Alaa A R Alsaeedy1, Edwin K P Chong1. 1. Department of Electrical and Computer EngineeringColorado State UniversityFort CollinsCO80523USA.
Abstract
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. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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 infectedpeople 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. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Authors: Catherine P Adans-Dester; Stacy Bamberg; Francesco P Bertacchi; Brian Caulfield; Kara Chappie; Danilo Demarchi; M Kelley Erb; Juan Estrada; Eric E Fabara; Michael Freni; Karl E Friedl; Roozbeh Ghaffari; Geoffrey Gill; Mark S Greenberg; Reed W Hoyt; Emil Jovanov; Christoph M Kanzler; Dina Katabi; Meredith Kernan; Colleen Kigin; Sunghoon I Lee; Steffen Leonhardt; Nigel H Lovell; Jose Mantilla; Thomas H McCoy; Nell Meosky Luo; Glenn A Miller; John Moore; Derek O'Keeffe; Jeffrey Palmer; Federico Parisi; Shyamal Patel; Jack Po; Benito L Pugliese; Thomas Quatieri; Tauhidur Rahman; Nathan Ramasarma; John A Rogers; Guillermo U Ruiz-Esparza; Stefano Sapienza; Gregory Schiurring; Lee Schwamm; Hadi Shafiee; Sara Kelly Silacci; Nathaniel M Sims; Tanya Talkar; William J Tharion; James A Toombs; Christopher Uschnig; Gloria P Vergara-Diaz; Paul Wacnik; May D Wang; James Welch; Lina Williamson; Ross Zafonte; Adrian Zai; Yuan-Ting Zhang; Guillermo J Tearney; Rushdy Ahmad; David R Walt; Paolo Bonato Journal: IEEE Open J Eng Med Biol Date: 2020-08-07