Learning the Dynamic SIR Model: An Optimal Control Approach

Abstract

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.

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