How control theory can help us control Covid-19

Gregory, Stewart; Heusden, Klaskevan; Dumont, Guy A.

doi:10.1109/mspec.2020.9099929

Cited by 78 publications

(74 citation statements)

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“…which is similar to other models found in the literature [6,9,10]. See [24] and the references within for a brief study on using control theory to fight COVID-19. Proof.…”

Section: Disease-free Equilibriumsupporting

confidence: 79%

COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility

Gui

2020

Physica D: Nonlinear Phenomena

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The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.

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“…which is similar to other models found in the literature [6,9,10]. See [24] and the references within for a brief study on using control theory to fight COVID-19. Proof.…”

Section: Disease-free Equilibriumsupporting

confidence: 79%

COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility

Gui

2020

Physica D: Nonlinear Phenomena

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“…An HMPC controller using a GA to solve for the optimal control action (similar to [18]) is implemented to determine optimal lockdown levels for different policy scenarios. The controlled variable is the active number of confirmed infectious cases: Q as given by (4).…”

Section: Controller Designmentioning

confidence: 99%

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

2020

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To curb the spread of COVID-19, many governments around the world have implemented tiered lockdowns with varying degrees of stringency. Lockdown levels are typically increased when the disease spreads and reduced when the disease abates. A predictive control approach is used to develop optimized lockdown strategies for curbing the spread of COVID-19. The strategies are then applied to South African data. The South African case is of interest as the South African government has defined five distinct levels of lockdown, which serves as a discrete control input. An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios. Scenarios considered include how to flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19. The main purpose of this article is to show what the optimal lockdown level should be given the policy that is in place, as determined by the closed-loop feedback controller.

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“…We note that the accuracy of any epidemiological model depends on the accuracy of its reproduction number, and so further improvements in this area would be of great benefit. For example, there is the potential for the adoption of an approach as recently proposed in 13 if the reproduction number could be approximated as a continuous function. While this is a challenging proposition, our work opens the door for more engineering researchers to create an impact on current and future pandemics.…”

Section: Discussionmentioning

confidence: 99%

“…There is recent evidence that such engineering thinking may have relevance for COVID-19. The Institute of Electrical and Electronics Engineers (IEEE) published an article citing the benefits of the application of such feedback control theory 13 , which is evidence of concurrent thinking along our lines. However, their work is primarily based on studying the impact of fictitious controllers over a simple disease model, without considering the actual data from the current pandemic.…”

mentioning

confidence: 99%

Compositional cyber-physical epidemiology of COVID-19

Allen

et al. 2020

Sci Rep

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The COVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis. However, the interactions between the government policy and the disease dynamics are not formally captured. We, for the first time, formally study the interaction between the disease dynamics, which is modelled as a physical process, and the government policy, which is modelled as the adjoining controller. Our approach enables compositionality, where either the plant or the controller could be replaced by an alternative model. Our work is inspired by the engineering approach for the design of Cyber-Physical Systems. Consequently, we term the new framework Compositional Cyber-Physical Epidemiology. We created different classes of controllers and applied these to control the disease in New Zealand and Italy. Our controllers closely follow government decisions based on their published data. We not only reproduce the pandemic progression faithfully in New Zealand and Italy but also show the tradeoffs produced by differing control actions.

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How control theory can help us control Covid-19

Cited by 78 publications

References 0 publications

COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility

COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility

Optimized Lockdown Strategies for Curbing the Spread of COVID-19: A South African Case Study

Compositional cyber-physical epidemiology of COVID-19

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