Figure: Transmission risk level for Case 1: A is symptomatic with DSO provided. Figure 10, page 17.
If Corona-Warn-App (CWA) is successful in its Open Science approach it will be a game changer for the responsible use of machine learning in the public realm but also to enable more and better data gathering and applications in research.
The German COVID-19 tracking and tracing app Corona-Warn-App (CWA) has been released as open-source code on GitHub which also includes documentation of the underlying programming assumption of how transmission risk levels are calculated.
This really looks like a watershed moment where Open Science show its value in terms of accelerating knowledge exchange, but also its value to the public at large. In many sectors the use of algorithms or computing for decision making has been ‘black-boxed’ only to show later to be flawed in its assumptions or to leave the public with too many unanswered questions.
Abstract
Epidemiological Motivation of the Transmission Risk Level
This document contains an epidemiological description of the transmission risk level used in the German Corona-Warn-App (CWA). As its name suggests, the transmission risk is an essential part when estimating the overall risk of a person to get infected in an exposure incident. Usage of the transmission risk level is specified in the ExposureNotification API and in the CWA Architecture. In particular we use epidemiological information about COVID-19 from the literature to motivate the choice of levels for this parameter. To enhance transparency and reproducibility of the computations, we provide the mathematical derivations and the computations in one Rmarkdown document. The methods sketched below are likely to be subject to change, once additional information about the characteristics of COVID-19 is obtained or as feedback from the use of the app arrives.
Copyright (c) 2020 Deutsche Telekom AG and SAP SE or an SAP affiliate company. Licensed under the Apache License, Version 2.0
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