Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App for increased understanding of its privacy-preserving design

TL;DR

This paper presents a visual simulation of the Corona-Warn-App to demonstrate its privacy-preserving contact tracing mechanism and its role in controlling COVID-19 spread.

Contribution

It introduces a visual simulation tool that illustrates the app's privacy features and contact notification process, enhancing understanding of its design.

Findings

The simulation shows effective privacy preservation in contact tracing.

It demonstrates timely notifications of infectious contacts.

The tool aids in understanding app's role in pandemic containment.

Abstract

The world is under an ongoing pandemic, COVID-19, of a scale last seen a century ago. Contact tracing is one of the most critical and highly effective tools for containing and breaking the chain of infections especially in the case of infectious respiratory diseases like COVID-19. Thanks to the technological progress in our times, we now have digital mobile applications like the Corona-Warn-App for digital contact tracing. However, due to the invasive nature of contact tracing, it is very important to preserve the privacy of the users. Privacy preservation is important for increasing trust in the app and subsequently enabling its widespread usage in a privacy-valuing population. In this paper, we present a visual simulation of the working of the Corona-Warn-App to demonstrate how the privacy of its users is preserved, how they're notified of infectious contacts and how it helps in containing the spread of COVID-19.