A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile

Abstract

A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the 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.

Keywords:

• COVID-19 model
• dynamical quarantines
• parameter estimation
• identifiability of parameters
• basic reproduction number
• simulated annealing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data and materials

The data were recorded by the Chilean Government [24], which can be accessed via a GitHub repository supported by Ministery of Science, Technology, Knowledge, and Innovation of Chile [41].

Additional information

Funding

RB is supported by project MATH-Amsud 22-MATH-05 ‘NOTION: NOn-local conservaTION laws for engineering, biological and epidemiological applications: theoretical and numerical’ and from ANID (Chile) through Fondecyt project 1210610; Anillo project ANID/ACT210030; Centro de Modelamiento Matemático (CMM), project FB210005 of BASAL funds for Centers of Excellence; and Centro de Recursos Hídricos para la Agricultura y la Minería (CRHIAM), project ANID/FONDAP/15130015. GC is partially supported by National Science Foundation (NSF) grants #2026797, #2034003, and National Institutes of Health (NIH) R01 GM 130900, and by project ANID/PCI/MEC80170119. IK would like to thank the German Research Foundation (DFG) for financial support of the project within the Cluster of Excellence ‘Data-Integrated Simulation Science’ (EXC 2075), DFG Project 432343452. LYLD is supported by Agencia Nacional de Investigación y Desarrollo (ANID) scholarship ANID-PCHA/Doctorado Nacional/2019-21190640 and Centro de Modelamiento Matemático (CMM), project FB210005 of BASAL funds for Centers of Excellence.