Bayesian Identifiability Analysis for Infectious Disease Models: Parameter Reduction and Model Selection

Key Points

• Identifiability analysis demonstrates improved understanding of parameters in infectious disease models, providing clarity.
• Key evidence shows that leveraging Bayesian methods leads to more robust models with reduced parameters.
• Method involves a Bayesian identifiability analysis, focusing on parameter selection and model effectiveness in infectious disease contexts.
• Highlights the importance of refined models for better predictions in public health responses to infectious diseases.