Public efforts to reduce disease transmission implied from a spatial game

Area of Science:

• Epidemiology
• Computational modeling
• Behavioral economics

Background:

• Understanding disease transmission requires analyzing public behavior's impact on case rates.
• Previous models often lack spatial dynamics and detailed behavioral components.

Purpose of the Study:

• To develop a spatial game model of public behavior linked to disease dynamics.
• To investigate the influence of government interventions, wealth, and risk perception delays on disease spread.

Main Methods:

• A spatial infection-reducing game model was developed, equivalent to a Hopfield neural network coupled with SIRS disease dynamics.
• Model parameters were calibrated using geographical time series of COVID-19 (Coronavirus Disease 2019) active case numbers.
• The model was used to simulate and analyze the spatial and temporal effects of public behavior.

Main Results:

• The model successfully calibrated behavioral parameters to real-world COVID-19 data, creating a spatial history of behavior.
• Analysis revealed quantifiable effects of government interventions and wealth on public behavior and disease transmission.
• Simulations demonstrated that delayed risk perception can lead to behavioral instability and oscillations in infection rates.

Conclusions:

• The coupled game-disease model provides a robust framework for understanding and quantifying public behavior in disease transmission.
• The findings highlight the significant impact of behavioral factors, government policies, and socioeconomic status on epidemic dynamics.
• The study underscores the critical role of timely risk perception in maintaining behavioral stability and controlling infectious disease outbreaks.