Parameter estimation in dynamical systems via Statistical Learning: a reinterpretation of Approximate Bayesian Computation applied to COVID-19 spread

TL;DR

This paper introduces a flexible, robust parameter estimation method for dynamical systems, reinterpreting Approximate Bayesian Computation through Statistical Learning, with applications to COVID-19 epidemiological modeling.

Contribution

It presents a general, property-agnostic approach to parameter estimation that enhances qualitative analysis of disease spread dynamics using statistical learning techniques.

Findings

Effective estimation of COVID-19 parameters from real data

Ability to assess intervention impacts on disease spread

Qualitative insights into future epidemic evolution

Abstract

We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. The method is quite general and flexible, since it does not rely on any specific property of the dynamical system, and represents a reinterpretation of Approximate Bayesian Computation methods through the lens of Statistical Learning. The method is specially useful for estimating parameters in epidemiological compartmental models in order to obtain qualitative properties of a disease evolution. We apply it to simulated and real data about COVID-19 spread in the US in order to evaluate qualitatively its evolution over time, showing how one may assess the effectiveness of measures implemented to slow the spread and some qualitative features of the disease current and future evolution.