tetano
Editor, Senior Moderator
Sci Adv
. 2022 Jan 21;8(3):eabg5234.
doi: 10.1126/sciadv.abg5234. Epub 2022 Jan 19.
Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models
Luca Gallo[SUP] 1 2 [/SUP], Mattia Frasca[SUP] 3 4 [/SUP], Vito Latora[SUP] 1 2 5 6 [/SUP], Giovanni Russo[SUP] 7 [/SUP]
Affiliations
Abstract
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.
. 2022 Jan 21;8(3):eabg5234.
doi: 10.1126/sciadv.abg5234. Epub 2022 Jan 19.
Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models
Luca Gallo[SUP] 1 2 [/SUP], Mattia Frasca[SUP] 3 4 [/SUP], Vito Latora[SUP] 1 2 5 6 [/SUP], Giovanni Russo[SUP] 7 [/SUP]
Affiliations
- PMID: 35044820
- DOI: 10.1126/sciadv.abg5234
Abstract
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.
