There are various methods to measure parameter sensitivity, including:
One-at-a-time (OAT) Sensitivity Analysis: This involves changing one parameter at a time while keeping the others constant to observe changes in the model output. Global Sensitivity Analysis: This method considers the simultaneous variation of all parameters to understand their combined effect on the model output. Partial Rank Correlation Coefficient (PRCC): This statistical method assesses the relationship between input parameters and model output, taking into account the influence of other parameters.
