Detecting Regions At Risk for Spreading
COVID-19 Using Existing Cellular Wireless
Network Functionalities
Alaa A. R. Alsaeedy and Edwin K. P. Chong , Fellow, IEEE
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.
Index Terms—COVID-19, infectious diseases, tracking.
Impact Statement—Method to identify crowded regions
with actively moving individuals, at risk for spreading
COVID-19, by exploiting existing cellular-network functionalities.
Requires no active participation by individuals and
introduces no privacy concerns.
I. INTRODUCTION
THE global COVID-19 pandemic is easily spread by people
in close proximity, especially in crowds with mobile
individuals (e.g., city centers).Awidely accepted strategy to mitigate
its spread is social distancing, avoiding crowded areas [1].
There is an urgent need for different mitigation strategies to slow
the spread of this disease. Spreading by “silent carriers” mostly
depends on how they move and gather, the two viral-spreading
risk factors motivating our new mitigation strategy.
Manuscript received May 3, 2020; revised May 15, 2020 and May 31,
2020; accepted June 7, 2020. Date of publication June 15, 2020; date of
current version July 2, 2020. Alaa A. R. Alsaeedy was supported by a
scholarship from the Iraqi Ministry of Higher Education and Scientific Research
under Grant 4650/11/16/2014. Edwin K. P. Chong was supported
in part by the National Science Foundation under Grant CMMI-1638284.
(Corresponding author: Alaa Alsaeedy.)
The authors are with the Department of Electrical and Computer
Engineering, Colorado State University, Fort Collins, CO 80523 USA
(e-mail: alaa.alsaeedy@colostate.edu/outlook.com; edwin.chong@
colostate.edu).
This article has supplementary downloadable material available at
https://ieeexplore.ieee.org, provided by the authors.
Digital Object Identifier 10.1109/OJEMB.2020.3002447
Our strategy does not track individuals, unlike many existing
contact-tracing mobile-phone apps [2], which require
widespread user adoption and have obvious privacy concerns.
Instead,weanonymously measure the aggregate density and mobility
of mobile devices,without individual identities, as detailed
below. Moreover, thesemeasurements do not require installation
of any app nor any other action on the part of mobile users.