Identifying the effects of COVID-19 on psychological well-being through unsupervised clustering for mixed data

Abstract

The COVID-19 pandemic has a strong worldwide impact on not only the health and economic sectors but also the (socio-)psychological functioning of individuals. Since psychological health is an important protective factor to prevent diseases, it is crucial to identify individuals with increased vulnerability during the crisis. 275 adults participated in a German online survey from April until August 2020 which investigated health-related, social, behavioral, and psychological effects of the COVID-19 pandemic. We here introduce an unsupervised clustering approach suitable for mixed data types combining the Gower distance with the Partitioning Around Medoids (PAM) algorithm k-Medoids. We were able to identify three clusters differing significantly in subjects’ well-being, psychological distress, and current financial and occupational concerns. The clusters also differed in age with younger persons reporting greater financial and occupational concerns, increased anxiety, higher psychological distress, and reduced subjective well-being. Features with the strongest impact on the clustering were examined using a wrapping method and the feature importance implemented in the random forest. Particularly, answers regarding financial and occupational concern, psychological distress, and current well-being were decisive for the assignment to a cluster. In summation, the clustering approach can identify persons with weakened psychological protective factors allowing them to provide tailored recommendations for preventive actions based on the cluster affiliation, e.g., via a web application.