Model-based predictive control for the regulation of the golden mussel Limnoperna fortunei (Dunker, 1857)

Ávila-Simas, Sunshine de; Morato, Marcelo Menezes; Reynalte‐Tataje, David Augusto; Silveira, Hector Bessa; Zaniboni‐Filho, Evoy; Normey‐Rico, Julio E.

doi:10.1016/j.ecolmodel.2019.05.010

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…We must remark that many research papers have demonstrated the application and validation of this control tool for health-related, biological and ecological regulation purposes Ionescu et al (2008); Zurakowski et al (2004); Moscoso-Vásquez et al (2016);de Ávila-Simas et al (2019). Thus, MPC fits naturally to COVID-19 social distancing control problem.…”

Section: Introductionmentioning

confidence: 91%

An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil

Morato¹,

Bastos²,

Cajueiro³

et al. 2020

Preprint

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The global COVID-19 pandemic (SARS-CoV-2 virus) is the defining health crisis of our century. Due to the absence of vaccines and drugs that can help to fight it, the world solution to control the spread has been to consider public social distance measures that avoids the saturation of the health system. In this context, we investigate a Model Predictive Control (MPC) framework to determine the time and duration of social distancing policies. We use Brazilian data in the period from March to May of 2020. The available data regarding the number of infected individuals and deaths suffers from sub-notification due to the absence of mass tests and the relevant presence of the asymptomatic individuals. We estimate variations of the SIR model using an uncertainty-weighted Least-Squares criterion that considers both nominal and inconsistent-data conditions. Moreover, we add to our versions of the SIR model an additional dynamic state variable to mimic the response of the population to the social distancing policies determined by the government that affects the speed of COVID-19 transmission. Our control framework is within a mixed-logical formalism, since the decision variable is forcefully binary (the existence or the absence of social distance policy). A dwell-time constraint is included to avoid harsh shifting between these two states. Finally, we present simulation results to illustrate how such optimal control policy would operate. These results point out that no social distancing should be relaxed before mid August 2020. If relaxations are necessary, they should not be performed before the beginning this date and should be in small periods, no longer than 25 days. This paradigm would proceed roughly until January/2021. The second peak of infections, which has a forecast to the beginning of October, can be reduced if the periods of no-isolation days are

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Section: Introductionmentioning

confidence: 91%

An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil

Morato¹,

Bastos²,

Cajueiro³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…We must remark that many research papers have demonstrated the application and validation of this control tool for health-related, biological and ecological regulation purposes ( de Ávila-Simas, Morato, Reynalte-Tataje, Silveira, Zaniboni-Filho, Normey-Rico, 2019 , Ionescu, De Keyser, Torrico, De Smet, Struys, Normey-Rico, 2008 , Moscoso-Vásquez, Colmegna, Sánchez-Peña, 2016 , Zurakowski, Messina, Tuna, Teel, 2004 ). Thus, MPC fits naturally to COVID-19 social distancing control problem.…”

Section: Introductionmentioning

confidence: 99%

An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil

Morato

Bastos

Cajueiro

et al. 2020

Annual Reviews in Control

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110

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This paper formulates a Model Predictive Control (MPC) policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy. The proposed optimization algorithm is able to determine the time and duration of social distancing policies in the country. The achieved results are based on data from the period between March and May of 2020, regarding the cumulative number of infections and deaths due to the SARS-CoV-2 virus. This dataset is assumably largely sub-notified due to the absence of mass testing in Brazil. Thus, the MPC is based on a SIR model which is identified using an uncertainty-weighted Least-Squares criterion. Furthermore, this model includes an additional dynamic variable that mimics the response of the population to the social distancing policies determined by the government, which affect the COVID-19 transmission rate. The proposed control method is set within a mixed-logical formalism, since the decision variable is forcefully binary (existence or the absence of social distance policy). A dwell-time constraint is included to avoid too frequent shifts between these two inputs. The achieved simulation results illustrate how such optimal control method would operate in practice, pointing out that no social distancing should be relaxed before mid August 2020. If relaxations are necessary, they should not be performed before this date and should be in small periods, no longer than 25 days. This paradigm would proceed roughly until January/2021. The results also indicate a possible second peak of infections, which has a forecast to the beginning of October. This peak can be reduced if the periods of days with relaxed social isolation measures are shortened.

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“…Different social distancing policies have been implemented worldwide, according to the dynamics of SARS-CoV-2 in different countries. In general, those policies were formulated from evidence-based simulation models, some derived from well-known optimal control algorithms [ [38] , [39] , [40] ]. Control strategies based only on trial and error are risky in an epidemic context.…”

Section: Discussionmentioning

confidence: 99%

Unveiling conflicting strategies in the Brazilian response to COVID-19: A cross-sectional study using the Functional Resonance Analysis Method

Jatobá

Bellas

Viana

et al. 2022

Dialogues in Health

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Model-based predictive control for the regulation of the golden mussel Limnoperna fortunei (Dunker, 1857)

Cited by 6 publications

References 36 publications

An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil

An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil

An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil

Unveiling conflicting strategies in the Brazilian response to COVID-19: A cross-sectional study using the Functional Resonance Analysis Method

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