Inversion of a SIR-based model: a critical analysis about the application to COVID-19 epidemic

TL;DR

This paper critically examines the challenges of calibrating SIR models for COVID-19 using international data, highlighting the impact of data uncertainties on model reliability and prediction accuracy.

Contribution

It provides a numerical analysis of parameter calibration in SIR models, emphasizing the role of data quality and uncertainties in inverse modeling.

Findings

Calibration is sensitive to data uncertainties.

Data quality significantly affects model reliability.

Inverse problems in epidemiology require careful data consideration.

Abstract

Calibration of a SIR (Susceptibles-Infected-Recovered) model with official international data for the COVID-19 pandemics provides a good example of the difficulties inherent the solution of inverse problems. Inverse modeling is set up in a framework of discrete inverse problems, which explicitly considers the role and the relevance of data. Together with a physical vision of the model, the present work addresses numerically the issue of parameters calibration in SIR models, it discusses the uncertainties in the data provided by international authorities, how they influence the reliability of calibrated model parameters and, ultimately, of model predictions.