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Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities

Goal: 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 at-risk regions) are susceptible to spreading the disease, especially if they contain...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043492/
https://www.ncbi.nlm.nih.gov/pubmed/34192281
http://dx.doi.org/10.1109/OJEMB.2020.3002447
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collection PubMed
description Goal: 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 at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities—handover and cell (re)selection—used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.
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spelling pubmed-80434922021-04-28 Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities IEEE Open J Eng Med Biol Article Goal: 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 at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities—handover and cell (re)selection—used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation. IEEE 2020-06-15 /pmc/articles/PMC8043492/ /pubmed/34192281 http://dx.doi.org/10.1109/OJEMB.2020.3002447 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043492/
https://www.ncbi.nlm.nih.gov/pubmed/34192281
http://dx.doi.org/10.1109/OJEMB.2020.3002447
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