Development of a multi-parameter multi-agent system for modeling the dynamics of the spread of infectious diseases

Abstract

This paper presents an approach to modeling the dynamics of infectious disease spread using a multiparametric multi-agent system that integrates both biological and social factors. The main goal of the research is to develop a flexible simulation model that enables the analysis of epidemiological processes in urban environments and the evaluation of various intervention strategies. Each agent in the model represents an individual with personalized characteristics, including health status, daily mobility, social activity, and behavioral patterns. The core infection dynamics are based on an adapted SIR model, integrated into the agent-based framework, allowing probabilistic transitions between susceptible, infected, and recovered states depending on the nature of agent interactions. The implementation was carried out using Java and the MASON simulation platform, which supports high-resolution and scalable agent-based modeling. To enable interactive spatial analysis, the visualization module was developed using JavaFX and the GeoFX library. This combination allows for the dynamic rendering of agent locations, health states, and the spatial-temporal progression of the infection. The visualization not only enhances interpretability but also serves as a real-time analytical tool for assessing the effectiveness of preventive measures and containment policies. Simulation results demonstrate the model’s capacity to reflect complex patterns of infection spread, including the impact of initial infection placement and social interaction networks. The developed system shows the practical potential of combining agent-based simulation with geospatial visualization for epidemiological forecasting and decision-making in public health management