A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing
July 20, 2020
Norman Fenton, Scott McLachlan, Peter Lucas, Kudakwashe Dube, Graham Hitman, Magda Osman, Evangelia Kyrimi, Martin Neil (2020); A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing. Preprint DOI: 10.1101/2020.07.15.20154286Listed on Kuda Dube’s research highlights
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. We 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.
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