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

Alaa A R Alsaeedy¹, Edwin K P Chong¹

¹Department of Electrical and Computer EngineeringColorado State UniversityFort CollinsCO80523USA.

IEEE Open Journal of Engineering in Medicine and Biology

|June 30, 2021

Summary

This study introduces a new method to identify high-risk COVID-19 spread areas using cellular network data. It detects crowded, mobile regions without compromising individual privacy for targeted interventions.

Keywords:

COVID-19 infectious diseases tracking

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Area of Science:

Epidemiology
Mobile Network Analysis
Public Health

Background:

COVID-19 spread is influenced by human density and mobility.
Identifying at-risk regions is crucial for disease containment.
Existing methods may face privacy challenges.

Purpose of the Study:

To present a novel strategy for identifying COVID-19 at-risk regions.
To leverage cellular network functionalities for public health surveillance.
To detect areas with high human density and mobility.

Main Methods:

Utilizing cellular network handover and cell (re)selection events.
Measuring the frequency of these events to infer population density and movement.
Aggregating data across numerous user equipment (UE) devices.
Ensuring individual privacy and anonymity throughout the process.

Main Results:

Successfully identified at-risk regions based on aggregated cellular network activity.
Demonstrated a correlation between handover/reselection frequencies and human density/mobility.
Validated the privacy-preserving nature of the proposed method.

Conclusions:

The developed strategy effectively identifies areas prone to COVID-19 transmission.
Cellular network data offers a scalable solution for public health monitoring.
Inferred at-risk regions can guide targeted public health interventions and monitoring.