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

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

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
30 Downloads (Pure)


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 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.

Original languageEnglish
Pages (from-to)1435-1440
JournalIEEE Control Systems Letters
Issue number4
Publication statusPublished - 2021


  • Emerging control applications
  • Large-scale systems.
  • Network analysis and control


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