One of the biggest challenges in managing the coronavirus pandemic has been the lack of real-time information on the spread of the virus.
If the goal of well-meaning governments is clear – to mitigate the spread with minimal economic and social impact – it is very difficult to decide on the best policy to achieve it.
In Australia, public health authorities have consistently encouraged testing for the virus. The main driver behind this has been the desire to find positive cases and follow up on their contacts.
A by-product is information about how the virus is spread in the general population, which can be used to inform decisions about measures such as wearing a mask and restricting movement.
However, at best, the information provided by those tested is partial, biased and backward. This makes it difficult to make real-time decisions on when to impose restrictions, which are arguably the most important tool in the fight against the pandemic.
To help you, we have designed the Safe Blues framework. This is a joint project between researchers from the University of Queensland, the University of Auckland, the University of Melbourne, Cornell University, Columbia University, the Massachusetts Institute of Technology, Macquarie University and Delft University of Technology.
In this context, virus-type tokens are distributed among mobile devices via Bluetooth, in the same way that a biological virus spreads between people.
If adopted, a tool like Safe Blues could help public health authorities better control outbreaks by providing data on the general level of contact between people. Our article, recently published in Cell Press’s Patterns journal, sums up the idea.
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What about COVIDSafe?
Last year, the Australian government released the COVIDSafe contact tracing app. It was backed by a massive government-funded media campaign and initially adopted by millions of users.
COVIDSafe works by using Bluetooth signals to record digital “handshakes” between a user’s smartphone and nearby contacts who also have the app. It stores this information in a form that can be accessed retrospectively if the owner of the device is positive. Their contacts can then be traced and tested.
While the idea has merit, there have only been a few instances where the app has been able to detect confirmed cases more effectively than human contact tracers. Is the marriage between Bluetooth and public health therefore useless?
We do not think so.
Spread a virtual virus powered by Bluetooth
Social distancing works on all viruses, not just SARs-CoV-2 (the virus of the COVID-19 pandemic). For example, major lockdowns from COVID-19 have not only curbed the spread of SARS-CoV-2, but have also mitigated the spread of the common cold.
With that in mind, let’s return to the problem of lack of information and consider a thought experiment: What if we could create a virtual virus that spreads in exactly the same way as COVID-19? Although this one is harmless and traceable in real time.
By studying how this virtual virus evolves, we would gain important information about how SARs-CoV-2 evolves. This could allow policymakers to design the best strategies to enforce the restrictions and prevent the virus from spreading further.
To implement the framework, the mobile devices of a random subset of the population would be deliberately infected with the secure digital virus, which we refer to as Safe Blues.
Then, based on the measurements of the spread of this “infection”, public health authorities might have a better idea of how the real coronavirus is spreading.
Statistically, the number of cases and models shown on the Safe Blue app would follow similar trends to the real virus.
Of course, individuals infected with SARs-CoV-2 and those infected with Safe Blues would not be the same. In fact, the simulations showed that only a small fraction of the population would need to participate with a Safe Blues app for it to provide reliable predictions.
It is important to note that Bluetooth signals do not spread like viruses. But if we generated hundreds of variations of Bluetooth virtual tokens, this set could capture many of the social movement patterns that cause the infection – and therefore could be correlated with the actual virus.
Where is it now with Safe Blues?
Safe Blues’ machine learning methods have been developed and evaluated using mathematical simulation models. Early results show that a set of Safe Blues token strands can provide powerful estimates of actual epidemic behavior.
The Safe Blues team is currently working on a pilot system at the University of Auckland. Using an experimental Safe Blues Android app, the goal is to generate and study how the virtual virus spreads across a campus.
The information provided by the application could also be associated with data from wastewater measurements and existing social networks such as Google, Apple or Facebook.
We believe that virtual virus spreading techniques such as Safe Blues could greatly contribute to our real-time understanding of this pandemic, as well as future outbreaks.
This article by Yoni Nazarathy, Associate Professor of Data Science, University of Queensland, and Peter Taylor, Australian Fellow, University of Melbourne is republished from The Conversation under a Creative Commons license. Read the original article.