Susceptible Infected

Susceptible-Infected (SI) models, often extended to include additional compartments like recovered or deceased individuals (SIR, SIRD, SVEIDR, etc.), are fundamental epidemiological tools used to understand and predict infectious disease spread. Current research focuses on improving the accuracy and adaptability of these models, employing techniques like hybrid approaches combining ordinary differential equations with machine learning algorithms (e.g., deep learning, neural networks, particle swarm optimization) to account for non-stationary patterns, policy interventions, and variant-specific transmission rates. This work aims to enhance the predictive power of these models for better pandemic preparedness and response, offering valuable insights for public health decision-making and resource allocation.