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

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Yaw Mei Wyin
Prajindra Sankar Krishnan
Chen Chai Phing
Tiong Sieh Kiong


contact networks, human mobility simulation, epidemic control policy


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.


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