MONDAY, SEPTEMBER 26, 2016

Scientists advance parameter selection, verification techniques in HIV model

Biological and physical models, such as the HIV model, often have a large number of parameters and initial conditions that cannot be measured directly. | File photo
In a paper recently published in SIAM Journal on Uncertainty Quantification, Mami Wentworth, Ralph Smith and H.T. Banks used robust parameter selection and verification techniques on a dynamic HIV model.

"Biological and physical models, such as the HIV model, often have a large number of parameters and initial conditions that cannot be measured directly, and instead must be inferred through statistical analysis," Smith said. "For this to be successfully accomplished, measured responses must adequately reflect changes in these inputs."

In their determination of the model’s influential factors, the authors were able to minimize the parameter dimensions for future uncertainty quantification by fixing the noninfluential parameters. They used global sensitivity analysis to garner information -- like identifying input subsets, fixing noninfluential inputs and pinpointing inputs with high potential to impact model response -- that allowed them to separate influential and noninfluential parameters.

"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," Smith said. "Noninfluential parameters are fixed at nominal values for subsequent analysis."

Through reducing the parameter dimensions, the areas of the model that impact HIV treatment plans can be more closely examined.

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