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

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.

References in corpus (2)

• Inferring the COVID-19 infection curve in Italy
• Is Time to Intervention in the COVID-19 Outbreak Really Important? A Global Sensitivity Analysis Approach

Cited by in corpus (4)

• Limits of epidemic prediction using SIR models
• SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy
• Accurately summarizing an outbreak using epidemiological models takes time
• A Mathematical Dashboard for the Analysis of Italian COVID-19 Epidemic Data