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Wireless Identification of Viral Infection (WiVi)

College
College of Engineering (COE)
Researchers
El Gamal, Hesham
Licensing Manager
Hong, Dongsung Hong.923@osu.edu

T2021-023 This invention introduces a novel method for airborne viral pandemic mitigation using WiFi access points as infrastructural spatiotemporal anchors.

The Need

New viruses are constantly being discovered. From SARS to Bird Flu, and HIV to COVID-19. The threat of global pandemics is real and methods for tracing and controlling it need to be available.

It is necessary that there are capabilities available which can inform the modeling of future viral spread and inform various pandemic mitigation decisions. (e.g., deep cleaning of certain areas, shutting down building, avoiding locations with high risk of infection)

The Technology

Dr. Hesham El Gamal has provided a novel method to solve this issue. This invention consists of a method for airborne viral pandemic mitigation using WiFi access points as infrastructural spatiotemporal anchors. The WiFi access logs are used as a primary data source which can be coupled with other data feeds based on the specific environments in which this technology is being deployed.

Compared with other device level approaches (e.g., mobile applications), the proposed technique can be significantly more efficient in capturing the risk of airborne and surface-based transmission of viral diseases in indoor environments. This technology will empower efficient contact tracing, surveillance testing, and predictive modeling and mitigation of airborne viral pandemics.

Commercial Applications

  • Healthcare
  • Mobile/Web-based services
  • Public Health Sector
    • Universities
    • Businesses

Benfits/Advantages

  • Effective tracing
  • Better mitigation strategies
  • Accuracy

Research Interests

Dr. Hesham El Gamal is the chair of the Department of Electrical and Computer Engineering (ECE) at The Ohio State University. His research interests span information theory, signal processing, algebraic number theory, wireless communications, and machine learning.