Using a Stochastic SIR Model to Design Optimal Vaccination Campaigns via Multiobjective Optimization

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

doi:10.1007/978-3-030-46306-9_16

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…An individual within a certain time unit is recognized on one class only. Studying individuals' behavior and their movements between compartments helps in assessing and forming appropriate procedures and policies to override or mitigate the effects of diseases as much as possible by vaccination, for example, or by applying any countermeasure means [7,31]. The following section describes the compartmental model used in this work, the SIR (Susceptible-Infected-Recovered) model.…”

Section: Epidemiological Modelsmentioning

confidence: 99%

“…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 work proposes a vaccination allocation policy using Pulse vaccination, based on an SIR (Susceptible-Infected-Recovered) epidemiological model that simulates the behavior of an infectious disease. A Pulse vaccination policy requires allocating vaccine doses in certain time steps, as well as vaccinating a predetermined proportion of the susceptible individuals, so that they become part of the recovered population, according to references [5][6][7]. This approach permits two types of control policies: heterogenous and homogeneous vaccination.…”

Section: Introductionmentioning

confidence: 99%

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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.

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Section: Epidemiological Modelsmentioning

confidence: 99%

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…An individual within a certain time unit is recognized on one class only. Studying individuals' behavior and their movements between compartments helps in assessing and forming appropriate procedures and policies to override or mitigate the effects of diseases as much as possible by vaccination, for example, or applying any countermeasure means [7,31]. The following section describes the compartmental model used in this work, the SIR (Susceptible-Infected-Recovered) model.…”

Section: Epidemiological Modelsmentioning

confidence: 99%

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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“…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

Self Cite

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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.

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Using a Stochastic SIR Model to Design Optimal Vaccination Campaigns via Multiobjective Optimization

Cited by 3 publications

References 13 publications

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

A Synthesis of Pulse 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 Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

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