Abstract
This study investigates the use of mobile data to understand patterns of population movements and disease transmission during the Covid-19 outbreak. It also focuses on understanding the implications of using this data for individual privacy. Using a mixed methods approach, we present 10 rich qualitative interviews and 412 survey responses from participants across the Nordics. Our novel results show that the use of mobile data can be characterized by two main categories: validation data and complementary data. We also identify five implications for practice: sharing resources and expertise between health agencies and telecom companies; extended collaboration with multiple network operators; cross-disciplinary collaboration among multiple parties; developing data and privacy guidelines; and developing novel methods and tools to address the trade-off between maintaining individual privacy and obtaining detailed information from mobile data. These implications may inform immediate and future actions to prepare for, mitigate, and control the spread of infectious diseases using mobile data. They also show privacy-driven limitations of mobile data in terms of data accuracy, richness, and scope.
Original language | English |
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Title of host publication | Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 |
Editors | Tung X. Bui |
Publisher | IEEE Computer Society |
Pages | 7151-7160 |
Number of pages | 10 |
ISBN (Electronic) | 9780998133157 |
Publication status | Published - 2022 |
Event | 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 - Virtual, Online, United States Duration: 2022 Jan 3 → 2022 Jan 7 |
Conference
Conference | 55th Annual Hawaii International Conference on System Sciences, HICSS 2022 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 2022/01/03 → 2022/01/07 |
Subject classification (UKÄ)
- Computer Science