At FAU-ZISC “Data-Driven COVID Modeling”

Worlwide. 10.06.2020. During COVID-19 the World recognized the important role of Mathematics when critical decisions has to be made using the information and data analysis from mathematical models for the well being of our Society. Researchers all over the world with unstoppable activities keep working on it.

From FAU – Friedrich Alexander Universität (Erlangen-Nürnberg, Germany) our Head Enrique Zuazua has published with Cyprien Neverov a work about “Data-driven COVID-19 modeling” at ZISC – ZentralInstitute Scientific Computing, a platform to researchers to promote modeling and simulations about COVID-19.

Data driven approach
We decided to take an approach without this domain knowledge and explore the usage of a system identification tool called Sparse Identification of Nonlinear Dynamics that allows to find an ODE from observed data. This system identification algorithm has two key features:
(1) it relies on a set of user-defined candidate functions
(2) it converges to a sparse formulation of the dynamics by gradually zeroing-out the values that are under a certain threshold.