COVID-19 Snapshot Monitoring (COSMO Germany) - Wave 16

In a crisis such as the current outbreak of the newly emerged coronavirus, it is of utmost importance to monitor public perceptions of risk, protective and preparedness behaviours, public trust, as well as knowledge and misinformation to enable government spokespeople, the media, and health organizations to implement adequate responses (WHO Europe, 2017; World Health Organization, 2017). The purpose of this serial cross-sectional study COSMO is to allow rapid and adaptive monitoring of these variables over time and to assess the relations between risk perceptions, knowledge and misinformation to preparedness and protective behaviour regarding COVID-19 in Germany.

A substantial percentage of transmissions of SARS-CoV-2 occur through infected persons in the presymptomatic stage or through asymptomatic cases. Mobile applications might have the potential to interrupt transmission chains by tracing and identifying presymptomatic infections, however, persons with poor health, low income or education, and older adults are more likely to have low digital skills and less technical resources but they are also more vulnerable to infection and severe COVID-19 disease. We assessed the sociodemographic characteristics of persons who had or had not downloaded the German Corona Tracing App during its initial deployment. We found that persons with lower educational status and lower income were less likely to download, were less willing to disclose positive test results and less willing to quarantine.

This study aims to analyze the association of sociodemographic characteristics of users to download and use of the Corona tracing app launched in Germany in June 2020 (“Corona Warn App”, CWA). Also, we want to examine attitudes towards the app stratified by selected population characteristics to understand barriers to successful use. Data from the most recent waves of the COVID-19 Snapshot Monitoring (COSMO) that contain items referring to the CWA (waves 15-19) will be used. Sociodemographic characteristics include age, gender, education, federal state, migrant status, number of children, presence of chronic disease, work status, income, smart phone use. Variables concerning CWA include information on download, use and opinions about functionality of the CWA. Main outcome parameter for CWA use will be the question “have you already downloaded the app”. Attitudes towards CWA will be analysed exploratively. Multiple logistic regression analyses will be used to assess the association of sets of potential sociodemographic predictors as independent variables.