Privacy-aware Contact Tracing with Knowledge Mining Mechanisms to Monitor and Understand the COVID-19 Pandemic

While researchers work on a vaccine to handle the COVID-19 outbreak, our best weapon against this infection is knowledge combined with strict isolation policies. The SARS-CoV-2 virus is rapidly spreading across several countries using its high infection risk combined with a variable incubation period (between 2 and 14 days). Quarantine decisions and containment efforts must ground on reliable information about the probability of contagion. We have designed a mobile app and a technological platform, compliant to the European legislation, which enables unidentified contact/exposure information of users to be efficiently collected in a fully anonymous way. After a case is diagnosed, those who were exposed with the infected patient can easily be tracked back and analysed. This allows the medical and emergency management authorities to take the correct actions to alert people who may have been in close contact with an infected patient. While existing solutions rely on sensitive data based on geolocalisation, our open-source framework does not expose personal information. This is achieved by exploiting solely the anonymous data exchanged by the Bluetooth LE handshaking protocol of our smartphones. Our solution does not use sensitive data to run any of the analysis and it does not allow people to locate infected patients. On the other hand, data that is relevant for research on understanding this disease will be collected (while keeping anonymity), such as user's symptoms and genetic sequences from lab samples. The aim of this project is to give authorities the right tools to enforce the best strategy to limit the outbreaks of COVID-19 or potential future outbreaks, by allowing them to deploy solutions at scale, and collect data for investigating Covid-19 through a data-driven approach.