Publication:
Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data

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dc.citedby0
dc.contributor.authorWyin Y.M.en_US
dc.contributor.authorKrishnan P.S.en_US
dc.contributor.authorPhing C.C.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorid58687252300en_US
dc.contributor.authorid36053261400en_US
dc.contributor.authorid57884999200en_US
dc.contributor.authorid57216824752en_US
dc.date.accessioned2024-10-14T03:17:48Z
dc.date.available2024-10-14T03:17:48Z
dc.date.issued2023
dc.description.abstractAccording to mobility data that records mobility traffic using location trackers on mobile phones, the COVID-19 epidemic and the adoption of social distance policies have drastically altered people�s visiting patterns. However, rather than the volume of visitors, the transmission is controlled by the frequency and length of concurrent occupation at particular places. Therefore, it is essential to comprehend how people interact in various settings in order to focus legislation, guide contact tracking, and educate prevention initiatives. This study suggests an effective method for reducing the virus�s propagation among university students enrolled on-campus by creating a self-developed Google History Location Extractor and Indicator software based on actual data on people�s movements. The platform enables academics and policymakers to model the results of human mobility and the epidemic condition under various epidemic control measures and assess the potential for future advancements in the epidemic�s spread. It provides tools for identifying prospective contacts, analyzing individual infection risks, and reviewing the success of campus regulations. By more precisely focusing on probable virus carriers during the screening process, the suggested multi-functional platform makes it easier to decide on epidemic control measures, ultimately helping to manage and avoid future outbreaks. Copyright � 2023.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18080/jtde.v11n3.771
dc.identifier.epage162
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85176218251
dc.identifier.spage143
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176218251&doi=10.18080%2fjtde.v11n3.771&partnerID=40&md5=de37796eea50eb0164d3861dcd77a21e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34055
dc.identifier.volume11
dc.pagecount19
dc.publisherTelecommunications Association Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Telecommunications and the Digital Economy
dc.subjectContact networks
dc.subjectepidemic control policy
dc.subjecthuman mobility simulation
dc.titleBig Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Dataen_US
dc.typeArticleen_US
dspace.entity.typePublication
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