Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29663
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dc.contributor.authorTang, K-
dc.date.accessioned2024-09-03T16:26:22Z-
dc.date.available2024-09-03T16:26:22Z-
dc.date.issued2024-08-29-
dc.identifierORCiD: Kangkang Tang https://orcid.org/0000-0002-9289-937X-
dc.identifier.citationTang. K. (2024) 'Enhancing healthcare facility resilience: utilizing machine learning model for airborne disease infection prediction', Journal of Building Performance Simulation, 0 (ahead of print), pp. 1 - 16. doi: 10.1080/19401493.2024.2395269.en_US
dc.identifier.issn1940-1493-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/29663-
dc.description.abstractDuring this pandemic, advanced epidemiological models have been widely used to determine intervention strategies for controlling the spread of the disease in public and healthcare settings. These models played a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. However, the accuracy of these predictions depends on the substantial amount of input data, which was not readily available at the onset of the pandemic. Another concern with the existing models is their inability to adequately account for the complex indoor built environments, which has been shown to significantly impact infection risk. To tackle these issues, this paper discusses the potential of developing a joint modelling technique that integrates machine learning models, building information models and agent-based models to assess the risk of nosocomial airborne infections. With limited available data, machine learning models can determine infection risk with high confidence.en_US
dc.format.extent1 - 16-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherTaylor & Francisen_US
dc.rightsCopyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectartificial neural network modelling (ANN)en_US
dc.subjectrespiratory syndrome coronavirus 2 (SARS-CoV-2)en_US
dc.subjectagent-based modelling (ABM)en_US
dc.subjectbuilding information modelling (BIM)en_US
dc.subjectmodern methods of construction (MMC)en_US
dc.titleEnhancing healthcare facility resilience: utilizing machine learning model for airborne disease infection predictionen_US
dc.typeArticleen_US
dc.date.dateAccepted2024-08-17-
dc.identifier.doihttps://doi.org/10.1080/19401493.2024.2395269-
dc.relation.isPartOfJournal of Building Performance Simulation-
pubs.issue00-
pubs.publication-statusPublished online-
pubs.volume0-
dc.identifier.eissn1940-1507-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Author-
Appears in Collections:Dept of Civil and Environmental Engineering Research Papers

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