The generation of synthetic data involves various techniques, including:
Statistical Methods: Techniques such as multivariate normal distributions and bootstrapping can generate synthetic datasets that retain the statistical properties of the original data. Machine Learning: Algorithms like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can create highly realistic synthetic data. Agent-based Modeling: This approach simulates the interactions of individuals within a population, providing insights into disease dynamics and intervention strategies.
