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Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Computational Biology

Background:

  • Phenomenological models are crucial for disease dynamics forecasting, especially with limited mechanistic data.
  • Model reliability hinges on the structural and practical identifiability of parameters.
  • Existing models often face challenges with non-integer exponents, impacting analysis.

Purpose of the Study:

  • To systematically analyze the identifiability of six prevalent epidemiological growth models.
  • To address challenges in models with non-integer exponents through reformulation.
  • To validate identifiability and robustness using real-world epidemiological data.

Main Methods:

  • Reformulated six growth models (GGM, GLM, Richards, GRM, Gompertz, modified SEIR) by introducing state variables.
  • Conducted structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA.
  • Validated results via parameter estimation and forecasting with the GrowthPredict MATLAB Toolbox on monkeypox, COVID-19, and Ebola data.
  • Assessed practical identifiability using Monte Carlo simulations to evaluate robustness against observational noise.

Main Results:

  • All six reformulated models were confirmed as structurally identifiable.
  • Parameter estimates demonstrated practical identifiability and robustness across varying noise levels.
  • Model and dataset specific sensitivities in parameter estimation were observed.

Conclusions:

  • The study validates the structural identifiability and practical robustness of key phenomenological growth models for epidemic forecasting.
  • These models are adaptable to real-world data complexities and noise.
  • Findings support the use of these models for informing public health strategies and interventions.