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- Publisher Website: 10.1007/s10916-018-1085-4
- Scopus: eid_2-s2.0-85054145893
- PMID: 30284042
- WOS: WOS:000446331300001
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Article: Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System
Title | Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System |
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Authors | |
Keywords | Healthcare-associated infections Disease outbreak Tracking Traceability Person-to-person contact analytics |
Issue Date | 2018 |
Publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0148-5598 |
Citation | Journal of Medical Systems, 2018, v. 42, p. article no. 222 How to Cite? |
Abstract | Our research is motivated by the rapidly-evolving outbreaks of rare and fatal infectious diseases, for example, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome. In many of these outbreaks, main transmission routes were healthcare facility-associated and through person-to-person contact. While a majority of existing work on modelling of the spread of infectious diseases focuses on transmission processes at a community level, we propose a new methodology to model the outbreaks of healthcare-associated infections (HAIs), which must be considered at an individual level. Our work also contributes to a novel aspect of integrating real-time positioning technologies into the tracking and modelling framework for effective HAI outbreak control and prompt responses. Our proposed solution methodology is developed based on three key components – time-varying contact network construction, individual-level transmission tracking and HAI parameter estimation – and aims to identify the hidden health state of each patient and worker within the healthcare facility. We conduct experiments with a four-month human tracking data set collected in a hospital, which bore a big nosocomial outbreak of the 2003 SARS in Hong Kong. The evaluation results demonstrate that our framework outperforms existing epidemic models for characterizing macro-level phenomena such as the number of infected people and epidemic threshold. |
Persistent Identifier | http://hdl.handle.net/10722/272904 |
ISSN | 2021 Impact Factor: 4.920 2020 SCImago Journal Rankings: 0.685 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cheng, CH | - |
dc.contributor.author | Kuo, YH | - |
dc.contributor.author | Zhou, Z | - |
dc.date.accessioned | 2019-08-06T09:18:46Z | - |
dc.date.available | 2019-08-06T09:18:46Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Medical Systems, 2018, v. 42, p. article no. 222 | - |
dc.identifier.issn | 0148-5598 | - |
dc.identifier.uri | http://hdl.handle.net/10722/272904 | - |
dc.description.abstract | Our research is motivated by the rapidly-evolving outbreaks of rare and fatal infectious diseases, for example, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome. In many of these outbreaks, main transmission routes were healthcare facility-associated and through person-to-person contact. While a majority of existing work on modelling of the spread of infectious diseases focuses on transmission processes at a community level, we propose a new methodology to model the outbreaks of healthcare-associated infections (HAIs), which must be considered at an individual level. Our work also contributes to a novel aspect of integrating real-time positioning technologies into the tracking and modelling framework for effective HAI outbreak control and prompt responses. Our proposed solution methodology is developed based on three key components – time-varying contact network construction, individual-level transmission tracking and HAI parameter estimation – and aims to identify the hidden health state of each patient and worker within the healthcare facility. We conduct experiments with a four-month human tracking data set collected in a hospital, which bore a big nosocomial outbreak of the 2003 SARS in Hong Kong. The evaluation results demonstrate that our framework outperforms existing epidemic models for characterizing macro-level phenomena such as the number of infected people and epidemic threshold. | - |
dc.language | eng | - |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0148-5598 | - |
dc.relation.ispartof | Journal of Medical Systems | - |
dc.rights | This is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: http://dx.doi.org/[insert DOI] | - |
dc.subject | Healthcare-associated infections | - |
dc.subject | Disease outbreak | - |
dc.subject | Tracking | - |
dc.subject | Traceability | - |
dc.subject | Person-to-person contact analytics | - |
dc.title | Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System | - |
dc.type | Article | - |
dc.identifier.email | Kuo, YH: yhkuo@hku.hk | - |
dc.identifier.authority | Kuo, YH=rp02314 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10916-018-1085-4 | - |
dc.identifier.pmid | 30284042 | - |
dc.identifier.scopus | eid_2-s2.0-85054145893 | - |
dc.identifier.hkuros | 299672 | - |
dc.identifier.volume | 42 | - |
dc.identifier.spage | article no. 222 | - |
dc.identifier.epage | article no. 222 | - |
dc.identifier.isi | WOS:000446331300001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0148-5598 | - |