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Automated contact tracing offers the potential to help minimize the spread of COVID-19 clusters, and to all appearances, the foundation is in place to leverage readily available Bluetooth® technology to realize its potential. Yet, automated contact tracing has been slow to take hold, appearing in a slowly growing pool of mobile apps. An emerging set of technologies and solutions might help overcome some of the limitations that have hampered successful deployment.
In principle, epidemiologists and healthcare providers rely on contact tracing to throttle the growth of clusters of widespread infections. In its traditional form, contact tracing relies on the ability of contagious individuals to provide contact-tracing workers with a list of individuals with whom they have been in recent contact. In turn, these workers attempt to notify those contacts of their possible exposure to the infection and further monitor the infection’s spread (Figure 1).
Figure 1: A typical contact tracing workflow starts with a list of patient-identified contacts. (Source: CDC)
The ability to quickly build a comprehensive list of identified contacts is clearly crucial to successful contact tracing. For diseases with well-understood timelines of infection and contagion, public-health experts can effectively triage such lists to prioritize contact with individuals who might have contact with an infected individual during the most contagious phase of the disease. COVID-19 complicates this normal process not only because of the wide variation in individual responses to exposure but also because of fundamental uncertainties regarding methods of transmission of the COVID-19-causing SARS-CoV-2 virus. Variation in the onset and duration of contagion and the existence of asymptomatic COVID-19 superspreaders adds further urgency to the task of efficiently tracing the source of infection in emerging clusters. As a result, the ability to rapidly develop a comprehensive list of contacts is critical to contact tracing.
The ubiquity of Bluetooth-enabled smartphones would seem to offer an obvious mechanism for automated identification and notification. As other Bluetooth-enabled smartphones come within range, a user's smartphone can record the unique identifier associated with the Bluetooth signal transmitted by each of those other devices. Over time, an app running on the user's smartphone can build a list of identifiers representing other individuals. If one of those individuals later becomes infected, the user can be notified by the smartphone app. If that user has become infected, the user's entire list of contacts can be similarly notified using higher level protocols.
Even so, a simple list of discovered Bluetooth transmitters is insufficient for contact tracing because a typical smartphone can detect transmitters located far enough away to be of no practical concern for contact tracing. For this reason, mobile apps for contact tracing have attempted to use the Received Signal Strength Indicator (RSSI) value generated by typical radio frequency (RF) receivers as an estimation of received signal strength.
In theory, RSSI values should follow the inverse square law, falling off to a level inversely proportional to the square of the distance between the transmitter and receiver. RSSI values change unpredictably because of signal absorption, and interference as a Bluetooth-enabled device such as a smartphone is moved through a real-world environment. Indeed, researchers at Trinity College in Dublin, Ireland, found that RSSI values changed significantly without a change in physical distance simply because the smartphones were held differently or repositioned, so the bearer's body blocked or absorbed RF energy.
Developers routinely average multiple RSSI measurements to help reduce RSSI variability, and others have applied more sophisticated filtering methods. However, even with more advanced post-processing algorithms, absorption and interference fundamentally limit the accuracy of methods based solely on RSSI values. As a result, researchers found that using conventional Bluetooth signaling alone can be insufficient to accurately determine whether another device has approached a specific physical distance, such as the six-foot social-distancing guideline.
To provide the needed accuracy, emerging methods combine conventional Bluetooth protocols with other RF methods. Although normal Bluetooth protocols exchange anonymized unique identifiers, complementary RF methods provide more accurate distance measurements. Still, other approaches have largely abandoned Bluetooth in favor of GPS or the time-of-flight methods with alternate RF technologies such as ultrawideband (UWB) to deliver more accurate distance measurements. Of course, these approaches are largely unable to take full advantage of the huge installed base of Bluetooth-enabled smartphones, typically requiring new device platforms that incorporate the required RF transceivers and signal processing capabilities.
Regardless of the underlying technology for distance measurement, however, a fundamental conflict between personal privacy and public-health requirements have limited the widespread acceptance required to make automated contact-tracing solutions effectively. Privacy concerns drive efforts to reduce personally identifiable information (PII), while public-health concerns drive efforts to gain additional detail needed to identify outbreaks. The difficulty of resolving this conflict largely hinges on how automated contact tracing solutions manage lists of acquired anonymized identifiers representing different contacts and how they deal with PII metadata, including the individual's absolute location.
For managing the list of contacts, two approaches differ in how contact lists are managed. In the centralized approach, each user device uploads its own unique anonymized identifier and its list of acquired identifiers to the server; in the decentralized approach, the device uploads only its own unique anonymized identifier to the server. The centralized approach then uses this data to identify possible infection sources and send a notification to each affected contact (replacing the manual notification currently performed by human contact tracing workers). In the decentralized approach, the user's device determines whether it came into close proximity with contagious individuals by downloading a list of associated identifiers from the server.
Public-health workers consider both centralized management and individual location information essential for identifying emerging clusters of infection—considerations both seemly at odds with pre-pandemic thinking. A recent study from researchers at Carnegie Mellon University and Stanford University found that most study participants preferred a securely managed centralized approach with controlled location sharing.
As developers combine enhanced methods for distance measurement accuracy with improved strategies for securely managing sensitive information, automated contact tracing solutions are likely to break through the current technological barrier and the current impasse between privacy and public health concerns.
Stephen Evanczuk has more than 20 years of experience writing for and about the electronics industry on a wide range of topics including hardware, software, systems, and applications including the IoT. He received his Ph.D. in neuroscience on neuronal networks and worked in the aerospace industry on massively distributed secure systems and algorithm acceleration methods. Currently, when he's not writing articles on technology and engineering, he's working on applications of deep learning to recognition and recommendation systems.
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