Several methods are employed for disease prediction, including:
- Statistical Models: These involve traditional regression models, time-series analyses, and survival analysis to understand disease patterns. - Machine Learning: Techniques like decision trees, random forests, and neural networks can uncover complex patterns in large datasets. - Simulation Models: These include agent-based models and compartmental models (e.g., SIR models) that simulate disease spread within a population.
