Optimization of Influenza Vaccine Selection

Joseph T. Wu (), Lawrence M. Wein () and Alan S. Perelson ()
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Joseph T. Wu: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, and Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Lawrence M. Wein: Graduate School of Business, Stanford University, Stanford, California 94305
Alan S. Perelson: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545

Operations Research, 2005, vol. 53, issue 3, 456-476

Abstract: The World Health Organization (WHO) recommends which strains of influenza to include in each year’s vaccine to countries around the globe. The current WHO strategy attempts to match the vaccine strains with the expected upcoming epidemic strains, a strategy we refer to as the follow policy. The recently proposed antigenic distance hypothesis suggests that vaccine efficacy can be enhanced by taking into account the antigenic histories of vaccinees. To assess the potential benefit of history-based vaccination, we formulate the annual vaccine-strains selection problem as a stochastic dynamic program using the theory of shape space, which maps each vaccine and epidemic strain into a point in multidimensional space. Computational results show that a near-optimal policy can be derived by approximating the entire antigenic history by a single reduced historical strain, and then solving the multiperiod problem myopically, as a series of single-period problems. The modest suboptimality of the follow policy, together with our current inability to quantitatively link the model’s objective function (a measure of cross-reactivity) with actual vaccine efficacy, leads us to recommend the continued use of the follow policy.

Keywords: dynamic programming; health care (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (20)

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