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

According 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 th...

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Main Authors: Wyin Y.M., Krishnan P.S., Phing C.C., Kiong T.S.
Other Authors: 58687252300
Format: Article
Published: Telecommunications Association Inc. 2024
Subjects:
Contact networks
epidemic control policy
human mobility simulation
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id my.uniten.dspace-34055
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spelling my.uniten.dspace-340552024-10-14T11:17:48Z Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data Wyin Y.M. Krishnan P.S. Phing C.C. Kiong T.S. 58687252300 36053261400 57884999200 57216824752 Contact networks epidemic control policy human mobility simulation According 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. Final 2024-10-14T03:17:48Z 2024-10-14T03:17:48Z 2023 Article 10.18080/jtde.v11n3.771 2-s2.0-85176218251 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176218251&doi=10.18080%2fjtde.v11n3.771&partnerID=40&md5=de37796eea50eb0164d3861dcd77a21e https://irepository.uniten.edu.my/handle/123456789/34055 11 3 143 162 All Open Access Gold Open Access Telecommunications Association Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Contact networks
epidemic control policy
human mobility simulation
spellingShingle Contact networks
epidemic control policy
human mobility simulation
Wyin Y.M.
Krishnan P.S.
Phing C.C.
Kiong T.S.
Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
description According 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.
author2 58687252300
author_facet 58687252300
Wyin Y.M.
Krishnan P.S.
Phing C.C.
Kiong T.S.
format Article
author Wyin Y.M.
Krishnan P.S.
Phing C.C.
Kiong T.S.
author_sort Wyin Y.M.
title Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
title_short Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
title_full Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
title_fullStr Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
title_full_unstemmed Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
title_sort big data analytics in tracking covid-19 spread utilizing google location data
publisher Telecommunications Association Inc.
publishDate 2024
_version_ 1814061039088566272
score 13.222552

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