An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit

Abstract

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agentbased and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and

The Application of Model-Based Systems Engineering to Rural Healthcare System Disaster Planning: A Scoping Review

“…In fact, we identified several simulations and models that were developed and applied to urban healthcare settings that would be appropriate for use in rural settings, either in the existing form or with minor tweaks. Model-based systems engineering applications have become increasingly popular in large, urban healthcare systems, particularly related to pandemic response (Haghpanah et al 2021 ; Possik et al 2022 ). These applications have also found considerable use in other disaster contexts, including earthquakes (Arboleda et al 2007 ; Jacques et al 2014 ; Ceferino et al 2020 ; Gul et al 2020 ; Hassan and Mahmoud 2020 ), floods (Zehrouni et al 2017 ), wildfire (Hassan and Mahmoud 2021 ), and other mass causality or surge-triggering events (Smith et al 2009 ; TariVerdi et al 2019 ; Benkacem et al 2022 ; Trucco et al 2022 ), and across the disaster management cycle, including preparedness (for example, evaluating healthcare systems planning (Smith et al 2009 ; Zehrouni et al 2017 ; Gul et al 2020 ), assessing vulnerability (Arboleda et al 2007 )), response (for example, resource allocation, supply chain, and patient demand (Smith et al 2009 ; Hassan and Mahmoud 2021 ; Benkacem et al 2022 ; Trucco et al 2022 ), or evaluate response activities or services (Jacques et al 2014 ; TariVerdi et al 2019 ; Ceferino et al 2020 )), or recovery (for example, recovery of healthcare systems (Hassan and Mahmoud 2020 )).…”

Immersive Innovation: Exploring Interactive Virtual Reality through Distributed Simulations

“…Similarly, the use of DS in simulating complex transportation networks, leveraging existing computational resources effectively and enhancing the efficiency of simulating large-scale systems, has been demonstrated [11]. The application of DS in healthcare systems has been studied in [12], showcasing how DS enables the integration of disparate simulation models to create a comprehensive representation of the healthcare environment. These studies [9][10][11][12] exemplify the potential benefits of DS in simulating complex models in manufacturing, transportation, and healthcare.…”

“…The application of DS in healthcare systems has been studied in [12], showcasing how DS enables the integration of disparate simulation models to create a comprehensive representation of the healthcare environment. These studies [9][10][11][12] exemplify the potential benefits of DS in simulating complex models in manufacturing, transportation, and healthcare. DS offers a cost-effective approach by leveraging distributed resources, enabling scalability, and effectively handling large-scale systems across various domains.…”

“…and recovered) model, as depicted in Figure 2 [12]. This enhanced model incorporated additional features to account for asymptomatic and presymptomatic infected healthcare workers, who served as potential sources of viral contamination and transmission.…”