During the COVID-19 pandemic, public institutions and media outlets referred to possible severity levels of COVID-19 with a range of category labels. The most commonly used distinction in Germany was “mild” versus “severe”. “Mild” COVID-19 includes a wide variety of symptoms such as fever, loss of smell, and vomiting. However, laypeople who encounter the term “mild” may underestimate disease severity and as a result may be less likely to take protective measures against the disease. One way to avoid misinterpretations of disease severity labels is to provide explanations about the possible consequences of each severity level – a method used in meteorological forecasting and referred to as “impact-based communication”.
In an online experiment, we plan to investigate how providing a list of possible symptoms of the disease in addition to the severity label (vs. label only) influences perceived risk of the disease and the likelihood to take up protective measures against it, e.g., taking a preventive medication. Furthermore, we investigate how people interpret disease severity levels by having them match a variety of symptoms to what they think is the appropriate severity category. Finally, we plan to investigate how previous knowledge and susceptibility beliefs about a certain disease influence the effects by comparing a condition in which we specify the disease as COVID-19 to a neutral condition in which we simply use the term “respiratory disease”.
The study aims to provide a better understanding about how severity labels are interpreted by non-experts and ultimately improve future communication of disease severity.