Compartmental Model

Compartmental models are mathematical frameworks used to simulate the dynamics of systems by dividing them into interacting compartments, often applied to epidemiology and pharmacokinetics. Current research focuses on improving the accuracy and applicability of these models by incorporating individual heterogeneity (e.g., through mobility data or agent-based modeling), leveraging machine learning techniques (like neural networks and Gaussian processes) for parameter estimation and prediction, and addressing challenges like parameter identifiability and uncertainty quantification. These advancements enhance the predictive power and explanatory capabilities of compartmental models, leading to improved disease forecasting, drug development, and resource allocation strategies in public health.