The construction of a simulation model involves several steps:
Define Objectives: Clearly state the goals of the simulation, such as predicting the number of cases or evaluating intervention effectiveness. Data Collection: Gather relevant data, including epidemiological parameters (e.g., transmission rates, recovery rates) and demographic information. Model Selection: Choose an appropriate model type (e.g., deterministic, stochastic) based on the objectives and available data. Parameter Estimation: Use statistical methods to estimate model parameters from the collected data. Model Implementation: Develop the model using computational tools and software (e.g., R, Python, AnyLogic). Validation: Validate the model by comparing its outputs to real-world data or using cross-validation techniques. Sensitivity Analysis: Assess the model's sensitivity to changes in parameters to understand the robustness of the results. Scenario Analysis: Run different scenarios to explore various outcomes and inform decision-making.
