Leveraging SIR and Barabási-Albert Models for Epidemic Modelling

The Susceptible, Infected, and Recovered (SIR) model predicts the number of living beings in a population who are infected and recovering from a disease. This article addresses the critical challenge of modelling and simulating the spread of contagious diseases in a population. Drawing inspiration from global events like the COVID-19 pandemic, our proposed simulation aims to comprehensively understand the epidemic dynamics and thus enhances the public awareness for effective decision-making. The proposed simulation integrates the computational models and simulation techniques, including the logistic functions, agent-based models, SIR models, and network-based spread models.