TY - JOUR
T1 - Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models
AU - Gallo, Luca
AU - Frasca, Mattia
AU - Latora, Vito
AU - Russo, Giovanni
N1 - Publisher Copyright:
Copyright © 2022 The Authors, some rights reserved.
PY - 2022/1
Y1 - 2022/1
N2 - Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these 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 coronavirus disease 2019 (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 affects 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. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.
AB - Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these 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 coronavirus disease 2019 (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 affects 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. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85123276225&partnerID=8YFLogxK
U2 - 10.1126/sciadv.abg5234
DO - 10.1126/sciadv.abg5234
M3 - Article
C2 - 35044820
AN - SCOPUS:85123276225
SN - 2375-2548
VL - 8
JO - Science Advances
JF - Science Advances
IS - 3
M1 - eabg5234
ER -