Survey of Agent-Based Simulations for Modelling COVID-19 Pandemic
Advances in Science, Technology and Engineering Systems
On the 11th of March 2020, the World Health Organization (WHO) declared COVID-19 as a pandemic. Part of controlling measures of the pandemic is to understand the disease's trajectories. There are several possible interventions that can prevent and control its spread. Determining an optimal strategy is critical for policymakers to understand the impact of different scenarios. Many modelers are using agent-based simulations to virtually examine the efficiency of these scenarios. This paper aims
... review published papers that discussed Agent-based simulation (ABS) for modelling the COVID-19 pandemic. Major databases were searched for published articles in 2020 from top-ranking journals. Ten published papers were carefully chosen, and their findings were summarized and discussed. Among the methods used, three ABS models were shared as open source. Major findings included mask-wearing and working/studying from home as the optimal strategies, whereas airport screening is insufficient, and vertical isolation is similar to 'doing nothing' scenarios. Finally, one paper discussed the gaps in ABS and proposed a call of actions to the scientific community and guidelines to responsibly improve the ABS modelling's quality. This paper can contribute to understanding the current landscape of the COVID-19 pandemic simulation models and their limitations. It is proposed to access selected opensourced agent-based models to evaluate, utilize, customize or learn from, to help conduct more accurate simulations.