Optimal targeting of seasonal influenza vaccination toward younger ages isrobust to parameter uncertainty Public Deposited

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This is the author's peer-reviewed final manuscript. The version of record is copyrighted by Elsevier and can be found here:  http://www.journals.elsevier.com/vaccine/

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  • Identification of the optimal vaccine allocation for the control of influenza requires consideration of uncertainty arising from numerous unpredictable factors, including viral evolution and diversity within the human population’s immunity as well as variation in vaccine efficacy. The best policy must account for diverse potential outcomes based on these uncertainties. Here we used a mathematical model parametrized with survey-based contact data, demographic, and epidemiological data from seasonal influenza in the United States to determine the optimal vaccine allocation for five outcome measures:infections, hospitalizations, deaths, years of life loss, and contingent valuation. We incorporated uncertainty of epidemiological parameters and derive probability distributions of optimal age- and risk-specific allocation of vaccine. Our analysis demonstrated that previous recommendations of targeting schoolchildren (ages 5–17 years) and young adults (18–44 years) are generally robust in the face of uncertainty.However, when the outcome measure is to minimize deaths, years of life loss, or contingent valuation, uncertainty analysis identified scenarios under which it is optimal to target people at high risk for complications, even when vaccine are in abundance.
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  • Ndeffo Mbah, M. L., Medlock, J., Meyers, L. A., Galvani, A. P., & Townsend, J. P. (2013). Optimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty. Vaccine, 31(30), 3079-3089 . doi:10.1016/j.vaccine.2013.04.052
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  • description.provenance : Made available in DSpace on 2013-05-17T23:05:17Z (GMT). No. of bitstreams: 4 MedlockJanBiomedicalSciencesVaccineAllocationUncertaintyAnalysis.pdf: 601864 bytes, checksum: 76d9ed432379757fc288afcf096d68d2 (MD5) Figures.zip: 2176530 bytes, checksum: 01d45cd3a3ee282dcc96ba314f87d402 (MD5) Tables.zip: 138684 bytes, checksum: bd786cfe59aa3dbc4044b957a0c8d202 (MD5) SupplementaryMaterials.zip: 336720 bytes, checksum: 1aeab960ee7394a1b3550afaf7666a8f (MD5) Previous issue date: 2013-04-24
  • description.provenance : Submitted by Sue Kunda (sue.kunda@oregonstate.edu) on 2013-05-17T23:04:26Z No. of bitstreams: 4 MedlockJanBiomedicalSciencesVaccineAllocationUncertaintyAnalysis.pdf: 601864 bytes, checksum: 76d9ed432379757fc288afcf096d68d2 (MD5) Figures.zip: 2176530 bytes, checksum: 01d45cd3a3ee282dcc96ba314f87d402 (MD5) Tables.zip: 138684 bytes, checksum: bd786cfe59aa3dbc4044b957a0c8d202 (MD5) SupplementaryMaterials.zip: 336720 bytes, checksum: 1aeab960ee7394a1b3550afaf7666a8f (MD5)
  • description.provenance : Approved for entry into archive by Sue Kunda(sue.kunda@oregonstate.edu) on 2013-05-17T23:05:17Z (GMT) No. of bitstreams: 4 MedlockJanBiomedicalSciencesVaccineAllocationUncertaintyAnalysis.pdf: 601864 bytes, checksum: 76d9ed432379757fc288afcf096d68d2 (MD5) Figures.zip: 2176530 bytes, checksum: 01d45cd3a3ee282dcc96ba314f87d402 (MD5) Tables.zip: 138684 bytes, checksum: bd786cfe59aa3dbc4044b957a0c8d202 (MD5) SupplementaryMaterials.zip: 336720 bytes, checksum: 1aeab960ee7394a1b3550afaf7666a8f (MD5)

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