ArticleIEEE control systems letters2021
Covid-19 and Flattening the Curve: A Feedback Control Perspective.
Article in IEEE control systems letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Network-based proactive contact tracing: A pre-emptive, degree-based alerting framework for privacy-preserving COVID-19 apps.PLOS digital health · 2025Article
- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Latin America and the Caribbean: Longitudinal Trend Analysis.JMIR public health and surveillance · 2024Article
- Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles.Scientific reports · 2024Article
- Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources-a factorial experiment.BMC public health · 2022Article
- Feedback control of social distancing for COVID-19 via elementary formulae.IFAC-PapersOnLine · 2022Article
- Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review.Dynamic games and applications · 2022Article
- Estimating the state of epidemics spreading with graph neural networks.Nonlinear dynamics · 2022Article
- A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.