Fig. 1: Overview of the proximity tracing concept and results.

a Typical infection scenario in a public space (e.g. a supermarket), where close contact between an infected and a contact person is established over a long enough period of time. b An epidemiological risk function translates a time series of contact distances into infectiousness scores, which are then used to label the encounters in the training data set. c Example of a raw RSSI time series of the BLE signal, as well a corresponding contact distances. d We train a linear regression model to predict the infectiousness scores obtained from a given risk model. The linear regression receives as input a list of features, which were derived from the raw RSSI data. e The predictions of the linear regression model correlate strongly with the ground truth risk (up to 0.95 for the linear risk model). For a fixed critical risk threshold η the approach achieves high true positive rates with very few false classifications. f To this day only little is known about spreading behaviour of SARS-Cov-2. In this work, we calibrated our epidemiological models according to the latest recommendations of epidemiologists¹⁶. After large-scale deployment of proximity tracing technologies, it will be possible to compare the predicted infection events with the actually measured ones. This may help to refine epidemiological models.