Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

Cardoso, Rodrigo T. N.; Dusse, A. C. S.; Adam, Kay

doi:10.5540/tcam.2021.022.02.00179

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…This work is based on Pulse vaccination, which means that in a predetermined time period, there is a vaccination policy that will be applied on a certain percentage of susceptible individuals. This approach allows different/exact sizes of Pulse control actions at arbitrary/autonomous time points in order to form a complete vaccination campaign [3,6,7]. According to the work of A. C. S. DUSSE et al [3], applying a deterministic (discrete) or stochastic (continuous) SIR system in Pulse vaccination yields negligible differences in the obtained findings.…”

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

“…This approach allows different/exact sizes of Pulse control actions at arbitrary/autonomous time points in order to form a complete vaccination campaign [3,6,7]. According to the work of A. C. S. DUSSE et al [3], applying a deterministic (discrete) or stochastic (continuous) SIR system in Pulse vaccination yields negligible differences in the obtained findings. Moreover, the effects of Pulse vaccination on a continuous system can be observed as a transformation of the continuous system to a non-continuous system.…”

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

“…Moreover, the effects of Pulse vaccination on a continuous system can be observed as a transformation of the continuous system to a non-continuous system. The Pulse action affects the functions (S), (I), and (R) by turning them into step functions [3]. Thus, the aim of this research is to study Pulse vaccination control from a discrete time optimization point of view.…”

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

“…In the optimization set-up, we must determine the chromosome representation and fitness equation that helps in examining the objective functions. For the sake of this work we adopt the general framework methodology found in [3,6,7] to test the suggested CENSGA technique under the same conditions and parameter calibrations.…”

Section: Proposed Modelmentioning

confidence: 99%

“…With the introduction of vaccinations for different diseases starting from the nineteenth century, this remarkable era of vaccination has had a promising influence on public health and population growth [2]. Since then, a succession of well-planned campaigns have been documented, for instance, to reduce or eradicate hepatitis, yellow fever, polio and measles [3]. Although vaccination against contagious diseases is currently considered as an intuitive strategy to control an outbreak or epidemic, policymakers are still facing a big challenge in allocating vaccine doses to the public, especially for mutant diseases or pandemics, when the aim is eradicating the disease, or keeping it within a tolerable level with minimal cost [4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Alkhamis¹,

Hosny²

2022

Preprint

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: Seasonal influenza (a.k.a flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine, in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management, and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

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Section: Pulse Vaccine Allocationmentioning

confidence: 99%

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Alkhamis¹,

Hosny²

2022

Preprint

View full text Add to dashboard Cite

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Optimal Lockdown Policies in a Stochastic SIR Model

Sato,

Yagi,

Sawaki

2023

Journal of Real Options and Strategy

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A Synthesis of Pulse Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Alkhamis

Hosny

2022

Electronics

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Seasonal influenza (also known as flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to the WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of the influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results indicate that optimal vaccination campaigns with compromise tradeoffs between the two conflicting objectives can be designed effectively using CENSGA, providing policymakers with a number of alternatives to accommodate the best strategies. The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

show abstract

Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

Cited by 4 publications

References 21 publications

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Optimal Lockdown Policies in a Stochastic SIR Model

A Synthesis of Pulse Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

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