Chaotic and Oscillatory Behavior of an Epidemic: Agent-Based Model.

Area of Science:

• Epidemiology
• Computational modeling
• Complex systems

Background:

• Epidemic spread across interconnected populations is a significant public health concern.
• Mathematical models are crucial for understanding and predicting disease transmission dynamics.
• Existing continuous models may oversimplify complex population interactions.

Purpose of the Study:

• To compare the predictive capabilities of a continuous system dynamics model with an agent-based model for epidemic spread.
• To identify limitations of the continuous Susceptible-Infected-Recovered-Susceptible (SIRS) model in representing real-world epidemic scenarios.
• To highlight the advantages of agent-based approaches in capturing nuanced epidemic behaviors.

Main Methods:

• Development and analysis of a continuous system dynamics Susceptible-Infected-Recovered-Susceptible (SIRS) model.
• Implementation and simulation of an agent-based model to represent epidemic spread across distinct population centers.
• Comparative analysis of model outputs, focusing on emergent behaviors and underlying assumptions.

Main Results:

• The continuous SIRS model demonstrated limitations in accounting for population memory and spatial heterogeneity.
• The agent-based model exhibited chaotic behavior, suggesting a more realistic approximation of epidemic dynamics.
• Discrepancies highlight the importance of individual-level interactions and spatial structure in disease transmission.

Conclusions:

• Continuous SIRS models may not fully capture the complexity of epidemic spread in spatially distributed populations.
• Agent-based modeling offers a more robust framework for simulating epidemics, incorporating factors like collective memory and individual behavior.
• Further research into agent-based epidemic modeling is warranted for improved public health preparedness.