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

Abstract

Viral spread is a complicated function of biological properties, the environment, preventative measures such as sanitation and masks, and the rate at which individuals come within physical proximity. It is these last two elements that governments can control through social-distancing directives. However, infection measurements are almost always delayed, making real-time estimation nearly impossible. Safe Blues is one way of addressing the problem caused by this time lag via online measurements combined with machine learning methods that exploit the relationship between counts of multiple forms of the Safe Blues strands and the progress of the actual epidemic. The Safe Blues protocols and techniques have been developed together with an experimental minimal viable product, presented as an app on Android devices with a server backend. Following initial exploration via simulation experiments, we are now preparing for a university-wide experiment of Safe Blues.

Emulation of epidemics via Bluetooth-based virtual safe virus spread: Experimental setup, software, and data

“…For example, Fig 2 , which also appeared in [ 1 ], presents a simulation run where blue strands are measured in real time, but the red strand is only measurable with a two-week delay. Here the Safe Blues machine learning framework was used to predict the current unobserved state of the red strand.…”

“…Machine learning based prediction using Safe Blues data was initially developed in [1,2] where both standard neural networks and scientific machine learning based techniques were employed. The measurements of Safe Blues data together with viral data were artificially simulated using several alternative models, and this synthetic data was used to calibrate and test the machine learning techniques.…”

“…After a strand was successfully transmitted to a susceptible individual, they became exposed and remained in this compartment for a random amount of time (incubation time) drawn from a gamma distribution with mean μ E and shape κ E as in Eq (1). Subsequently, once their exposure time elapsed, they became infectious and were able to infect further individuals with this strand.…”

“…Safe Blues, introduced in [1,2], works by spreading virtual 'virus-like' tokens, which we call strands. The strands can be of Susceptible-Exposed-Infectious-Removed (SEIR), Susceptible-Infectious-Removed (SIR), Susceptible-Exposed-Infectious (SEI), or Susceptible-Infectious (SI) type.…”

Emerging data inputs for infectious diseases surveillance and decision making

“…Participants were informed about an estimate of the number of encounters they made and a real-time visualization of the spread of virtual SEIR type diseases in the contact network ( 34 ). The Safe Blues experiment 7 is a similar study, which took place at the University of Auckland during 2021–2022 ( 35 , 36 ), examining how physical interactions affect the spread of diseases. The Safe Blues Android app spreads multiple safe virtual virus strands via Bluetooth based on individual’s physical proximity, with strands mimicking the behavior of biological viruses.…”