Data Collection: Gather accurate and comprehensive epidemiological data. Model Selection: Choose an appropriate model based on the nature of the disease and the available data. Parameter Estimation: Use statistical methods to estimate the parameters of the model. This often involves techniques like Maximum Likelihood Estimation (MLE) or Bayesian Inference. Model Validation: Compare the model's predictions with real-world data to assess its accuracy. Refinement: Adjust the model parameters or structure to improve its fit to the data.
