Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search

Cruz, Andre Rodrigues da; Cardoso, Rodrigo T. N.; Takahashi, Ricardo H. C.

doi:10.1007/978-3-642-19893-9_28

Cited by 11 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We now turn to the design of the objective function, trying to take into consideration the sanitary and socio-economic outcomes of the lockdown and detection policy. In the context of planning vaccination campaigns, da Cruz et al (2011) and Verriest et al (2005) suggest a convex quadratic cost function by minimizing both the number of infected individuals in a time horizon and the cost to implement the control policy. In Kim et al (2017), a model for 2009 A/H1N1 influenza in Korea is considered: the goal is to minimize the number of infected individuals and the cost of implementing the control measures, and the cost is taken to be a nonlinear quadratic function.…”

Section: Objective Functionmentioning

confidence: 99%

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

Charpentier

Élie

Laurière

et al. 2020

Preprint

View full text Add to dashboard Cite

We consider here an extended model, including several features of the recent COVID-19 outbreak: in particular the infected and recovered individuals can either be detected (+) or undetected (-) and we also integrate an intensive care unit (ICU) capacity. Our model enables a tractable quantitative analysis of the optimal policy for the control of the epidemic dynamics using both lockdown and detection intervention levers. With parametric specification based on literature on COVID-19, we investigate the sensitivities of various quantities on the optimal strategies, taking into account the subtle trade-off between the sanitary and the socio-economic cost of the pandemic, together with the limited capacity level of ICU. We identify the optimal lockdown policy as an intervention structured in 4 successive phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition phase to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. The optimal scenario hereby avoids the second wave of infection, provided the lockdown is released sufficiently slowly. We also provide optimal intervention measures with increasing ICU capacity, as well as optimization over the effort on detection of infectious and immune individuals. Whenever massive resources are introduced to detect infected individuals, the pressure on social distancing can be released, whereas the impact of detection of immune individuals reveals to be more moderate.

show abstract

Section: Objective Functionmentioning

confidence: 99%

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

Charpentier

Élie

Laurière

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This work is based on control by pulse vaccination, which means that in certain time steps, a given percentage of the susceptible individuals get vaccinated and becomes part of the recovered population, according to references [1,7,9,10,14]. This approach allows different sizes of pulse control actions at arbitrary time instants.…”

Section: Optimization Modelmentioning

confidence: 99%

Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

Cardoso

Dusse²,

Adam³

2021

TCAM

View full text Add to dashboard Cite

A multiobjective impulsive control scheme is proposed to give answers on how optimal vaccination campaigns should be implemented, regarding two conflicting targets: making the total number of infecteds small and the vaccination campaign as handy as possible. In this paper, a stochastic SIR model is used to better depict the characteristics of a disease in practical terms, where little influences may lead to sudden and unpredictable changes in the behavior of transmissions. This model is extended to analyze the effects of impulsive vaccinations in two phases: the transient regime control, taking into account the necessity to reduce the number of infected individuals to an acceptable level in a finite time, and the permanent regime control, that will act with fixed vaccinations to avoid another outbreak. A parallel version of NSGA-II is used as the multiobjective optimization machinery, considering both the probability of eradication and the vaccination campaign costs. The final result using the proposed framework nondominated policies that can guide for public managers to decide which is the best procedure to be taken depending on the present situation.

show abstract

“…ynamic multi-objective optimization problems (DMOPs), with multiple conflicting and time-varying objectives, are ubiquitous in real-world applications [1], [2], [3], [4], [5], [6]. Multi-objective evolutionary algorithms (MOEAs) [7], [8], [9], [10], [11], [12], [13], [14] have achieved success on various static multi-objective optimization problems (MOPs) [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Multiobjective Evolutionary Algorithm Based on Decision Variable Classification

Liang

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.

show abstract

Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search

Cited by 11 publications

References 19 publications

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

A Dynamic Multiobjective Evolutionary Algorithm Based on Decision Variable Classification

Contact Info

Product

Resources

About