Function estimation and regularization in the SIRD model applied to the COVID-19 pandemics

This paper deals with the quantification of the different rates in epidemiological models from a function estimation framework, with the objective of identifying the desired unknowns without defining a priori basis functions for describing its behaviour. This approach is used to analyze data for the Covid-19 pandemic in Italy and Brazil. The forward problem is written in terms of the SIRD model, while the inverse problem is solved by combining the Levenberg-Marquardt method with Tikhonov regularization. A very good agreement was achieved between data and the calculated values. The resulting methodology is robust and very versatile, being easily applicable to other epidemiology models and data from other countries.

Automated summary

This paper quantifies different rates in epidemiological models using a function estimation framework, successfully applied to Covid-19 data in Italy and Brazil, with a good agreement between data and calculated values. The methodology is robust and versatile, easily adaptable to other epidemiology models and data from other countries.