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

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 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.

Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources—a factorial experiment

“…For authorities, it is important to understand population’s preferences and gain their trust in governmental strategies [ 23 ]. An example of strategic communication of infectious disease modelling results during the Covid-19 pandemic was the “flattening the curve” approach, which meant slowing the rate of infection and reducing the number of cases so that it did not overburden the healthcare system [ 24 , 25 ]. Studies about population’s preferences have also been implemented during the Covid-19 pandemic, e.g.…”

Designing Social Distancing Policies for the COVID-19 Pandemic: A probabilistic model predictive control approach

“…Today, the mathematical modelling efforts are focused more on the use of such mathematical models to design in a systematic way efficient coronavirus-slowing down-control policies accounting for both the effect of vaccinations and the impact of mobility policies on the COVID-19 transmission. Simple control approaches to COVID-19 involve the use of feedback control and on/off control to simulate the implementation of mobility policies [9]. More advanced control approaches such as Model predictive control (MPC) have been also applied constrained by specific epidemiological models.…”

Toward more realistic social distancing policies via advanced feedback control

“…[Di Lauro et al(2021b)], [Efimov & Ushirobira(2021)], [Esterhuizen et al(2021)], [Fliess et al(2022)], [Gevertz et al(2021)], ], [Ianni & Rossi(2021)], [Jing et al(2021)], [Köhler et al(2021)], [McQuade et al(2021)], [Morato et al(2020a)], [Morato et al(2020b)], [Morgan et al(2021)], [Morris et al(2021)], [O'Sullivan et al(2020)], [Péni et al(2020)], ], [Sadeghi et al(2021)], [Sontag(2021)], [Stella et al(2022)], [Tsay et al(2020)]. Most diverse viewpoints have been developed.…”

“…g ., [Adolph et al(2021)]) have stimulated a huge number of mathematically oriented investigations among which we select control-theoretic publications: See, e . g ., [Al-Radhawi et al(2022)], [Ames et al(2020)], [Angulo et al(2021)], [Berger(2022)], [Bisiacco & Pillonetto(2021)], [Bisiacco et al(2022)], [Bliman & Duprez(2021)], [Bliman et al(2021)], [Bonnans & Gianatti(2020)], [Borri et al(2021)], [Charpentier et al(2020)], [Dias et al(2022)], [Di Lauro et al(2021a)], [Di Lauro et al(2021b)], [Efimov & Ushirobira(2021)], [Esterhuizen et al(2021)], [Fliess et al(2022)], [Gevertz et al(2021)], [Greene & Sontag(2021)], [Ianni & Rossi(2021)], [Jing et al(2021)], [Köhler et al(2021)], [McQuade et al(2021)], [Morato et al(2020a)], [Morato et al(2020b)], [Morgan et al(2021)], [Morris et al(2021)], [O’Sullivan et al(2020)], [Péni et al(2020)], [Pillonetto et al(2021)], [Sadeghi et al(2021)], [Sontag(2021)], [Stella et al(2022)], [Tsay et al(2020)]. Most diverse viewpoints have been developed.…”