Parameter estimation in dynamical systems via Statistical Learning: a reinterpretation of Approximate Bayesian Computation applied to COVID-19 spread
This provides a flexible method for estimating parameters in compartmental models to analyze disease spread, though it appears incremental as it reinterprets existing techniques.
The authors tackled the problem of parameter estimation in dynamical systems, particularly for epidemiological models, by proposing a reinterpretation of Approximate Bayesian Computation through Statistical Learning, and applied it to COVID-19 spread data in the US to assess intervention effectiveness and disease evolution.
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.