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Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.
Yuganthi R Liyanage1, Gerardo Chowell2,3, Gleb Pogudin4
1Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, Florida, USA.
Arxiv
|April 1, 2025
Summary
This study confirms that six common epidemiological growth models are structurally identifiable and practically robust for disease forecasting. Findings support their use in real-world public health scenarios.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Computational Biology
Background:
- Phenomenological models are crucial for disease forecasting when mechanisms are unknown.
- Model reliability hinges on parameter structural and practical identifiability.
- Existing models face challenges with non-integer exponents.
Purpose of the Study:
- To systematically analyze the identifiability of six common epidemiological growth models.
- To reformulate models for rigorous identifiability analysis.
- To validate findings using real-world epidemiological data.
Main Methods:
- Reformulated six growth models (GGM, GLM, Richards, GRM, Gompertz, modified SEIR) by adding state variables.
- Performed structural identifiability analysis using StructuralIdentifiability.jl (JULIA).
- Validated results via parameter estimation and forecasting with GrowthPredict (MATLAB) on monkeypox, COVID-19, and Ebola data.
- Assessed practical identifiability using Monte Carlo simulations under varying noise levels.
Main Results:
- All six models were confirmed as structurally identifiable after reformulation.
- Parameter estimates demonstrated practical identifiability and robustness across noise levels.
- Model and dataset specific sensitivities in parameter estimation were observed.
Conclusions:
- Phenomenological growth models are adaptable and reliable tools for disease dynamics forecasting.
- Identifiability analysis is critical for ensuring the validity of these models.
- The study provides insights into model selection and application for public health interventions.
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