Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models

2020/12/01 by Luca Gallo, Gallo, Luca, Mattia Frasca +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · #COVID-19 epidemiological studies #Complex Systems and Time Series Analysis #Evolution and Genetic Dynamics #FOS: Physical sciences #Physics and Society (physics.soc-ph)

paper · pdf · doi:10.48550/arxiv.2012.00443

openalex publication_date 2020/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of such models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of COVID-19, they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data impacts the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Finally, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable forecasting of the epidemic dynamics.

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