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

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