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

Embargo End Date
2021-11-01

Type
Article

Authors
Piazzola, Chiara
Tamellini, Lorenzo
Tempone, Raul

KAUST Department
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

KAUST Grant Number
URF/1/2584-01-01

Preprint Posting Date
2020-08-04

Date
2020-11

Submitted Date
2020-08-04

Abstract
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.

Citation
Piazzola, C., Tamellini, L., & Tempone, R. (2020). A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology. Mathematical Biosciences, 108514. doi:10.1016/j.mbs.2020.108514

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.
Lorenzo Tamellini and Chiara Piazzola have been supported by the PRIN 2017 project 201752HKH8 “Numerical Analysis for Full and Reduced Order Methods for the efficient and accurate solution of complex systems governed by Partial Differential Equations (NA-FROM-PDEs)”. Lorenzo Tamellini also acknowledges the support of GNCS-INdAM (Gruppo Nazionale Calcolo Scientifico - Istituto Nazionale di Alta Matematica). This work was supported by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/2584-01-01 and the Alexander von Humboldt foundation. Raúl Tempone is a member of the KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering.

Publisher
Elsevier BV

Journal
Mathematical Biosciences

DOI
10.1016/j.mbs.2020.108514

PubMed ID
33217409

arXiv
2008.01400

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0025556420301590

Permanent link to this record

Version History

Now showing 1 - 2 of 2
VersionDateSummary
2*
2020-11-23 12:19:02
Published in journal
2020-08-18 13:29:38
* Selected version