Forecasting in epidemiology involves multiple steps and methods:
1. Data Collection: Gathering high-quality data is the first step. This includes historical data on disease incidence, population demographics, and environmental conditions. 2. Model Selection: Choosing the right model is crucial. Common models include SIR models, SEIR models, and agent-based models. 3. Parameter Estimation: Estimating the parameters that govern the model dynamics using statistical techniques like maximum likelihood estimation or Bayesian inference. 4. Validation and Calibration: Ensuring the model accurately reflects real-world scenarios by comparing it with historical data. 5. Simulation and Prediction: Running the model to generate forecasts and evaluate different scenarios.
