On the Performance of Automatic Exposure Determination Using Bluetooth-based Proximity Estimation

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 paper, using a simulation platform and a notion of proximity detection error, we 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 and identification of methodologies that can reduce this impact will be helpful to enhance the effectiveness of an automatic contact tracing system. Our preliminary results indicate the substantial impact of the proximity estimation error on the exposure detection accuracy. The results also suggest how proper filtering of distance measurements may reduce this impact.