Performance Evaluation of Automatic Exposure Notification with Bluetooth-based Proximity Detection

Description

The proximity detection mechanism in current automatic exposure notification systems is typically based on the Bluetooth signal strength from the individual’s mobile phone. However, there is an underlying error in this proximity detection methodology that could result in wrong exposure decisions i.e., false negatives and false positives. A false negative error happens if a truly exposed individual is mistakenly identified as not exposed. This misidentification could result in further spread of the virus by the exposed (yet undetected) individual. Likewise, when a non-exposed individual is incorrectly identified as exposed, a false positive error occurs. This could lead to unnecessary quarantine of the individual; and therefore, incurring further economic cost. In this project, an agent-based simulation platform has been developed to investigate the performance of the system in terms of false exposure determinations. Knowledge of how the Bluetooth-based proximity detection error impacts such false determinations as well as identification of methodologies that can reduce this impact will be helpful to enhance the effectiveness of automatic contact tracing.

Recent Publications

• K. Sayrafian, B. Cloteaux, V. Marbukh, “Impact of Using Soft Exposure Thresholds in Automatic Contact Tracing”, IEEE International Conference on E-health Networking, Application & Services (IEEE HEALTHCOM 2022), Genoa, Italy, Oct. 17-19, 2022
• K. Sayrafian, B. Cloteaux, V. Marbukh, “On the Performance of Automatic Exposure Determination Using Bluetooth-based Proximity Estimation”, IEEE International Conference on Communications (IEEE ICC), Seoul, South Korea, May 16-20, 2022
• K. Sayrafian, B. Cloteaux, V. Marbukh, C. Emiyah, “Evaluation of the Bluetooth-based Proximity Estimation for Automatic Exposure Determination”, IEEE Consumer Communications & Networking Conference (IEEE CCNC), Virtual Conference, Jan. 8-11, 2022