Indirect Model Predictive Control with Sparse Nonlinear Regression on Erdös-Rényi-generated Bernoulli-SIR Network Models

Abstract

Epidemiological modeling is important in order to be able to predict and mitigate the consequences of epidemics. Disease transmission network models can be used to model epidemics on a detailed level, which in turn can yield better predictions, at the cost of being more difficult to analyze and control. This paper demonstrates methods that enable simple network models to be controlled with conventional indirect optimal control methods, through model simplification via Monte-Carlo simulations and sparse nonlinear regression.