Covid-19 and Flattening the Curve: a Feedback Control Perspective
arXiv:2008.05245 · doi:10.1109/LCSYS.2020.3039322
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.
arXiv comment6 pages, 8 figures, letter
References in corpus (5)
- Epidemic processes in complex networks
- A SIDARTHE Model of COVID-19 Epidemic in Italy
- Can the COVID-19 epidemic be controlled on the basis of daily test reports?
- Post-Lockdown Abatement of COVID-19 by Fast Periodic Switching
- The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: a mean-field model perspective
Cited by in corpus (7)
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- Designing Social Distancing Policies for the COVID-19 Pandemic: A probabilistic model predictive control approach
- Feedback control of social distancing for COVID-19 via elementary formulae
- On Control of Epidemics with Application to COVID-19
- Epidemic oscillations induced by social network control: the discontinuous case