Several types of models are used in epidemiological forecasting:
1. Compartmental Models: These include SIR (Susceptible, Infected, Recovered) and SEIR (Susceptible, Exposed, Infected, Recovered) models. They divide the population into compartments and use differential equations to describe the flow between them. 2. Statistical Models: These models rely on statistical methods to identify patterns and trends in the data. Examples include time-series analysis and regression models. 3. Agent-Based Models: These simulate the actions and interactions of individual agents to assess their effects on the system as a whole. They are particularly useful for modeling complex behaviors and interventions. 4. Machine Learning Models: These use algorithms to automatically learn patterns from the data. Techniques like neural networks and random forests have been increasingly applied in epidemiological forecasting.
