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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07870 |
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| _version_ | 1866910444626640896 |
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| author | Krishnan, Prajindra Sankar Chen, Chai Phing Alkawsi, Gamal Tiong, Sieh Kiong Capretz, Luiz Fernando |
| author_facet | Krishnan, Prajindra Sankar Chen, Chai Phing Alkawsi, Gamal Tiong, Sieh Kiong Capretz, Luiz Fernando |
| contents | The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the frequency and duration of concurrent occupancy at specific locations govern the transmission rather than the number of customers visiting. Therefore, understanding how people interact in different locations is crucial to target policies, inform contact tracing, and prevention strategies. This study proposes an efficient way to reduce the spread of the virus among on-campus university students by developing a self-developed Google History Location Extractor and Indicator software based on real-world human mobility data. The platform enables policymakers and researchers to explore the possibility of future developments in the epidemic's spread and simulate the outcomes of human mobility and epidemic state under different epidemic control policies. It offers functions for determining potential contacts, assessing individual infection risks, and evaluating the effectiveness of on-campus policies. The proposed multi-functional platform facilitates the screening process by more accurately targeting potential virus carriers and aids in making informed decisions on epidemic control policies, ultimately contributing to preventing and managing future outbreaks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mapping the Invisible: A Framework for Tracking COVID-19 Spread Among College Students with Google Location Data Krishnan, Prajindra Sankar Chen, Chai Phing Alkawsi, Gamal Tiong, Sieh Kiong Capretz, Luiz Fernando Software Engineering The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the frequency and duration of concurrent occupancy at specific locations govern the transmission rather than the number of customers visiting. Therefore, understanding how people interact in different locations is crucial to target policies, inform contact tracing, and prevention strategies. This study proposes an efficient way to reduce the spread of the virus among on-campus university students by developing a self-developed Google History Location Extractor and Indicator software based on real-world human mobility data. The platform enables policymakers and researchers to explore the possibility of future developments in the epidemic's spread and simulate the outcomes of human mobility and epidemic state under different epidemic control policies. It offers functions for determining potential contacts, assessing individual infection risks, and evaluating the effectiveness of on-campus policies. The proposed multi-functional platform facilitates the screening process by more accurately targeting potential virus carriers and aids in making informed decisions on epidemic control policies, ultimately contributing to preventing and managing future outbreaks. |
| title | Mapping the Invisible: A Framework for Tracking COVID-19 Spread Among College Students with Google Location Data |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2405.07870 |