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Register a new userAn Empirical Evaluation of Bluetooth-based Decentralized Contact Tracing in Crowds
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Added by
Wei Jeng
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Digital contact tracing is being used by many countries to help contain COVID-19s spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
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Since the onset of the COVID-19s global spread we have been following the debate around contact tracing apps -- the tech-enabled response to the pandemic. As corporations, academics, governments, and civil society discuss the right way to implement these apps, we noticed recurring implicit assumptions. The proposed solutions are designed for a world where Internet access and smartphone ownership are a given, people are willing and able to install these apps, and those who receive notifications about potential exposure to the virus have access to testing and can isolate safely. In this work we challenge these assumptions. We not only show that there are not enough smartphones worldwide to reach required adoption thresholds but also highlight a broad lack of internet access, which affects certain groups more: the elderly, those with lower incomes, and those with limited ability to socially distance. Unfortunately, these are also the groups that are at the highest risks from COVID-19. We also report that the contact tracing apps that are already deployed on an opt-in basis show disappointing adoption levels. We warn about the potential consequences of over-extending the existing state and corporate surveillance powers. Finally, we describe a multitude of scenarios where contact tracing apps will not help regardless of access or policy. In this work we call for a comprehensive and equitable policy response that prioritizes the needs of the most vulnerable, protects human rights, and considers long term impact instead of focusing on technology-first fixes.
The global outbreak of COVID-19 has led to focus on efforts to manage and mitigate the continued spread of the disease. One of these efforts include the use of contact tracing to identify people who are at-risk of developing the disease through exposure to an infected person. Historically, contact tracing has been primarily manual but given the exponential spread of the virus that causes COVID-19, there has been significant interest in the development and use of digital contact tracing solutions to supplement the work of human contact tracers. The collection and use of sensitive personal details by these applications has led to a number of concerns by the stakeholder groups with a vested interest in these solutions. We explore digital contact tracing solutions in detail and propose the use of a transparent reporting mechanism, FactSheets, to provide transparency of and support trust in these applications. We also provide an example FactSheet template with questions that are specific to the contact tracing application domain.
How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus such as contact tracing, clusters identification, movement restrictions, and status validation. Specifically, we classify available techniques based on some characteristics such as technology, architecture, trade-offs (privacy vs utility), and the phase of adoption. We present a novel approach for evaluating privacy using both qualitative and quantitative measures of privacy-utility assessment of contact tracing applications. In this new method, we classify utility at three (3) distinct levels: no privacy, 100% privacy, and at k where k is set by the system providing the utility or privacy.
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.
Digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. Real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust.
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