Safe Blues: A Method for Estimation and Control in the Fight Against COVID-19

Abstract

How do fine modifications to social distancing measures really affect COVID-19 spread? A major problem for health authorities is that we do not know. In an imaginary world, we might develop a harmless biological virus that spreads just like COVID-19, but is traceable via a cheap and reliable diagnosis. By introducing such an imaginary virus into the population and observing how it spreads, we would have a way of learning about COVID-19 because the benign virus would respond to population behaviour and social distancing measures in a similar manner. Such a benign biological virus does not exist. Instead, we propose a safe and privacy-preserving digital alternative. Our solution is to mimic the benign virus by passing virtual tokens between electronic devices when they move into close proximity. As Bluetooth transmission is the most likely method used for such inter-device communication, and as our suggested "virtual viruses" do not harm individuals' software or intrude on privacy, we call these Safe Blues. In contrast to many app-based methods that inform individuals or governments about actual COVID-19 patients or hazards, Safe Blues does not provide information about individuals' locations or contacts. Hence the privacy concerns associated with Safe Blues are much lower than other methods. However, from the point of view of data collection, Safe Blues has two major advantages: - Data about the spread of Safe Blues is uploaded to a central server in real time, which can give authorities a more up-to-date picture in comparison to actual COVID-19 data, which is only available retrospectively. - Sampling of Safe Blues data is not biased by being applied only to people who have shown symptoms or who have come into contact with known positive cases. These features mean that there would be real statistical value in introducing Safe Blues. In the medium term and end game of COVID-19, information from Safe Blues could aid health authorities to make informed decisions with respect to social distancing and other measures. In this paper we outline the general principles of Safe Blues and we illustrate how Safe Blues data together with neural networks may be used to infer characteristics of the progress of the COVID-19 pandemic in real time. Further information is on the Safe Blues website: https://safeblues.org/.

Forecasting virus outbreaks with social media data via neural ordinary differential equations

“…A large amount of data is being generated, directly or indirectly, related to the spread of the virus, on various spatial and temporal scales. Sources include social media, mobile phone GPS, and mobile fitness devices, with other tools such as contact tracing or contact simulations 2 . It is clear that it is necessary to include this information in the first principles models or combinations with data-driven models 3,4 through a learning process or optimization.…”

Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis

“…The outbreak of COVID-19 and its accompanying pandemic has created an unprecedented challenge and unilateral response worldwide, and urged every nation to deploy its utmost resources toward combating the disease whilst managing the economic and social impacts. Tracking the epidemic and estimating the size of the infected population and effects of potential guidelines and restrictions has become a critical priority for most governments around the globe as it has immediate ramifications on all subsequent policy interventions (e.g., see [3][4][5][6][7]).…”

Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics

“…We have developed an Android application based on the open source contact-tracing OpenTrace software 7 published by the ll OPEN ACCESS Singaporean government. Our app follows the Safe Blues protocol specified in Appendix A of a previous study 4 with additional implementation details. The app source code is written in the Kotlin language, and interfaces with a backend service using the gRPC protocol.…”

“…To help address this problem, we propose the use of Safe Blues. 4,5 Safe Blues uses Bluetooth signals to transmit tokens between mobile devices that mimic virus spread in a real-time privacy-preserving manner. The wireless technology that it uses is similar to existing and emerging contact-tracing frameworks such as, Proximity, 6 Blue Trace, 7 and the Privacy-Preserving Contact Tracing framework developed by Apple and Google.…”