Individuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose a way to integrate sur- vey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results indicate that behavioral het- erogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while on the other hand premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior among groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters does not strongly impact the model’s outcomes. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and highlights the necessity of further extensive data collection to enhance model accuracy and relevance.

Link to the preprint version of the paper: https://www.medrxiv.org/content/10.1101/2024.04.10.24305600v1