The Corona-Warn-App (CWA) is one of the measures that has been put in place to contain the pandemic since June 2020. How effective is the app as a digital tool and what are its benefits? We will be using this blog to publish answers to these questions as well as results from the research undertaken by the Robert Koch Institute.
In this paper we want to examine the significance of a red warning versus a lower risk with or without a risk exposure. For this we consider the positivity rates of the test results, depending on the indicated risk. The data for this comes from the data donation of the Corona-Warn-App.
In recent months, there has been criticism that in times of high incidence, a red warning is no longer meaningful. The risk of infection would exist regardless of whether there was a red warning in the CWA or not. Moreover, the frequency of red warnings is so high that the individual warning no longer has any significance. We want to show that this is not the case. In particular for rapid test results, which are often the first reason for a PCR test, the positivity rate with a red warning was significantly greater than with a green warning (low risk with risk exposure). Furthermore, we investigate whether these greater positivity rates with a red warning can be solely explained by local fluctuations in incidence.
The Corona-Warn-App (CWA) is a key component for fighting the pandemic in that it helps in quickly ending chains of infection. The more individuals use the CWA, the higher its effectiveness. This is why it is important to know how many people actively use the CWA daily or occasionally, respectively. Since its release, the CWA has already been downloaded millions of times. The number of downloads is a first important indicator for the evaluation of the dissemination of the app. But not everyone who has downloaded the app, does in fact use it ‘actively’. We will discuss the effectiveness of the CWA for containing the pandemic occurrence in more detail in this post. We show in particular how many people actively use the app and thus help to make our daily lives safer.
This article is the first in our focus on data donation and the Corona-Warn-App. We analyse the extent to which the voluntary, data protection-compliant provision of user data can provide information about user behaviour and the processes involved in the CWA ecosystem.
How quickly is a PCR test result submitted to the CWA? How many warnings are sent out by the app? How quickly do people get tested after having received a warning notification? This first article responds to these and other questions about CWA user behaviour and does so using data voluntarily provided by the users.
This second analysis of the event-driven user survey (EDUS) focuses on the following questions: How surprised were Corona-Warn-App users to receive a red warning, and how did they change their behaviour in response? In addition, it also provides insights into the role played by the Corona-Warn-App in helping to contain the SARS-CoV-2 pandemic.
In order to measure the effectiveness of the Corona-Warn-App (CWA), it is important to know who is using it. However, data protection measures and the way in which the app is designed prevent this information from being collected or viewed.
Therefore, a voluntary, event-driven user survey (EDUS) was carried out between March and May 2021 to compile and analyse data on socio-demographics and user behaviour. Data were collected on sex, age, school leaving certificate, geographical distribution, and contact-related behaviour.
On 16 June 2020, the CWA was made available for download in the Apple App Store and the Google Play Store. By 31 May 2021, it had been downloaded 28.1 million times. The CWA, which provides digital contact tracing for people with SARS-CoV-2, is published by the Robert Koch Institute on behalf of the German government.
Some of the most important aspects of the app that have been discussed since its introduction are its effectiveness and benefits in terms of contact tracing for people with SARS-CoV-2. Very few empirical data are currently available to analyse these issues. Therefore, it is crucial that the data needed to do so are collected and analysed, and this has been planned since the app was developed.