Control Strategies to Curtail Transmission of COVID-19

Shah, Nita H.; Suthar, Ankush H.; Jayswal, Ekta N.

doi:10.1101/2020.04.04.20053173

Cited by 11 publications

(15 citation statements)

References 23 publications

(32 reference statements)

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“…An optimal strategy in this sense involves weighing the relative costs of control and COVID-related mortality, and finding an approach to control that minimizes that combined cost. Other optimal control analyses of COVID-19 are beginning to emerge (Djidjou-Demasse 2020; Mallela 2020; Patterson-Lomba 2020; Piguillem and Shi 2020;Shah et al 2020), although these have been less focused on any particular geographic setting. One contextual feature of the USA that our analysis considers is the level of control that was implemented early in the pandemic.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

Perkins¹,

España²

2020

Bull Math Biol

156

104

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The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

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Section: Introductionmentioning

confidence: 99%

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

Perkins¹,

España²

2020

Bull Math Biol

156

104

View full text Add to dashboard Cite

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“…A mathematical Covid-19 model is constructed by Shah et al (2020) [1] to study human to human transmission of the Covid-19.…”

Section: Presentation Of Mathematical Covid-19 Model With Its Parametmentioning

confidence: 99%

“…(1) class of exposed individuals E(t) (individuals surrounded by infection by not yet infected), (iv) A detailed description of the model can be found in shah et al (2020) [1] and there in references. :…”

Section: Presentation Of Mathematical Covid-19 Model With Its Parametmentioning

confidence: 99%

“…Several efforts were done since the 1970's in order to understand the spread of diseases and to forecast their evolution through mathematical models. Amongst the various papers and preprints published to better understand the properties of the Covid-19 and model its evolution in different countries, a mathematical Covid-19 model was constructed by Shah et al (2020) [1] to study the human to human transmission of the Covid-19. This model is reinvestigated in this paper.…”

Section: Introductionmentioning

confidence: 99%

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A Mathematical Study of a Generalized SEIR Model of COVID-19

Intissar

2020

SciMed. J.

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In this work (Part I), we reinvestigate the study of the stability of the Covid-19 mathematical model constructed by Shah et al. (2020) [1]. In their paper, the transmission of the virus under different control strategies is modeled thanks to a generalized SEIR model. This model is characterized by a five dimensional nonlinear dynamical system, where the basic reproduction number can be established by using the next generation matrix method. In this work (Part I), it is established that the disease free equilibrium point is locally as well as globally asymptotically stable when . When , the local and global asymptotic stability of the equilibrium are determined employing the second additive compound matrix approach and the Li-Wang’s (1998) stability criterion for real matrices [2]. In the second paper (Part II), some control parameters with uncertainties will be added to stabilize the five-dimensional Covid-19 system studied here, in order to force the trajectories to go to the equilibria. The stability of the Covid-19 system with these new parameters will also be assessed in Intissar (2020) [3] applying the Li-Wang criterion and compound matrices theory. All sophisticated technical calculations including those in part I will be provided in appendices of the part II.

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“…We only claim the novelty of the combined strategy (A)-(C). Recent literature contains a variety of approaches on optimal control of Covid-19 spreading, see, e.g., [19,22,26,29], with the underlying idea being even older and more general [14]. Some parts from these approaches could also be included in the framework presented in this paper, however, we focus on our core ideas for now to make clear where the novelty lies.…”

mentioning

confidence: 99%

Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts

Wulkow

Conrad

et al. 2020

Preprint

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The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.

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Control Strategies to Curtail Transmission of COVID-19

Cited by 11 publications

References 23 publications

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

A Mathematical Study of a Generalized SEIR Model of COVID-19

Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts

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