Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Abstract

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. This study novelly integrates disease perception, represented by perceived severity, as a critical determinant of behavioral change into a data-driven compartmental model to assess its impact on disease spread. Using survey data, 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 demonstrate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, with high heterogeneity yielding contrasting effects. Longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Epidemiological curves reveal that differences in behavior among groups can eliminate a second infection peak, resulting in a higher first peak and overall more severe outcomes. The specific modeling approach 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 extensive data collection to enhance model accuracy and relevance.