Epidemiological Model

Epidemiological models aim to simulate and predict the spread of infectious diseases, guiding public health interventions. Current research heavily utilizes machine learning, incorporating techniques like graph neural networks, deep learning (including LSTMs and recurrent Q-networks), and transformer models to improve prediction accuracy and handle complex, non-stationary patterns in disease transmission. These advancements allow for more nuanced modeling of heterogeneous populations, incorporating factors like age, vaccination status, and mobility patterns, and optimizing control policies through reinforcement learning to balance health and economic impacts. The resulting models offer valuable tools for forecasting outbreaks, evaluating intervention strategies, and informing resource allocation during public health emergencies.