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Sensitivity analysis of infectious disease models: methods, advances and their application
Jianyong Wu1, Radhika Dhingra, Manoj Gambhir
1Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
Journal of the Royal Society, Interface
|July 19, 2013
Summary
Advanced sensitivity analysis (SA) methods offer deeper insights into infectious disease models than traditional approaches. Comparing five global SA techniques reveals varying strengths for identifying influential parameters and understanding model dynamics over time.
Area of Science:
- Epidemiology
- Computational Biology
- Mathematical Modeling
Background:
- Infectious disease modeling is crucial for understanding disease dynamics and informing public health interventions.
- Traditional sensitivity analysis (SA) methods in this field often provide limited insights into complex model behaviors.
- Advanced SA techniques have the potential to significantly enhance model optimization and parameter identification.
Purpose of the Study:
- To investigate and compare the utility of five global SA methods in infectious disease modeling.
- To detail the relative merits and pitfalls of each SA method when applied to microparasite and macroparasite models.
- To guide infectious disease modelers in selecting appropriate SA techniques for improved model performance and design.
Main Methods:
- Comparative analysis of five global SA methods: scatter plots, Morris, Sobol', Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC), and sensitivity heat map.
- Application of these methods to a microparasite (cholera) and a macroparasite (schistosomiasis) transmission model.
- Evaluation of the insights provided by each method regarding parameter influence and model output relationships.
Main Results:
- All investigated SA methods identified influential parameters, with some variations in specific findings.
- Classical SA methods showed limitations in quantifying parameter-output relationships over time.
- The sensitivity heat map method offered comprehensive insights into group sensitivity and dynamic parameter influence on all model state variables.
Conclusions:
- Advanced SA methods provide valuable, method-specific insights beyond traditional approaches for infectious disease modeling.
- The sensitivity heat map is particularly useful for understanding dynamic parameter sensitivity in epidemic models.
- A comparative framework is provided to assist modelers in choosing optimal SA techniques to enhance model performance and structure.
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