Sunday 19 July 2020

A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing



Concerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020.

One of the key non-technical obstacles to widespread adoption of CTA has been concerns about privacy. In this new paper our group present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that does not compromise privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions. The model then provides them feedback about the likelihood of the presence of asymptotic, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

The paper contains details of how to download and run the model.


And here is a 7-minute video showing the model in action:


2 comments:

  1. Innovative concept and a great paper! But what is the meaning of "infected"? The paper states, "The listed background risk factors (age, sex, obesity and underlying medical conditions) have no impact on whether or not a person becomes infected." Depending on the definition of "infected," the data may support the opposite assumption, i.e., genetic or risk-factor-based heterogeneity cause variation in susceptibility. See, e.g., Nic Lewis, https://www.nicholaslewis.org/why-herd-immunity-to-covid-19-is-reached-much-earlier-than-thought/; Gomes et al., https://www.medrxiv.org/content/10.1101/2020.04.27.20081893v3.

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  2. Thank you very much for writing such an interesting article on this topic.
    This has really made me think and I hope to read more.

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