22 April 2020
(University of Siena)
The problem of parameter estimation for an epidemic model is crucial for the forecasting of the infection spread. We discuss an approach for learning the time-variant parameters of the dynamic SIR model from data. We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of the dynamic SIR. The resulting variational problem is then solved using a gradient flow on a suitable, regularized, functional. We show preliminary results on the estimates performed on COVID-19 data
relative to some Italian regions.