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An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions
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DOI：10.1109/ACCESS.2021.3114226.png)
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Abstract
En 中文
The traditional agent-based model requires high computing power of the central processing unit. Thus, an improved agent-based model combined with the discrete event simulation method is proposed. The result of the equation-based Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) model with the same parameter combination, which has been demonstrated to be effective, is used to verify the validity of this improved agent-based model. Additionally, an analysis based on simulation results of the Contact Tracing Measure (CTM), Location-Based Checking-Testing Measure (LCTM), Lockdown Measure (LM), Mobile Cabin Isolation and Hospital Measure (MCHM) is presented. The simulation results show that implementing long-term lockdown measures has the best effect on epidemic control. Moreover, according to the simulation results, we inferred that using only nonpharmaceutical epidemic prevention measures may result in a second outbreak of COVID-19 owing to the risk of asymptomatic transmission.
Keywords:
Computational modeling
Codes
Data models
COVID-19
Mathematical models
Statistics
Sociology
Agent-based model
asymptomatic
COVID-19
discrete event simulation