Evidence map›Paper›PMID 37954189›Full record

ArticleIEEE transactions on engineering management2023

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

Jalal Possik, Ali Asgary, Adriano O Solis, Gregory Zacharewicz, Mohammad A Shafiee, Mahdi M Najafabadi, Nazanin Nadri, Abel Guimaraes, Hossein Iranfar, Philip Ma and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in IEEE transactions on engineering management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 6 institutions in 3 countries.

Jalal PossikSchool of Administrative Studies and Advanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.ORCID https://orcid.org/0000-0002-5246-8102
Ali AsgarySchool of Administrative Studies and Advanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.ORCID https://orcid.org/0000-0002-6253-1021
Adriano O SolisSchool of Administrative Studies and Advanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.ORCID https://orcid.org/0000-0002-9166-3365
Gregory ZacharewiczLaboratoire des Sciences des RisquesInstitut Mines-Telecom-École des Mines d'Alès 30100 Alès France.ORCID https://orcid.org/0000-0001-7726-1725
Mohammad A ShafieeDepartment of MedicineToronto General Hospital Toronto ON M5G 2C4 Canada.
Mahdi M NajafabadiAdvanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.
Nazanin NadriAdvanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.
Abel GuimaraesAdvanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.
Hossein IranfarFaculty of Computer EngineeringK. N. Toosi University of Technology Tehran 19967-15433 Iran.
Philip MaCritical Care/Respiratory Therapy UnitToronto General Hospital Toronto ON M5G 2C4 Canada.
Christie M LeeCritical Care Medicine UnitSinai Health System Toronto ON M5G 1X5 Canada.
Mohammadali TofighiAdvanced Disaster, Emergency and Rapid Response SimulationYork University Toronto ON M3J 1P3 Canada.
Mehdi AarabiDepartment of MedicineToronto General Hospital Toronto ON M5G 2C4 Canada.
Simon GoreckiLab IMS UMR CNRS 5218University of Bordeaux 33400 Talence France.
Jianhong WuDepartment of Mathematics and Statistics and ADERSIMYork University Toronto ON M3J 1P3 Canada.
York University · CAToronto General Hospital · CACentre National de la Recherche Scientifique · FRInstitut Mines-Télécom · FRK.N.Toosi University of Technology · IRSinai Health System · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

Indexed as

Agent-based modeling (ABM)cloud computingCOVID-19discrete event simulation (DES)distributed simulation (DS)healthcare systemshigh-level architecture (HLA)hybrid simulationintensive care unit (ICU)interoperabilityvirtual reality (VR)

Identifiers

PMID37954189
PMCPMC10620956
OpenAlexW4292581156

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.