps contain the pandemic's spread. Through agent-based modeling (ABM), people gain insight into the transmission of the pandemic and develop better policies to contain its spread. This article introduces the existing agent-based models used in the pandemic, such as smallpox, H1N1, and COVID-19, and the conclusions about pandemic forecasting that the scientists have reached through ABM. The introduction also shows the development and improvement of ABM as the computational power increases. It has been concluded from the existing research that implementing contact tracing and lockdown regulations could contribute to the achievement of digital herd immunity and contain the spread of the pandemic. Currently, scientists are dedicated to making a more scalable version of the agent-based model to analyze the transmission of the virus on a global scale. © 2022 IEEE.