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Published on: January 31, 2014
Multi-method global sensitivity analysis of mathematical models
An Dela1, Blerta Shtylla2, Lisette de Pillis3
1Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA.
This study introduces a multi-method framework for parameter-sensitivity analysis, comparing Sobol's method, DGSM, and a new MeFAST implementation. The framework aids in selecting appropriate sensitivity analysis techniques for various models, enhancing decision-making.
Area of Science:
- Computational modeling
- Mathematical biology
- Sensitivity analysis
Background:
- Parameter-sensitivity analysis is crucial for model understanding but selecting methods is challenging.
- Existing methods have limitations, necessitating a comparative approach.
Purpose of the Study:
- To present a novel multi-method framework for global sensitivity analysis.
- To compare DGSM, Sobol's method, and a new MeFAST implementation.
- To provide a computational tool for informed method selection.
Main Methods:
- Incorporation of three global sensitivity analysis methods: DGSM, Sobol's method, and MeFAST (an enhanced eFAST implementation).
- Development of MATLAB codes and a hyper-parameter tuning guide.
- Demonstration using mathematical models of HIV disease progression and tumor growth.
Main Results:
- A computational framework generating comparative visualizations of DGSM, Sobol, and MeFAST outputs.
- Demonstration of the workflow on two distinct mathematical models.
- Facilitation of informed decisions regarding parameter influence on model outcomes.
Conclusions:
- The presented framework enables a comprehensive comparison of different sensitivity analysis techniques.
- This facilitates a more informed selection of methods based on model characteristics and analysis goals.
- The tool aids researchers in identifying key model parameters more effectively.
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