Physical and biological models often have hundreds of inputs, many of which may have a negligible effect on a model's response. Establishing parameters that can be fixed at nominal values without significantly affecting model outputs is often challenging; sometimes these parameters cannot be simply discerned by the outputs. Thus, verifying that a parameter is noninfluential is both computationally challenging and quite expensive.
The authors implement global sensitivity analysis to identify input subsets, fix noninfluential inputs, and pinpoint those with the most potential to affect model response. "The role of global sensitivity analysis is to isolate those parameters that are influential and that can and must be inferred through a fit to data," says Smith. "Noninfluential parameters are fixed at nominal values for subsequent analysis." Discerning influential parameters from noninfluential ones enables the authors to reduce the parameter dimensions and look more closely at the portions of the model that affect HIV treatment plans.
Determining their model's influential factors allow Wentworth et al. to fix the noninfluential parameters and minimize the parameter dimensions for future uncertainty quantification. Their selection techniques are essential in regulating better control for drug therapy. Ultimately, the authors seek to better comprehend HIV dynamics and eventually establish optimal treatment strategies. They chose to employ an HIV model because of Banks' familiarity with the model type. "He developed it and has substantial experience employing it to characterize HIV dynamics and develop potential treatment regimes," says Smith.
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Source article: Parameter Selection and Verification Techniques Based on Global Sensitivity Analysis Illustrated for an HIV Model. SIAM Journal on Uncertainty Quantification . (To be published).
The paper will publish online March 31st. Email sorg@siam.org to obtain an advance copy of the paper.
SIAM/ASA Journal on Uncertainty Quantification