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. 2017 Aug 30;12(8):e0182720.
doi: 10.1371/journal.pone.0182720. eCollection 2017.

Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points

Affiliations

Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points

Hsin-Chan Huang et al. PLoS One. .

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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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of methods for allocating vaccines of multiple types to priority groups at multiple locations, maximizing proportional fairness with consideration of policy simplicity and geographic equity.
The primary optimization model seeks proportionally fair coverage and the secondary model accounts for policy simplicity and geographic equity, while ensuring near optimality for proportional fairness. The post-processing step ensures integer-valued doses are allocated and then outputs the resulting final coverage and allocation.
Fig 2
Fig 2. County-level vaccine coverage before (blue) and after (red) allocation of the 6.8% discretionary reserve, for each of the five priority groups and the aggregate prioritized populations.
The left-hand side of each subfigure shows vaccine coverage for the 65 counties served by LHDs, which are not eligible for discretionary allocations; the right-hand side shows vaccine coverage for the other 189 counties that can receive discretionary doses. From left to right, the x-axis includes all 65 LHD counties and then 189 HSR counties in alphabetic order, but only displays a subset of the county names.

References

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