Introduction and Scope

We have proposed a decentralized privacy-preserving contact tracing protocol with both active and passive participants. Active participants broadcast BLE beacons with pseudo-random IDs, while passive participants model conservative users who do not broadcast BLE beacons but still listen to the broadcasted BLE beacons.

Datasets

The proposed protocol, called DP-ACT, is evaluated using both a face-to-face individual interaction dataset and five real-world BLE datasets.

• Face-to-Face Datasets: We have considered InVS15 dataset. InVS15 contains the face-to-face interactions of individuals measured during 12 days in an office building in France in 2015. We assume that all participants use COVID-19 contact-tracing mobile applications.

• Real-World BLE Datasets: We consider real-world BLE datasets collected considering five different scenarios: dining together at the table, "scenario01-lunch"; riding a train together, "scenario02-train"; working together in an open-space setting, "scenario03-work"; waiting in line at the supermarket, "scenario04-queue", and mingling in a club/bar, "scenario05 -party". These datasets were collected in laboratory conditions, and 20 users participated in these data collections (the information regarding 2 participants is missed, and the datasets are collected by the other 18 users) with different smartphone models. The duration of the data collection for each of these datasets is 30 minutes.

Assumptions and Measures

In our simulations, we consider 1,000 runs with random COVID-19 infected users and random indexes for passive users. The high-risk case detection probability is defined as the ratio of the detected high-risk cases to the total number of the high-risk cases, averaged over these 1,000 runs. A high-risk case is a person who has a high-risk contact with an infected person (the announced ones or the ones who are infected by announced ones and can transfer the virus to others after 4 days). The COVID-19 percentage is also defined as the number of announced COVID-19 infected users to the total number of users. Note that we assume that the passive users do not install the application based on the DP-3T protocol.

License and copyright

Copyright of the experimental data and code belongs to Lund University and EPFL. For other uses of codes, please reach out via e-mail.