scholarly journals Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread

2021 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Graziana Cavone ◽  
Nicola Epicoco ◽  
Mariagrazia Dotoli
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Control Strategies ◽  
Real Data ◽  
Chance Constraint ◽  
Science And Engineering ◽  
Italian Regions ◽  
Predictive Controller ◽  
Definition Of ◽  
Constraint Approach

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal non-pharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying <i>SIRCQTHE</i> epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socio-economic categories) to minimize the socio-economic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified non-pharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale.<br><br><div><br></div><div>Postprint accepted for pubblication in <i>IEEE Transactions on Automation Science and Engineering</i> (T-ASE)</div><div><br></div><div><b>How to cite</b>: P. Scarabaggio, R. Carli, G. Cavone, N. Epicoco and M. Dotoli, (2021) "Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread," in IEEE Transactions on Automation Science and Engineering.</div><div><br></div><div>DOI: http://doi.org/10.1109/TASE.2021.3111338<br><br></div>

scholarly journals Stochastic Non-Pharmaceutical Optimal Control Strategies to Mitigate COVID-19

2021 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Graziana Cavone ◽  
Nicola Epicoco ◽  
Mariagrazia Dotoli
Keyword(s):  
Control Strategies ◽  
Optimal Strategies ◽  
Real Data ◽  
Chance Constraint ◽  
Control Actions ◽  
Regional Strategies ◽  
Italian Regions ◽  
Predictive Controller ◽  
Definition Of ◽  
Constraint Approach

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal strategies to tackle the COVID-19 secondary waves. First, a time-varying <i>SIRCQTHE </i>epidemiological model (considering Susceptible, Infected, Removed, Contagious, Quarantined, Threatened, Healed, and Extinct compartments of individuals) is defined to get reliable predictions on the pandemic dynamics on a regional basis. A stochastic Model Predictive Control problem is then formulated to select the necessary control actions to minimize the arising socio-economic costs. <br>In particular, considering the unavoidable uncertainty characterizing this decision-making process, we ensure that the capacity of the network of regional healthcare systems is not violated in accordance with a chance constraint approach.<br>Furthermore, since the infection rate depends on people’s mobility, differently from the related literature, we model the control actions as interventions affecting the mobility levels associated to different socio-economic categories.<br><div>The effectiveness of the presented method in properly supporting the definition of diversified regional strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data from the Italian Civil Protection Department. However, provided the availability of reliable data, the proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Automation Science and Engineering (<em>T-ASE</em>)</div>

scholarly journals Stochastic Non-Pharmaceutical Optimal Control Strategies to Mitigate COVID-19

2021 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Graziana Cavone ◽  
Nicola Epicoco ◽  
Mariagrazia Dotoli
Keyword(s):  
Control Strategies ◽  
Optimal Strategies ◽  
Real Data ◽  
Chance Constraint ◽  
Control Actions ◽  
Regional Strategies ◽  
Italian Regions ◽  
Predictive Controller ◽  
Definition Of ◽  
Constraint Approach

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal strategies to tackle the COVID-19 secondary waves. First, a time-varying <i>SIRCQTHE </i>epidemiological model (considering Susceptible, Infected, Removed, Contagious, Quarantined, Threatened, Healed, and Extinct compartments of individuals) is defined to get reliable predictions on the pandemic dynamics on a regional basis. A stochastic Model Predictive Control problem is then formulated to select the necessary control actions to minimize the arising socio-economic costs. <br>In particular, considering the unavoidable uncertainty characterizing this decision-making process, we ensure that the capacity of the network of regional healthcare systems is not violated in accordance with a chance constraint approach.<br>Furthermore, since the infection rate depends on people’s mobility, differently from the related literature, we model the control actions as interventions affecting the mobility levels associated to different socio-economic categories.<br><div>The effectiveness of the presented method in properly supporting the definition of diversified regional strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data from the Italian Civil Protection Department. However, provided the availability of reliable data, the proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Automation Science and Engineering (<em>T-ASE</em>)</div>

scholarly journals The value of monitoring to control evolving populations

2015 ◽  
Vol 112 (4) ◽  
pp. 1007-1012 ◽  
Author(s):  
Andrej Fischer ◽  
Ignacio Vázquez-García ◽  
Ville Mustonen
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Finite Population ◽  
Control Strategies ◽  
Target Population ◽  
Therapy Resistance ◽  
Mathematical Concepts ◽  
Control Objective ◽  
Cancer Cell Population ◽  
Evolving Populations

Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.

scholarly journals Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration

2021 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Graziana Cavone ◽  
Nicola Epicoco ◽  
Mariagrazia Dotoli
Keyword(s):  
Optimal Control ◽  
Disease Transmission ◽  
Linear Time ◽  
Real Data ◽  
Science And Engineering ◽  
Vaccine Administration ◽  
Control Approach ◽  
International Conference ◽  
The Impact ◽  
Dose Vaccine

