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Related Experiment Videos

Sampling and sensitivity analyses tools (SaSAT) for computational modelling.

Alexander Hoare1, David G Regan, David P Wilson

  • 1National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Sydney, New South Wales, 2010, Australia. ahoare@nchecr.unsw.edu.au

Theoretical Biology & Medical Modelling
|February 29, 2008
PubMed
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SaSAT is a user-friendly software package for uncertainty and sensitivity analysis in computational models. It offers efficient sampling, correlation, regression, and graphical outputs, demonstrated with an epidemic model.

Area of Science:

  • Computational modeling
  • Mathematical analysis
  • Software development

Background:

  • Uncertainty and sensitivity analyses are crucial for understanding complex models.
  • Existing tools may have limitations in flexibility or accessibility.
  • A need exists for user-friendly, versatile software for model analysis.

Purpose of the Study:

  • To introduce SaSAT (Sampling and Sensitivity Analysis Tools), a novel software package.
  • To provide a comprehensive suite of tools for uncertainty and sensitivity analyses.
  • To demonstrate the utility of SaSAT, including novel methods, in epidemiological modeling.

Main Methods:

  • SaSAT utilizes algorithms from the Matlab Statistics Toolbox but runs as a standalone executable.
  • It offers efficient parameter space sampling methodologies.

Related Experiment Videos

  • Includes tools for correlation coefficients, regression analysis, factor prioritization, and graphical outputs.

    • Main Results:

    • SaSAT provides a user-friendly interface for complex model analysis.
    • The software seamlessly integrates with Microsoft Excel.
    • Application to an epidemic model demonstrates the effectiveness of SaSAT's tools, including novel sensitivity analysis methods.

    Conclusions:

    • SaSAT is a versatile and accessible tool for uncertainty and sensitivity analyses.
    • Its application in epidemiological modeling highlights the potential of its methods.
    • The software facilitates a deeper understanding of computational models across various disciplines.