Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31562
Title: Hybrid Conceptual Modeling for Simulation: An Ontology Approach during Covid-19
Authors: Saleh, N
Bell, D
Sulaiman, Z
Keywords: COVID-19;process modeling;pandemics;decision making;medical services;predictive models;ontologies
Issue Date: 12-Dec-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Saleh, N., Bell, D. and Sulaiman, Z. (2021) 'Hybrid Conceptual Modeling for Simulation: An Ontology Approach during Covid-19', Proceedings Winter Simulation Conference, 2021, Phoenix, AZ, USA, 12-15 December, pp. 1 - 11. doi: 10.1109/WSC52266.2021.9715298.
Abstract: The recent outbreak of Covid-19 caused by SARS-CoV-2 infection that started in Wuhan, China, has quickly spread worldwide. Due to the aggressive number of cases, the entire healthcare system has to respond and make decisions promptly to ensure it does not fail. Researchers have investigated the integration between ontology, algorithms and process modeling to facilitate simulation modeling in emergency departments and have produced a Minimal-Viable Simulation Ontology (MVSimO). However, the “minimalism” of the ontology has yet to be explored to cover pandemic settings. Responding to this, modelers must redesign services that are Covid-19 safe and better reflect changing realities. This study proposes a novel method that conceptualizes processes within the domain from a Discrete-Event Simulation (DES) perspective and utilizes prediction data from an Agent-Based Simulation (ABS) model to improve the accuracy of existing models. This hybrid approach can be helpful to support local decision making around resources allocation.
Description: This conference paper is freely available online via the INFORMS WSC21archive at https://www.informs-sim.org/wsc21papers/108.pdf .
URI: https://bura.brunel.ac.uk/handle/2438/31562
DOI: https://doi.org/10.1109/WSC52266.2021.9715298
ISBN: 978-1-6654-3311-2 (ebk)
ISSN: 0891-7736
978-1-6654-3312-9 (PoD)
Other Identifiers: ORCiD: David Bell https://orcid.org/0000-0003-3148-6691
Appears in Collections:Dept of Computer Science Research Papers

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