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Practical identifiability of parametrised models: A review of benefits and limitations of various approaches

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  • Lam, Nicholas N.
  • Docherty, Paul D.
  • Murray, Rua

Abstract

This systematic review of practical identifiability (PI) explores the challenging issue of how parameter identification of models is affected by both experimental considerations and model structure. Structural identifiability (SI) analyses that yield binary assessment of parameter uniqueness have been historically dominant in the field. However, recent developments in the less explored PI domain have facilitated more nuanced estimates of identified model parameter trade-off and variance. As PI acknowledges variation in parameter estimates due to real-world limitations in data quality and quantity, it can both explore how parameters may trade-off, and guide more informative experimental design.

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  • Lam, Nicholas N. & Docherty, Paul D. & Murray, Rua, 2022. "Practical identifiability of parametrised models: A review of benefits and limitations of various approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 199(C), pages 202-216.
    • Handle: RePEc:eee:matcom:v:199:y:2022:i:c:p:202-216
    DOI: 10.1016/j.matcom.2022.03.020
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    4. Diana Paola Lizarralde-Bejarano & Daniel Rojas-Díaz & Sair Arboleda-Sánchez & María Eugenia Puerta-Yepes, 2020. "Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-29, March.
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    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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