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 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 Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli ◽  
Graziana Cavone ◽  
Nicola Epicoco
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Mitigation Strategies ◽  
Economic Activities ◽  
Control Approach ◽  
Economic Framework ◽  
Regional Strategies ◽  
Italian Regions ◽  
Definition Of ◽  
Restrictive Measures

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. <br>In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. <br><br>Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario.<br>Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. <br>In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.<br><br>The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

scholarly journals Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli ◽  
Graziana Cavone ◽  
Nicola Epicoco
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Mitigation Strategies ◽  
Economic Activities ◽  
Control Approach ◽  
Economic Framework ◽  
Regional Strategies ◽  
Italian Regions ◽  
Definition Of ◽  
Restrictive Measures

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. <br>In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. <br><br>Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario.<br>Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. <br>In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.<br><br>The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

The first flush in sewer systems

1996 ◽  
Vol 33 (9) ◽  
pp. 101-108 ◽  
Author(s):  
Agnès Saget ◽  
Ghassan Chebbo ◽  
Jean-Luc Bertrand-Krajewski
Keyword(s):  
Treatment Strategy ◽  
Real Data ◽  
First Flush ◽  
Clear Definition ◽  
Controversial Subject ◽  
Sewer Systems ◽  
Wet Weather ◽  
Definition Of ◽  
Appearance Frequency

The first flush phenomenon of urban wet weather discharges is presently a controversial subject. Scientists do not agree with its reality, nor with its influences on the size of treatment works. Those disagreements mainly result from the unclear definition of the phenomenon. The objective of this article is first to provide a simple and clear definition of the first flush and then to apply it to real data and to obtain results about its appearance frequency. The data originate from the French database based on the quality of urban wet weather discharges. We use 80 events from 7 separately sewered basins, and 117 events from 7 combined sewered basins. The main result is that the first flush phenomenon is very scarce, anyway too scarce to be used to elaborate a treatment strategy against pollution generated by urban wet weather discharges.

scholarly journals An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Jorge Lopez-Jimenez ◽  
Nicanor Quijano ◽  
Alain Vande Wouwer
Keyword(s):  
Growth Models ◽  
Control Strategies ◽  
Low Cost ◽  
Soil Heterogeneity ◽  
Crop Growth ◽  
Negative Effects ◽  
Model Framework ◽  
Agent Based ◽  
Crop Growth Models ◽  
Definition Of

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

scholarly journals Dynamical aspects of smoking model with cravings to smoke

2021 ◽  
Vol 10 (1) ◽  
pp. 91-108
Author(s):  
Aziz Ullah Awan ◽  
Attia Sharif ◽  
Kashif Ali Abro ◽  
Muhammad Ozair ◽  
Takasar Hussain
Keyword(s):  
Sensitivity Analysis ◽  
Systems Approach ◽  
Control Strategies ◽  
Theoretic Approach ◽  
Square Root ◽  
Root Dynamics ◽  
Infected People ◽  
Graph Theoretic ◽  
Control Actions ◽  
The Media

Abstract The square-root dynamics of smoking model with cravings to smoke, in which square root of potential smokers and smokers is the interaction term, has been studied. We categorized net population in four different chambers: non-smokers/potential smokers, smokers/infected people, non-permanent smokers/temporary quitters and the permanent quitters. By dynamical systems approach, we analyzed our model. Moreover, for proving the unique equilibrium point to be globally stable, we took help of graph theoretic approach. The sensitivity analysis of the model is performed through the diseased classes effectively to design reliable, robust and stable control strategies. The model is designed like optimal control trouble to find out importance of various control actions on our system that are insisted by the sensitivity analysis. We have applied two controls, which are the awareness campaign through the media transmission to control the potential smokers and temporary quit smokers to become smokers and the treatment of smokers. Analytical and numerical methods are utilized for ensuring presence of these two control actions.

Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks

2001 ◽  
Vol 43 (11) ◽  
pp. 189-196 ◽  
Author(s):  
M. Bongards
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Treatment Plant ◽  
Municipal Wastewater Treatment ◽  
Controlled System ◽  
Measurement Results ◽  
Predictive Controller

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.

scholarly journals PLACING OF PRODUCTIVE FORCES AS AN INSTRUMENT OF POLICY FOR SUSTAINABLE DEVELOPMENT OF THE REGION

2021 ◽  
Author(s):  
Svitlana Mashchenko ◽  
◽  
Katerina Goncharova ◽  
Vladyslav Hrebennikov ◽  
◽  
...  
Keyword(s):  
Sustainable Development ◽  
Research Process ◽  
Development Programs ◽  
Balanced Development ◽  
Regional Strategies ◽  
Effective Policy ◽  
The Subject ◽  
Definition Of ◽  
Goals And Objectives ◽  
Determining Factor

A systematization of approaches to the definition of «sustainable development» has been provided in this article. It has been concluded that there is no unambiguous definition of sustainable development. This term is interpreted by scholars from the standpoint of their own subjective judgments. The definition given in the national strategy of sustainable development of Ukraine until 2030 can be considered as the most acceptable. It has been noted that in the implementation of the concept of sustainable development, the regional (subnational) level should be the determining factor. This is due to the fact that the region is the subject of integration relations, where the goals of sustainable development are most adequate to its position in the system of management of the national economy and regional association. It has been noted that the implementation of sustainable development tasks at the regional level requires: development of socio-economic and environmental development programs; formation of local budgets taking into account the priority of goals and objectives of sustainable development; implementation of a set of measures for balanced development of regions. It has been determined that for further implementation of the concept of sustainable development in the regional dimension, it is necessary to develop a new methodology taking into account the effective allocation of productive forces. It has been emphasized that the deployment of productive forces is a tool to achieve effective implementation of the concept of sustainable development. A scheme to ensure sustainable development of the regions, taking into account the factors of the APF has been suggested by the authors. There are three main areas: development of sustainable development strategies for each region; the formation of local budgets taking into account the priorities and goals of sustainable development and the formation of an effective policy of the APF in the context of sustainable development. It has been noted that the last area is the main one and needs the most attention in the research process. It has been concluded that sustainable development in the region is possible through the development and implementation of regional strategies and programs of sustainable development, as well as in the development of coordinated regional development policy in the context of sustainable development and local budgets taking into account the priority of sustainable development.

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