The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. <br>In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct). <br>Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low. <br><div>To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer's vaccine confirming the results of clinical tests.</div><div><br></div><div><br></div><div>Extended version of the paper published in <i>Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE) </i><br> </div><div><b>How to cite:</b> Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. "Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration." In <i>2021 IEEE 17th International Conference on Automation Science and Engineering</i> (CASE) (pp. 990-995). IEEE.<br></div><div><br></div><div>DOI: <u>https://doi.org/10.1109/CASE49439.2021.9551418</u></div><div><br></div><div><br> </div>

scholarly journals Optimal Containment Control Strategy of the Second Phase of the COVID-19 Lockdown in Morocco

2020 ◽  
Vol 10 (21) ◽  
pp. 7559
Author(s):  
Mustapha Lhous ◽  
Omar Zakary ◽  
Mostafa Rachik ◽  
El Mostafa Magri ◽  
Abdessamad Tridane
Keyword(s):  
Optimal Control ◽  
Least Squares Method ◽  
Control Strategies ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Contact Tracing ◽  
Second Phase ◽  
Containment Control ◽  
Treatment Measures ◽  
The Impact

This work investigates the optimal control of the second phase of the COVID-19 lockdown in Morocco. The model consists of susceptible, exposed, infected, recovered, and quarantine compartments (SEIRQD model), where we take into account contact tracing, social distancing, quarantine, and treatment measures during the nationwide lockdown in Morocco. First, we present different components of the model and their interactions. Second, to validate our model, the nonlinear least-squares method is used to estimate the model’s parameters by fitting the model outcomes to real data of the COVID-19 in Morocco. Next, to investigate the impact of optimal control strategies on this pandemic in the country. We also give numerical simulations to illustrate and compare the obtained results with the actual situation in Morocco.

Nonlinear Stochastic Optimal Control of Quasi Hamiltonian Systems

2005 ◽  
Author(s):  
W. Q. Zhu
Keyword(s):  
Optimal Control ◽  
Hamiltonian Systems ◽  
Stochastic Optimal Control ◽  
Control Strategies ◽  
First Passage Time ◽  
Stochastic Averaging ◽  
Passage Time ◽  
Stochastic Averaging Method ◽  
Dynamic Programming Principle ◽  
Stochastic Dynamic

In recent years, a class of nonlinear stochastic optimal control strategies were developed by the present author and his co-workers for minimizing the response, stabilization and maximizing the reliability and mean first-passage time of quasi Hamiltonian systems based on the stochastic averaging method for quasi Hamiltonian systems and the stochastic dynamic programming principle. This review summaries the basic idea, procedures and applications of these strategies and pointes out necessary further work.

scholarly journals Optimization of the Controls against the Spread of Zika Virus in Populations

Computation ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 76
Author(s):  
Gilberto González-Parra ◽  
Miguel Díaz-Rodríguez ◽  
Abraham J. Arenas
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Zika Virus ◽  
Control Strategies ◽  
Real Life ◽  
Real Data ◽  
Control Measures ◽  
Information Campaigns ◽  
Control Functions ◽  
Cost Efficient

In this paper, we study and explore two control strategies to decrease the spread of Zika virus in the human and mosquito populations. The control strategies that we consider in this study are awareness and spraying campaigns. We solve several optimal control problems relying on a mathematical epidemic model of Zika that considers both human and mosquito populations. The first control strategy is broad and includes using information campaigns, encouraging people to use bednetting, wear long-sleeve shirts, or similar protection actions. The second control is more specific and relies on spraying insecticides. The control system relies on a Zika mathematical model with control functions. To develop the optimal control problem, we use Pontryagins’ maximum principle, which is numerically solved as a boundary value problem. For the mathematical model of the Zika epidemic, we use parameter values extracted from real data from an outbreak in Colombia. We study the effect of the costs related to the controls and infected populations. These costs are important in real life since they can change the outcomes and recommendations for health authorities dramatically. Finally, we explore different options regarding which control measures are more cost-efficient for society.

A Methodology for the Definition of Optimal Control Strategies of a VVT-Equipped SI Engine

2001 ◽  
Author(s):  
F. Bozza ◽  
M. Cardone ◽  
A. Gimelli ◽  
A. Senatore ◽  
R. Tuccillo
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Si Engine ◽  
Definition Of

scholarly journals Optimal control strategies on COVID-19 infection to bolster the efficacy of vaccination in India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ashutosh Rajput ◽  
Mohammad Sajid ◽  
Tanvi ◽  
Chandra Shekhar ◽  
Rajiv Aggarwal
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Real Data ◽  
Vaccination Rate ◽  
Transmission Dynamics ◽  
The Novel ◽  
Long Term Effects ◽  
Precautionary Measures ◽  
Novel Coronavirus

AbstractThe Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin’s Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number $${\mathcal {R}}_0$$ R 0 is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of $${\mathcal {R}}_0$$ R 0 using the parameters value. In the numerical simulations, we concluded that reducing $${\mathcal {R}}_0$$ R 0 below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures.

Sign in / Sign up

Export Citation Format

Share Document