Available for Licensing, Collaboration, and Funding
Alaa A.R. Alsaeedy
At A Glance
Researchers at Colorado State University have developed a novel method to identify crowded regions with actively moving individuals, at risk for spreading COVID-19, by exploiting exiting cellular-network functionalities. The methods require no active participation by individuals and introduces no privacy concerns.
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The global COVID-19 pandemic is easily spread by people in close proximity, especially in crowds with mobile individuals (e.g., city centers). A widely accepted strategy to mitigate its spread is social distancing, avoiding crowded areas. 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.
According to a recent study, SARS-CoV-2 can live in the air for up to three hours (remaining viable in aerosols), exhaled by infected people while speaking, coughing, or even breathing, whether symptomatic or not. Officials and society are particularly concerned with the scenario where contagious people are present in areas with many other continuously mobile people. Such areas, which we call at-risk, naturally have high local basic reproduction number (R0). Conversely, sparse areas with mostly stationary people are not considered at-risk (e.g., residential areas with people remaining at home). The main issue is to distinguish high- from low-risk areas, allowing prioritization for further monitoring and risk mitigation. Our strategy is based on inferring the crowdedness and mobility using measurements of quantities already accessible to the cellular wireless network via UE mobility management protocols.
The novel strategy here does not track individuals, unlike many existing contact-tracing mobile-phone apps, where obvious privacy concerns abound. Instead, the method anonymously measures the aggregate density and mobility of mobile devices, without individual identities – through utilization of already existing cellular-network functionalities intended to manage end-users’ mobility and to ensure seamless coverage. Because practically everyone carries cellular mobile devices (called user equipment (UE)), these serve as always-on human trackers. More specifically, the higher the number and mobility of UEs, the higher the number and mobility of people. Moreover, these measurements do not require installation of any app nor any other action on the part of mobile users.
- Ability to Identify high risk areas for COVID-19
- Does not introduce privacy concerns as users are tracked anonymously
- Allows officials to distinguish high- and low- risk areas
- Estimation of populations staying at home
- Real-time updates of regional at-risk status
- Identifies locations that need to be prioritized for further monitoring and risk mitigation
Identification of at-risk locations for COVID-19
Estimation of populations following recommended public health policies
Applicable to other industries wherein understanding population density in real‑time is valuable
Currently under review
Last updated: May 2020
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#CSUInvents – #TechTuesday! The global #COVID19 pandemic is easily spread by people in close proximity, especially in crowds with #mobile individuals (e.g., city centers). Researchers at Colorado State University have developed a #novel method to identify crowded regions with actively moving individuals, at risk for spreading COVID-19, by exploiting exiting #cellular-network functionalities. The methods require no active participation by individuals and introduces no #privacy concerns. The method anonymously measures the aggregate density and mobility of #mobiledevices, without individual identities – through utilization of already existing cellular network functionalities intended to manage end-users’ mobility and to ensure seamless #coverage.
Inventors: Professor Edwin Chong, and PhD Candidate Alaa Refeis, Alsaeedy; CSU Walter Scott, Jr. College of Engineering
#covid19research #publichealth #contacttracing #FLC #AUTM #privacybydesign CSU Vice President for Research