Optimizing Vaccine Distribution Using Sine Cosine Algorithm with Mutation

Vaccination is the best approach in curbing the spread of a pandemic. However, during pandemic one of the challenge is limited number of vaccine due to limited manufacturing capacity and high demand. Therefore, optimal vaccine distribution is needed to ensure maximum effectiveness in decreasing the total infections in the population. In this paper, the vaccine distribution is optimized using sine cosine algorithm improved with mutation (SCAmut). The SEIR model of H1N1 pandemic in 2009 is used as the case problem here. The effectiveness of SCAmut vaccine deployment is studied using two factors, which are vaccine coverage percentage and vaccine releasing time. The algorithm's result is compared with three traditional methods and original SCA without mutation. The findings suggested that the proposed SCAmut is able to provide more effective vaccination distributions better than the three traditional methods and also the original SCA.