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

Autor: Chiara Piazzola, Raul Tempone, Lorenzo Tamellini
Rok vydání: 2020
Předmět:
Statistics and Probability
FOS: Computer and information sciences
Dynamical systems theory
Computer science
Context (language use)
Bayesian inversion
Machine learning
computer.software_genre
Models
Biological
Mathematical modelling of infectious disease
General Biochemistry
Genetics and Molecular Biology
Methodology (stat.ME)
Modelling and Simulation
Dynamical systems
Humans
Uncertainty quantification
Fisher approximation
Quantitative Biology - Populations and Evolution
Statistics - Methodology
Simple (philosophy)
Model identifiability
Mathematical epidemiology
General Immunology and Microbiology
business.industry
Applied Mathematics
Populations and Evolution (q-bio.PE)
Uncertainty
COVID-19
General Medicine
Inverse problem
Modeling and Simulation
FOS: Biological sciences
Curve fitting
Identifiability
Uncertainty Quantification
Artificial intelligence
business
General Agricultural and Biological Sciences
Epidemiologic Methods
computer
Zdroj: Mathematical Biosciences
Mathematical biosciences 332 (2021): 108514. doi:10.1016/j.mbs.2020.108514
info:cnr-pdr/source/autori:C. Piazzola, L. Tamellini, and R. Tempone/titolo:A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology/doi:10.1016%2Fj.mbs.2020.108514/rivista:Mathematical biosciences/anno:2021/pagina_da:108514/pagina_a:/intervallo_pagine:108514/volume:332
ISSN: 1879-3134
Popis: 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.
Databáze: OpenAIRE
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