Epidemic Simulation

Epidemic simulation uses computational models to study disease spread, aiming to understand transmission dynamics and inform public health interventions. Current research emphasizes agent-based models, often incorporating realistic geographic and socioeconomic data to simulate individual behaviors and interactions, alongside advancements in algorithms for efficient and accurate simulation of both Markovian and non-Markovian processes. These models are increasingly integrated with machine learning techniques for improved forecasting and source detection, and are being developed with a focus on privacy-preserving methods to enable the use of real-world data. The resulting simulations provide valuable tools for evaluating public health strategies and designing more effective interventions.