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

Area of Science:

• Epidemiology
• Mathematical Modeling
• Behavioral Science

Background:

• Disease perception influences preventive behavior and epidemic dynamics.
• Existing models often lack real-world behavioral data on disease perception.
• Understanding risk perception is crucial for effective public health interventions.

Purpose of the Study:

• To integrate survey data on contact patterns and disease perception into a data-driven compartmental model.
• To investigate how perceived disease severity affects behavioral changes and epidemic trajectories.
• To analyze the interplay between disease spread, vaccination campaigns, and public behavior.

Main Methods:

• Developed a data-driven compartmental model incorporating age-stratified contact patterns.
• Integrated survey data on individuals' perceived disease severity.
• Simulated scenarios of competing COVID-19 waves and vaccination campaigns.
• Analyzed the impact of behavioral heterogeneities driven by perceived severity on epidemic curves.

Main Results:

• Behavioral differences based on perceived severity significantly alter epidemic dynamics.
• High perceived severity leads to sustained protective behaviors, benefiting vulnerable groups.
• Low perceived severity can result in premature behavior relaxation, accelerating spread.
• Behavioral heterogeneities can shift epidemic patterns from dual to single, higher peaks, increasing mortality.

Conclusions:

• Integrating disease perception and behavioral feedback into models is essential for accurate epidemic forecasting.
• Age-stratified contact data and disease perception feedback loops are key drivers of epidemic phenomenology.
• Model outcomes are robust to variations in perceived severity distribution, emphasizing the importance of behavioral heterogeneity.