Dynamic pricing strategy to optimally allocate vaccines

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dc.contributor.advisor Proano, Ruben
dc.contributor.author Pommier Gasser, Mauricio
dc.date.accessioned 2012-10-31T15:32:34Z
dc.date.available 2012-10-31T15:32:34Z
dc.date.issued 2012-05-10
dc.identifier.uri http://hdl.handle.net/1850/15443
dc.description.abstract In the United States, the Advisory Committee on Immunization Practices (ACIP) makes recommendations as to which cohorts (identified groups of individuals) ought to have higher priority access to vaccines when their supply is insufficient to immunize all susceptible individuals in the country. Typically, cohorts are determined based on susceptibility to contracting seasonal influenza and on the resulting consequences of infection for different age groups. For seasonal influenza, high-risk cohorts commonly include children, teenagers, pregnant women and people with different chronic diseases. This study proposes the application of revenue management theory to better allocate seasonal influenza vaccines among different risk-based population cohorts. Our model maximizes the number of immunized individuals by dynamically adjusting the price per dose in each cohort as to discourage vaccination in low-risk cohorts and preserve more supply for high-risk cohorts. Experimental results show that up to 12% of infections and deaths due to seasonal influenza could be avoided by implementing this price discrimination policy in hypothetical yet realistic scenarios. en_US
dc.language.iso en_US en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from http://ritdml.rit.edu/handle/1850/15443 to RIT Scholar Works http://scholarworks.rit.edu/theses/5734, please update your feeds & links!
dc.subject Operations research en_US
dc.subject Public health en_US
dc.subject Vaccine en_US
dc.subject.lcc RM281 .P66 2012
dc.subject.lcsh Vaccines--Prices--United States--Mathematical models en_US
dc.subject.lcsh Price discrimination--United States--Mathematical models en_US
dc.title Dynamic pricing strategy to optimally allocate vaccines en_US
dc.type Thesis en_US
dc.description.college Kate Gleason College of Engineering en_US
dc.description.department Department of Industrial and Systems Engineering en_US
dc.contributor.advisorChair Proano, Ruben

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