Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points
Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points
Vaccines are arguably the most important means of pandemic influenza mitigation. However, as during the 2009 H1N1 pandemic, mass immunization with an effective vaccine may not begin until a pandemic is well underway. In the U.S., state-level public health agencies are responsible for quickly and fairly allocating vaccines as they become available to populations prioritized to receive vaccines. Allocation decisions can be ethically and logistically complex, given several vaccine types in limited and uncertain supply and given competing priority groups with distinct risk profiles and vaccine acceptabilities. We introduce a model for optimizing statewide allocation of multiple vaccine types to multiple priority groups, maximizing equal access. We assume a large fraction of available vaccines are distributed to healthcare providers based on their requests, and then optimize county-level allocation of the remaining doses to achieve equity. We have applied the model to the state of Texas, and incorporated it in a Web-based decision-support tool for the Texas Department of State Health Services (DSHS). Based on vaccine quantities delivered to registered healthcare providers in response to their requests during the 2009 H1N1 pandemic, we find that a relatively small cache of discretionary doses (DSHS reserved 6.8% in 2009) suffices to achieve equity across all counties in Texas.
Huang, Hsin Chan
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Singh, Bismark
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Morton, David P.
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Johnson, Gregory P.
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Clements, Bruce
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Meyers, Lauren Ancel
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30 August 2017
Huang, Hsin Chan
cb0e54c5-b241-4c91-9738-b295c2de2e75
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Morton, David P.
3e053a27-b1bb-4764-b807-c6ab0a133bbe
Johnson, Gregory P.
d3f35edb-f85a-472c-93b4-55fe0b2decfc
Clements, Bruce
d6c41bd7-7df4-4784-a753-6db6f0374d51
Meyers, Lauren Ancel
4f9ada54-8b4c-4607-ad88-dafb19fa06b3
Huang, Hsin Chan, Singh, Bismark, Morton, David P., Johnson, Gregory P., Clements, Bruce and Meyers, Lauren Ancel
(2017)
Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points.
PLoS ONE, 12 (8), [e0182720].
(doi:10.1371/journal.pone.0182720).
Abstract
Vaccines are arguably the most important means of pandemic influenza mitigation. However, as during the 2009 H1N1 pandemic, mass immunization with an effective vaccine may not begin until a pandemic is well underway. In the U.S., state-level public health agencies are responsible for quickly and fairly allocating vaccines as they become available to populations prioritized to receive vaccines. Allocation decisions can be ethically and logistically complex, given several vaccine types in limited and uncertain supply and given competing priority groups with distinct risk profiles and vaccine acceptabilities. We introduce a model for optimizing statewide allocation of multiple vaccine types to multiple priority groups, maximizing equal access. We assume a large fraction of available vaccines are distributed to healthcare providers based on their requests, and then optimize county-level allocation of the remaining doses to achieve equity. We have applied the model to the state of Texas, and incorporated it in a Web-based decision-support tool for the Texas Department of State Health Services (DSHS). Based on vaccine quantities delivered to registered healthcare providers in response to their requests during the 2009 H1N1 pandemic, we find that a relatively small cache of discretionary doses (DSHS reserved 6.8% in 2009) suffices to achieve equity across all counties in Texas.
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Accepted/In Press date: 24 July 2017
Published date: 30 August 2017
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© 2017, Public Library of Science. All rights reserved. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Local EPrints ID: 472273
URI: http://eprints.soton.ac.uk/id/eprint/472273
ISSN: 1932-6203
PURE UUID: 3970cb3e-ca6d-4a2f-8a03-15b887c1d134
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Date deposited: 30 Nov 2022 17:43
Last modified: 06 Jun 2024 02:15
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Contributors
Author:
Hsin Chan Huang
Author:
Bismark Singh
Author:
David P. Morton
Author:
Gregory P. Johnson
Author:
Bruce Clements
Author:
Lauren Ancel Meyers
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