Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106849
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | en_US |
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.contributor | Department of Applied Mathematics | en_US |
dc.contributor | Department of Rehabilitation Sciences | en_US |
dc.contributor | Department of Computing | en_US |
dc.contributor | Department of Health Technology and Informatics | en_US |
dc.contributor | Mental Health Research Centre | en_US |
dc.creator | Meng, J | en_US |
dc.creator | Liu, JYW | en_US |
dc.creator | Yang, L | en_US |
dc.creator | Wong, MS | en_US |
dc.creator | Tsang, H | en_US |
dc.creator | Yu, B | en_US |
dc.creator | Yu, J | en_US |
dc.creator | Lam, FMH | en_US |
dc.creator | He, D | en_US |
dc.creator | Yang, L | en_US |
dc.creator | Li, Y | en_US |
dc.creator | Siu, GKH | en_US |
dc.creator | Tyrovolas, S | en_US |
dc.creator | Xie, YJ | en_US |
dc.creator | Man, D | en_US |
dc.creator | Shum, DHK | en_US |
dc.date.accessioned | 2024-06-06T00:29:29Z | - |
dc.date.available | 2024-06-06T00:29:29Z | - |
dc.identifier.issn | 2468-2152 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106849 | - |
dc.language.iso | en | en_US |
dc.publisher | KeAi Publishing Communications Ltd. | en_US |
dc.rights | © 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Meng, J., Liu, J. Y. W., Yang, L., Wong, M. S., Tsang, H., Yu, B., Yu, J., Lam, F. M.-H., He, D., Yang, L., Li, Y., Siu, G. K.-H., Tyrovolas, S., Xie, Y. J., Man, D., & Shum, D. H. K. (2024). An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes. Infectious Disease Modelling, 9(2), 474-482 is available at https://doi.org/10.1016/j.idm.2024.02.002. | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Contact pattern | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | Indoor contact tracing | en_US |
dc.subject | Outbreak containment | en_US |
dc.title | An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 474 | en_US |
dc.identifier.epage | 482 | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.1016/j.idm.2024.02.002 | en_US |
dcterms.abstract | An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Infectious disease modelling, June 2024, v. 9, no. 2, p. 474-482 | en_US |
dcterms.isPartOf | Infectious disease modelling | en_US |
dcterms.issued | 2024-06 | - |
dc.identifier.scopus | 2-s2.0-85186093675 | - |
dc.identifier.eissn | 2468-0427 | en_US |
dc.description.validate | 202406 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a2769 | - |
dc.identifier.SubFormID | 48290 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Health and Medical Research Fund (HMRF) - Commissioned Research on COVID-19 from the Health Bureau of Hong Kong Special Administrative Region, the General Research Fund from the University Research Committee, and the Teaching Development Grant from the Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S2468042724000137-main.pdf | 1.04 MB | Adobe PDF | View/Open |
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