AN AHP BASED OPTIMAL DISTRIBUTION MODEL AND ITS APPLICATION IN COVID-19 VACCINATION
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Abstract
COVID-19 is causing a large number of causalities and producing tedious healthcare management problems at a global level. During a pandemic, resource availability and optimal distribution of the resources may save lives. Due to this issue, the authors have proposed an Analytical Hierarchy Process (AHP) based optimal distribution model. The proposed distribution model advances the AHP and enhances real-time model applicability by eliminating judgmental scale errors. The model development is systematically discussed. Also, the proposed model is utilized as a state-level optimal COVID-19 vaccine distribution model with limited vaccine availability. The COVID-19 vaccine distribution model used 28 Indian states and 7 union territories as the decision elements for the vaccination problem. The state-wise preference weights were calculated using the geometric mean AHP analysis method. The optimal state-level distribution of the COVID-19 vaccine was obtained using preference weights, vaccine availability and the fact that a patient requires exactly vaccine doses to complete a vaccination schedule. The optimal COVID-19 vaccine distribution along with state and union territory rank, and preference weights were compiled. The obtained results found Kerala, Maharashtra, Uttarakhand, Karnataka, and West Bengal to be the most COVID-19 affected states. In the future, the authors suggest using the proposed model to design an optimal vaccine distribution strategy at the district or country level, and to design a vaccine storage/inventory model to ensure optimal use of a vaccine storage center covering nearby territories.
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COVID-19, Vaccine distribution model, AHP, MCDM, Risk assessment
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