Several types of models are used to predict disease outbreaks:
Statistical models: Use historical data to identify patterns and trends in disease occurrence. Mathematical models: Such as the SIR (Susceptible-Infectious-Recovered) model, which simulates the spread of infections in a population. Machine learning models: Utilize algorithms to analyze large datasets and identify complex relationships between variables. Agent-based models: Simulate interactions between individuals within a population to understand how behaviors influence disease spread.
