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Article: Optimization of influenza vaccine selection
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TitleOptimization of influenza vaccine selection
 
AuthorsWu, JT1 3
Wein, LM2
Perelson, AS
 
KeywordsDynamic Programming
Health Care
 
Issue Date2005
 
PublisherI N F O R M S. The Journal's web site is located at http://or.pubs.informs.org
 
CitationOperations Research, 2005, v. 53 n. 3, p. 456-476 [How to Cite?]
DOI: http://dx.doi.org/10.1287/opre.1040.0143
 
AbstractThe 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. © 2005 INFORMS.
 
ISSN0030-364X
2013 Impact Factor: 1.500
 
DOIhttp://dx.doi.org/10.1287/opre.1040.0143
 
ISI Accession Number IDWOS:000230321200006
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorWu, JT
 
dc.contributor.authorWein, LM
 
dc.contributor.authorPerelson, AS
 
dc.date.accessioned2012-06-26T06:25:27Z
 
dc.date.available2012-06-26T06:25:27Z
 
dc.date.issued2005
 
dc.description.abstractThe 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. © 2005 INFORMS.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationOperations Research, 2005, v. 53 n. 3, p. 456-476 [How to Cite?]
DOI: http://dx.doi.org/10.1287/opre.1040.0143
 
dc.identifier.doihttp://dx.doi.org/10.1287/opre.1040.0143
 
dc.identifier.epage476
 
dc.identifier.isiWOS:000230321200006
 
dc.identifier.issn0030-364X
2013 Impact Factor: 1.500
 
dc.identifier.issue3
 
dc.identifier.scopuseid_2-s2.0-25144505774
 
dc.identifier.spage456
 
dc.identifier.urihttp://hdl.handle.net/10722/151612
 
dc.identifier.volume53
 
dc.languageeng
 
dc.publisherI N F O R M S. The Journal's web site is located at http://or.pubs.informs.org
 
dc.publisher.placeUnited States
 
dc.relation.ispartofOperations Research
 
dc.relation.referencesReferences in Scopus
 
dc.subjectDynamic Programming
 
dc.subjectHealth Care
 
dc.titleOptimization of influenza vaccine selection
 
dc.typeArticle
 
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Author Affiliations
  1. Georgia Institute of Technology
  2. Stanford University
  3. Los Alamos National Laboratory