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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Telecommunications Association Inc.
2024
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| _version_ | 1833350785494155264 |
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| author | Wyin Y.M. Krishnan P.S. Phing C.C. Kiong T.S. |
| author2 | 58687252300 |
| author_facet | 58687252300 Wyin Y.M. Krishnan P.S. Phing C.C. Kiong T.S. |
| author_sort | Wyin Y.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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. |
| format | Article |
| id | my.uniten.dspace-34055 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Telecommunications Association Inc. |
| record_format | dspace |
| 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 |
| 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 |
| title | 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_short | 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 |
| topic | Contact networks epidemic control policy human mobility simulation |
| url_provider | http://dspace.uniten.edu.my/ |