Profile likelihood-based analyses of infectious disease models.

Area of Science:

• Epidemiology
• Mathematical Biology
• Systems Biology

Background:

• Ordinary differential equation (ODE) models are widely used for epidemic temporal dynamics.
• ODE models are also prevalent in diverse scientific disciplines, including Systems Biology.
• Existing ODE modeling approaches can be enhanced by cross-disciplinary transfer of methods.

Purpose of the Study:

• To adapt and apply state-of-the-art Systems Biology approaches to infectious disease modeling.
• To demonstrate the utility of these advanced methods using real-world epidemic data.
• To perform comprehensive model analysis including parameter estimation, identifiability, and reduction.

Main Methods:

• Utilized a simple SIR (Susceptible-Infectious-Recovered) model for an influenza outbreak.
• Employed a complex model for a vector-borne disease, exemplified by the Zika virus outbreak in Colombia.
• Applied deterministic multistart optimization for parameter estimation and profile likelihood for identifiability and model reduction.
• Leveraged the Data2Dynamics modeling framework for analyses.

Main Results:

• Successfully applied cross-disciplinary methods to infectious disease ODE models.
• Demonstrated robust parameter estimation and identifiability analysis for both simple and complex models.
• Showcased model reduction techniques to simplify complex epidemiological models.
• Validated the effectiveness of the Data2Dynamics framework in comparative challenges.

Conclusions:

• Transferring methods from Systems Biology significantly enhances infectious disease modeling capabilities.
• Advanced analytical techniques like profile likelihood are crucial for robust model development and interpretation.
• The Data2Dynamics framework provides a powerful, validated platform for epidemiological modeling and analysis.