ArticleJournal of biological dynamics2023
A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.
Article in Journal of biological dynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.Bulletin of mathematical biology · 2026Article
- Impact of the first booster vaccine against SARS-CoV-2 in Chile.Infectious Disease Modelling · 2026Article
- A robust modeling framework to investigate transmissible diseases: COVID-19 in Chile as a case study.Frontiers in public health · 2025Article
- The effect of model structure and data availability on Usutu virus dynamics at three biological scales.Royal Society open science · 2024Article
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Authors and funding
4 authors.
Funding
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.
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