Agent based Simulation Modeling and Application of MAS in Health Care: Prediction of Corona using Euclidean Distance

Coronavirus disease (COVID-19) represents one of the most infectious diseases of the 21st century, significantly impacting public health and societal structures. This study introduces an innovative approach to simulating the virus's spread using a Multi-Agent System, showcasing how the dynamics of infection can be effectively modeled through Euclidean distance. By leveraging Intelligent Agents, which possess properties such as communication and reactivity, we can gain deeper insights into the mechanisms driving transmission. In this work, we create a unique simulation framework that represents a population of one thousand individuals, with one agent initially infected. The simulation not only highlights the role of spatial relationships but also incorporates factors such as resistance and recovery of infection dynamics. To enhance accessibility and reduce memory consumption, we visualize the simulation through an image viewer instead of traditional video formats. Implemented in Jupyter Notebook, this Multi-Agent Simulation provides an interactive platform for exploring the complexities of viral transmission, paving the way for future research and applications in epidemic modeling. This novel approach emphasizes the potential of agent-based modeling applied in healthcare.