Covid-19 and Flattening the Curve: A Feedback Control Perspective

Francesco Di Lauro; Istvan Zoltan Kiss; Daniela Rus; Cosimo Della Santina

doi:10.1109/LCSYS.2020.3039322

Covid-19 and Flattening the Curve: A Feedback Control Perspective

IEEE Control Syst Lett. 2020 Nov 19;5(4):1435-1440. doi: 10.1109/LCSYS.2020.3039322. eCollection 2021 Oct.

Authors

Francesco Di Lauro¹, Istvan Zoltan Kiss¹, Daniela Rus², Cosimo Della Santina^{3

4}

Affiliations

¹ Department of MathematicsUniversity of Sussex Brighton BN1 9QH U.K.
² MIT Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of Technology Cambridge MA 02139 USA.
³ Cognitive Robotics DepartmentDelft University of Technology 2628 CD Delft The Netherlands.
⁴ Institute of Robotics and MechatronicsGerman Aerospace Center (DLR) 82234 Weßling Germany.

Abstract

Many of the policies that were put into place during the Covid-19 pandemic had a common goal: to flatten the curve of the number of infected people so that its peak remains under a critical threshold. This letter considers the challenge of engineering a strategy that enforces such a goal using control theory. We introduce a simple formulation of the optimal flattening problem, and provide a closed form solution. This is augmented through nonlinear closed loop tracking of the nominal solution, with the aim of ensuring close-to-optimal performance under uncertain conditions. A key contribution of this letter is to provide validation of the method with extensive and realistic simulations in a Covid-19 scenario, with particular focus on the case of Codogno - a small city in Northern Italy that has been among the most harshly hit by the pandemic.

Keywords: Emerging control applications; large-scale systems; network analysis and control.

Grants and funding

The work of Francesco Di Lauro and István Zoltán Kiss was supported by the Leverhulme Trust for the Research Project under Grant RPG2017-370. The work of Cosimo Della Santina was supported by the TU Delft COVID-19 Response Fund.