Simply said, sensitivity is how much an output can change when an input is perturbed, but there is a whole science behind sensitivity analyses with mathematical, statistics and probabilistic principles. Practically, sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and in verifying and validating a model. Can you imagine if we knew how sensitive COVID predictions are to specific assumptions and focused on reducing uncertainty in such inputs? We identified ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method. These methods were compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Was this research helpful? Well, it has been cited 800+ times since published and a seminal paper in sensitivity analysis field.
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