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The spread of COVID-19 between different agents in a hospital emergency department can be simulated by modeling the interactions between the agents and the environment. In this research, we use Agent Based Modeling and Simulation techniques to build a model of COVID-19 propagation based on an Emergency Department Simulator which has been tested and validated previously. The benefits of ABM include its ability to simulate complex systems, its flexibility, and its ability to model the interactions between different agents in the system. The obtained model will allow us to build a propagation simulator that enables us to build virtual environments with the aim of analyzing how the interactions between agents influence the rate of virus transmission. The model can be used to study the effectiveness of different interventions, such as social distancing, wearing masks, and vaccination, in reducing the spread of COVID-19.

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An ABMS COVID-19 Propagation Model for Hospital Emergency Departments

  • 会议论文

  • 发表日期：

    2025年

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  • Morteza Ansari Dogaheh,
  • Manel Taboada,
  • Francisco Epelde,
  • Emilio Luque,
  • Dolores Rexachs,
  • Alvaro Wong

摘要

The spread of COVID-19 between different agents in a hospital emergency department can be simulated by modeling the interactions between the agents and the environment. In this research, we use Agent Based Modeling and Simulation techniques to build a model of COVID-19 propagation based on an Emergency Department Simulator which has been tested and validated previously. The benefits of ABM include its ability to simulate complex systems, its flexibility, and its ability to model the interactions between different agents in the system. The obtained model will allow us to build a propagation simulator that enables us to build virtual environments with the aim of analyzing how the interactions between agents influence the rate of virus transmission. The model can be used to study the effectiveness of different interventions, such as social distancing, wearing masks, and vaccination, in reducing the spread of COVID-19.