TY - JOUR

T1 - A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology

AU - Piazzola, Chiara

AU - Tamellini, Lorenzo

AU - Tempone, Raul

N1 - KAUST Repository Item: Exported on 2020-11-24
Acknowledged KAUST grant number(s): URF/1/2584-01-01
Acknowledgements: The authors acknowledge the many fruitful discussions with several colleagues, and in particular the colleagues at CNR-IMATI that participated in the COVID-19 modeling study group.

PY - 2020/11

Y1 - 2020/11

N2 - We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.

AB - We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.

UR - http://hdl.handle.net/10754/664659

UR - https://linkinghub.elsevier.com/retrieve/pii/S0025556420301590

U2 - 10.1016/j.mbs.2020.108514

DO - 10.1016/j.mbs.2020.108514

M3 - Article

C2 - 33217409

SP - 108514

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

ER -