  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.
