2026-09-19T23:44:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/296002022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Lawrence M. Wein.
Wu, Joseph T. (Joseph Tszkei), 1977-
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2006-03-24T16:06:22Z
2006-03-24T16:06:22Z
2003
2003
http://hdl.handle.net/1721.1/29600
53011098
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003.
Includes bibliographical references (p. 89-90).
The World Health Organization (WHO) is responsible for making annual vaccine strains recommendation to countries around the globe. However, various studies have found that the WHO vaccine selection strategy has not been effective in some years. This motivates the search for a better strategy for choosing vaccine strains. In this work, we use recent results from theoretical immunology to formulate the vaccine selection problem as a discrete-time stochastic dynamic program with a high-dimensional continuous state space. We discuss the techniques that were developed for solving this difficult dynamic program, and present an effective and robust heuristic policy. We compare the performance of the heuristic policy, the follow policy, and the no-vaccine policy and show that the heuristic policy is the best among the three. After taking the cost of implementation into account, however, we conclude that the WHO policy is a cost-effective influenza vaccine strain selection policy.
by Joseph T. Wu.
Ph.D.
90 p.
2974545 bytes
2974353 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Operations Research Center.
Optimization of influenza vaccine strain selection
Thesis
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<Abstract>The World Health Organization (WHO) is responsible for making annual vaccine strains recommendation to countries around the globe. However, various studies have found that the WHO vaccine selection strategy has not been effective in some years. This motivates the search for a better strategy for choosing vaccine strains. In this work, we use recent results from theoretical immunology to formulate the vaccine selection problem as a discrete-time stochastic dynamic program with a high-dimensional continuous state space. We discuss the techniques that were developed for solving this difficult dynamic program, and present an effective and robust heuristic policy. We compare the performance of the heuristic policy, the follow policy, and the no-vaccine policy and show that the heuristic policy is the best among the three. After taking the cost of implementation into account, however, we conclude that the WHO policy is a cost-effective influenza vaccine strain selection policy.</Abstract>
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