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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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/9117073/ |
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| author | Alaa A. R. Alsaeedy Edwin K. P. Chong |
| author_facet | Alaa A. R. Alsaeedy Edwin K. P. Chong |
| author_sort | Alaa A. R. Alsaeedy |
| collection | DOAJ |
| description | <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. |
| format | Article |
| id | doaj-art-c2db0255412e4bf0bfa3cd50b42e27cb |
| institution | DOAJ |
| issn | 2644-1276 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-c2db0255412e4bf0bfa3cd50b42e27cb2025-08-20T03:15:15ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762020-01-01118718910.1109/OJEMB.2020.30024479117073Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network FunctionalitiesAlaa A. R. Alsaeedy0https://orcid.org/0000-0001-7300-7308Edwin K. P. Chong1https://orcid.org/0000-0002-7622-4815Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USADepartment of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA<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.https://ieeexplore.ieee.org/document/9117073/COVID-19infectious diseasestracking |
| spellingShingle | Alaa A. R. Alsaeedy Edwin K. P. Chong Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities IEEE Open Journal of Engineering in Medicine and Biology COVID-19 infectious diseases tracking |
| title | Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities |
| title_full | Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities |
| title_fullStr | Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities |
| title_full_unstemmed | Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities |
| title_short | Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities |
| title_sort | detecting regions at risk for spreading covid 19 using existing cellular wireless network functionalities |
| topic | COVID-19 infectious diseases tracking |
| url | https://ieeexplore.ieee.org/document/9117073/ |
| work_keys_str_mv | AT alaaaralsaeedy detectingregionsatriskforspreadingcovid19usingexistingcellularwirelessnetworkfunctionalities AT edwinkpchong detectingregionsatriskforspreadingcovid19usingexistingcellularwirelessnetworkfunctionalities |