A modelling approach to characterise the interaction between behavioral response and epidemics: A study based on...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Behavioral Science

Background:

• Human behavior is a critical factor influencing epidemic transmission dynamics.
• Understanding the feedback between behavioral responses and disease spread is essential for effective outbreak management.

Purpose of the Study:

• To investigate the feedback loop between behavioral changes and epidemic dynamics.
• To develop a model that incorporates time-varying human behavior and risk perception.
• To analyze the impact of behavioral responses on disease transmission patterns.

Main Methods:

• Developed a SIR-β type model with an effective contact rate (β) that varies with population risk perception.
• Utilized a particle swarm optimization algorithm for model calibration.
• Validated the model using time series data of COVID-19 outbreaks with diverse infection peak shapes.

Main Results:

• The proposed model successfully captures epidemic trends, including varied infection peak shapes.
• The model demonstrates that behavioral changes can lead to oscillatory disease prevalence over time.
• Different levels of behavioral response were shown to influence the magnitude and pattern of infection waves.

Conclusions:

• Incorporating dynamic behavioral responses into epidemic models is crucial for accurate predictions.
• Population behavioral changes can drive recurrent waves of infection during outbreaks.
• The model provides insights into managing epidemics by understanding and influencing public behavior.