A computational approach is adapted to analyze the parameteridentifiability of a compartmental model. The model is intended todescribe the progression of the COVID-19 pandemic in Chile duringthe initial phase in early 2020 when government declared quarantinemeasures. The computational approach to analyze the structural andpractical identifiability is applied in two parts, one for synthetic dataand another for some Chilean regional data. The first part definesthe identifiable parameter sets when these recover the true param-eters used to create the synthetic data. The second part comparesthe results derived from synthetic data, estimating the identifiableparameter sets from regional Chilean epidemic data. Experimentsprovide evidence of the loss of identifiability if some initial conditionsare estimated, the period of time used to fit is before the peak, andif a significant proportion of the population is involved in quarantineperiods.ARTICLE HISTORYReceived 29 December 2022Accepted 30 August 2023KEYWORDSCOVID-19 model; dynamicalquarantines; parameterestimation; identifiability ofparameters; basicreproduction number;simulated annealing1. Introduction1.1. ScopeThe COVID-19 pandemic has received overwhelming news coverage worldwide. We recallthat the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syn-drome coronavirus 2 (SARS-CoV-2), was declared a global pandemic by the World HealthOrganization (WHO) on March 11, 2020 [11,64]. This highly contagious unprecedentedvirus has impacted government and public institutions, strained the health care systems,restricted people in their homes, and caused country-wide lockdowns resulting in a globaleconomic crisis [20,32,61].TheimpactofCOVID-19atthetimeofrevisionofthiswork (July 5, 2023) amounts to roughly 767 million cases moreover, 6.95 million deathsworldwide, of which 2.96 million have been reported in the Americas.CONTACTLeidy Yissedt Lara-Díazlelara@ucm.clSupplemental data for this article can be accessed here.https://doi.org/10.1080/17513758.2023.2256774© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, providedthe original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in arepository by the author(s) or with their consent.