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

Abstract

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.

Strategic Optimization of Influenza Vaccine Allocation: A Case Study of Age-Specific Approaches in Saudi Arabia

“…Minoza et al [36] developed a multi-objective linear programming model to optimize vaccine distribution in an age-stratified population. Alkhamis and Hosny [37] introduced a Controlled Elitism Non-Dominated Sorting Genetic Algorithm model in the context of influenza vaccination policies to optimize vaccine allocation. Additionally, Alkhamis and Hosny [38] continued their research by using a Multi-objective simulated annealing algorithm to tackle the design of optimal vaccination sequences and distribution plans, aiming to improve overall vaccine effectiveness.…”

“…Given that allocating vaccinations falls into the NP-hard problem category [48], employing metaheuristics offers a viable alternative to the traditional, more time-intensive exhaustive search techniques. The adoption of metaheuristics for this issue is considered an advanced approach for addressing the problem, despite its relatively rare application in this area [37,38,49]. Therefore, we to demonstrate our innovative contribution in this interdisciplinary field by utilizing a specific metaheuristic algorithm, known as CENSGA-MOSA [38], as our proposed approach for problem resolution.…”

“…The proposed model in this research generalized the two-phased optimization approach in [37], where:…”

A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza

“…A recent work proposed by Alkhamis and Hosny [20] was inspired by many works in the area of vaccination allocation. This paper introduces an innovative approach for synthesizing pulse influenza vaccination policies by utilizing the controlled elitism nondominated sorting genetic algorithm (CENSGA).…”

“…This paper attempts to estimate an optimal Pareto set for the vaccination allocation problem using a memetic version of CENSGA (the controlled elitism nondominated sorting genetic algorithm) [20], which is a fast and elitist multi-objective optimization engine, with selection considering both the nondominance ranking and the crowding distance [21]. This article suggests an optimization scheme, embedding population-based simulated annealing (MOSA) as a local search module, with an archival hash table to help in controlling variability within the solution set.…”