A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile. (English) Zbl 1528.92036
In this paper a computational approach developed earlier by K. Roosa and G. Chowell [“Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models”, Theor. Biol. Med. Modelling 16, No. 1, 15 p. (2019; doi:10.1186/s12976-018-0097-6)] for simple epidemic models is adapted to a complex compartmental model describing the spread of COVID-19 in Chile, for which the authors propose a methodology to analyse the structural and practical parameter identifiability. The parameter uncertainty is studied computationally using a parametric bootstrapping approach. Synthetic data generated with the same model were used to measure the capability of the model to recover the parameters assumed to satisfy the structural and practical identifiability. In the second part of the method, the results obtained from synthetic data are compared to parameter sets from regional Chilean epidemic data. The authors find loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods. The results highlight the importance of investigating the identifiability of model parameters through structural and practical identifiability analysis to avoid misleading model inferences during an epidemic outbreak.
Reviewer: Attila Dénes (Szeged)
Keywords:
COVID-19 model; dynamical quarantines; parameter estimation; identifiability of parameters; basic reproduction number; simulated annealingReferences:
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