Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities
<italic>Goal:</italic> The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called <italic/><bold>at-risk</bold><itali...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9117073/ |
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| Summary: | <italic>Goal:</italic> The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called <italic/><bold>at-risk</bold><italic/> regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. <italic>Methods:</italic> Our scheme identifies <italic/><bold>at-risk</bold><italic/> regions using existing cellular network functionalities—<italic>handover</italic> and <italic>cell (re)selection—used to maintain seamless coverage for mobile end-user equipment (UE)</italic>. The frequency of <italic>handover</italic> and <italic>cell (re)selection</italic> events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. <italic>Results:</italic> These measurements, which are accumulated over very many UEs, allow us to identify the <italic/><bold>at-risk</bold><italic/> regions without compromising the privacy and anonymity of individuals. <italic>Conclusions:</italic> The inferred <italic/><bold>at-risk</bold><italic/> regions can then be subjected to further monitoring and risk mitigation. |
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| ISSN: | 2644-1276 |