Modeling COVID-19 Transmission Dynamics With Self-Learning Population Behavioral Change.

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Recurrent COVID-19 outbreaks occurred after easing social distancing.
• Population behavior significantly influences pandemic spread and prediction.
• Understanding behavioral responses is key to controlling infectious diseases.

Purpose of the Study:

• To develop a SEAIR model integrating population behavioral changes for COVID-19 transmission dynamics.
• To analyze the feedback loop between societal behavior and disease spread.
• To investigate the impact of perceived infection costs and information delay on epidemic waves.

Main Methods:

• Developed a compartmental SEAIR (Susceptible-Exposed-Asymptomatic-Infectious-Removed) model.
• Incorporated population groups with distinct social behaviors responding to infection data.
• Simulated COVID-19 dynamics using Hong Kong data.

Main Results:

• Transmission rates are influenced by population behavioral shifts, creating a feedback loop.
• Perceived costs of infection and information delays significantly alter epidemic wave characteristics.
• Model simulations illustrate the complex interplay between behavior and disease transmission.

Conclusions:

• Population behavior is a critical determinant of COVID-19 transmission dynamics.
• The SEAIR model provides insights into managing future outbreaks by considering behavioral factors.
• Information delay and perceived infection costs are key parameters for pandemic control strategies.