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dc.contributor.authorYixuan, Y
dc.contributor.authorHao, F
dc.contributor.authorDoo-Soon, P
dc.contributor.authorSony, P
dc.contributor.authorHyejung, L
dc.contributor.authorMakara, M
dc.date.accessioned2021-11-26T13:45:54Z
dc.date.issued2021-12-30
dc.date.updated2021-11-26T13:29:30Z
dc.description.abstractDue to the COVID-19 outbreak, there is an urgent need to research the spread of disease and prevention strategies. As the spread of COVID-19 is closely related to the structure of human social networks, there are a lot of existing works that use a topological structure to analyze the characteristics of spread. Several studies have proposed certain strategies to prevent COVID-19 by analyzing the topological structure of the contact network, but most of the existing works have focused on detecting dense groups such as cliques; however, as the clique is the densest subgraph, it is easy for it to be influenced when the data has noise or lacks some edges. To reduce the influences of noise or lacks of data, there is a concept of γ-Quasi-Cliques is considered in this paper. γ-Quasi-Cliques is less restrictive and denser than cliques, and it is thus more suitable for analyzing and detecting communities in social networks to identify the close contacts of patients and achieve timely control under high levels of epidemic prevention strategies. Therefore, this paper proposed an algorithm based on the traditional formal concept analysis method for detecting γ-quasi-cliques, and also designed a model for detecting and mining close contacts and sub-close (secondary) contacts in the patient's contact network. Consequently, manual intervention occurs in response to the asymptomatic close or sub-close contacts detected by this model, and nucleic acid testing and home isolation are performed to prevent the widespread of COVID19. In our experiments, a real-life contact network is used to determine the ideal value of γ for the detection of quasi-clique, which is 0.6, and the results show the validity and feasibility of the model.en_GB
dc.description.sponsorshipNational Research Foundation of Koreaen_GB
dc.description.sponsorshipBK21 FOUR (Fostering Outstanding Universities for Research)en_GB
dc.description.sponsorshipEuropean Union Horizon 2020en_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipFundamental Research Funds for the Central Universitiesen_GB
dc.identifier.citationVol. 11, article 45en_GB
dc.identifier.doi10.22967/HCIS.2021.11.045
dc.identifier.grantnumberNRF2020RIA2B5B01002134en_GB
dc.identifier.grantnumber5199990914048en_GB
dc.identifier.grantnumber840922en_GB
dc.identifier.grantnumber61702317en_GB
dc.identifier.grantnumberGK202103080en_GB
dc.identifier.urihttp://hdl.handle.net/10871/127954
dc.identifierORCID: 0000-0001-5288-5523 (Hao, Fei)
dc.language.isoenen_GB
dc.publisherKIPS-CSWRG : Korea Information Processing Society - Computer Software Research Groupen_GB
dc.rights© 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.subjectEpidemic Preventionen_GB
dc.subjectCOVID-19en_GB
dc.subjectContact Networken_GB
dc.subjectγ-Quasi-Cliqueen_GB
dc.titleModelling prevention and control strategies for COVID-19 propagation with patient contact networksen_GB
dc.typeArticleen_GB
dc.date.available2021-11-26T13:45:54Z
dc.descriptionThis is the final version. Available on open access from KIPS-CSWRG via the DOI in this recorden_GB
dc.identifier.eissn2192-1962
dc.identifier.journalHuman-centric Computing and Information Sciencesen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/en_GB
dcterms.dateAccepted2021-11-18
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-11-18
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-11-26T13:29:32Z
refterms.versionFCDAM
refterms.dateFOA2022-02-28T16:06:30Z
refterms.panelBen_GB


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© 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.