SIR Model

The Susceptible-Infected-Recovered (SIR) model is a fundamental epidemiological framework used to simulate the spread of infectious diseases, primarily aiming to understand and predict outbreak dynamics. Current research focuses on extending the basic SIR model to incorporate factors like misinformation spread, regional interactions (using techniques like Universal Differential Equations and neural networks), individual heterogeneity, and the interplay between public health interventions and economic stability (often employing reinforcement learning). These advancements enhance the model's accuracy and applicability, improving predictions and informing policy decisions related to disease control and resource allocation. The resulting insights are crucial for managing public health crises and mitigating the impact of infectious diseases.