Structural and practical identifiability analysis of outbreak models

Mathematical Biosciences
Necibe Tuncer, Trang T Le

Abstract

Estimating the reproduction number of an emerging infectious disease from an epidemiological data is becoming more essential in evaluating the current status of an outbreak. However, these studies are lacking the fundamental prerequisite in parameter estimation problem, namely the structural identifiability of the epidemic model, which determines the possibility of uniquely determining the model parameters from the epidemic data. In this paper, we perform both structural and practical identifiability analysis to classical epidemic models such as SIR (Susceptible-Infected-Recovered), SEIR (Susceptible-Exposed-Infected-Recovered) and an epidemic model with the treatment class (SITR). We performed structural identifiability analysis on these epidemic models using a differential algebra approach to investigate the well-posedness of the parameter estimation problem. Parameters of these models are estimated from different data types, namely prevalence, cumulative incidences and treated individuals. Furthermore, we carried out practical identifiability analysis on these models using Monte Carlo simulations and Fisher's Information Matrix. Our study shows that the SIR model is both structurally and practically identifiable from the pre...Continue Reading

Citations

Dec 17, 2020·Journal of the Royal Society, Interface·Alexander P BrowningMatthew J Simpson
Apr 9, 2021·Journal of Biological Dynamics·Joceline Lega
Dec 29, 2020·Annual Reviews in Control·Gemma MassonisAlejandro F Villaverde
Jan 5, 2022·Journal of Mathematical Biology·Marissa RenardyMarisa Eisenberg

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