Disease Dynamic

Disease dynamic research aims to understand and predict the spread of infectious diseases, primarily to inform public health interventions. Current efforts focus on developing and refining sophisticated models, including compartmental models (e.g., SIR, SEIR), agent-based models, and hybrid approaches integrating these with machine learning techniques like neural networks (e.g., RNNs, CNNs, PINNs) and graph neural networks. These advancements enable more accurate simulations of disease spread, considering factors like individual behavior, geographical location, and intervention strategies, ultimately improving pandemic preparedness and response.