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

TL;DR

This paper applies control theory to design and validate strategies for flattening the Covid-19 infection curve, providing a closed-form solution and simulation-based validation for real-world scenarios.

Contribution

It introduces a novel control-theoretic approach with a closed-form optimal solution and nonlinear tracking for Covid-19 curve flattening, validated through realistic simulations.

Findings

The control strategy effectively flattens the infection curve in simulations.

The method maintains performance under uncertain conditions.

Validation on a real-world scenario demonstrates practical applicability.

Abstract

Many of the control 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 ofthis paper 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.