Abstract
SaSAT (Sampling and Sensitivity Analysis Tools) is a user-friendly software package for applying uncertainty and sensitivity analyses to mathematical and computational models of arbitrary complexity and context. The toolbox is built in Matlab®, a numerical mathematical software package, and utilises algorithms contained in the Matlab® Statistics Toolbox. However, Matlab® is not required to use SaSAT as the software package is provided as an executable file with all the necessary supplementary files. The SaSAT package is also designed to work seamlessly with Microsoft Excel but no functionality is forfeited if that software is not available. A comprehensive suite of tools is provided to enable the following tasks to be easily performed: efficient and equitable sampling of parameter space by various methodologies; calculation of correlation coefficients; regression analysis; factor prioritisation; and graphical output of results, including response surfaces, tornado plots, and scatterplots. Use of SaSAT is exemplified by application to a simple epidemic model. To our knowledge, a number of the methods available in SaSAT for performing sensitivity analyses have not previously been used in epidemiological modelling and their usefulness in this context is demonstrated.
Highlights
Mathematical and computational models today play a key role in almost every branch of science
Latin hypercube sampling (LHS), a type of stratified Monte Carlo sampling [2,3] that is an extension of Latin Square sampling [4,5] first proposed by McKay at al. [6] and further developed and introduced by Iman et al [1,2,3], is a sophisticated and efficient method for achieving equitable sampling of all predictors simultaneously
Sensitivity analyses The 'Sensitivity Analysis Utility' provides a suite of powerful sensitivity analysis tools for calculating: 1) Pearson Correlation Coefficients, 2) Spearman Correlation Coefficients, 3) Partial Rank Correlation Coefficients, 4) Unstandardized Regression, 5) Standardized Regression, 6) Logistic Regression, 7) Kolmogorov-Smirnov test, and 8) Factor Prioritization by Reduction of Variance
Summary
Mathematical and computational models today play a key role in almost every branch of science. Sensitivity analyses The 'Sensitivity Analysis Utility' (interface shown in Figure 4c) provides a suite of powerful sensitivity analysis tools for calculating: 1) Pearson Correlation Coefficients, 2) Spearman Correlation Coefficients, 3) Partial Rank Correlation Coefficients, 4) Unstandardized Regression, 5) Standardized Regression, 6) Logistic Regression, 7) Kolmogorov-Smirnov test, and 8) Factor Prioritization by Reduction of Variance The results of these analyses can be shown directly on the screen, or saved to a file for later inspection allowing users to identify key relationships between parameters and outcome variables. SFcigatuterer p9lots comparing the total number of infections (log scale) against each parameter Scatter plots comparing the total number of infections (log scale) against each parameter: (a) τ1, shows some weak correlation, (b) τ2, shows little or no correlation, and (c) λ, showing a strong correlation (see Table 2 for correlation coefficients)
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