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Release immediately or sequentially? Strategies for allocating scarce therapeutic resources during disease outbreaks

Release immediately or sequentially? Strategies for allocating scarce therapeutic resources during disease outbreaks
Release immediately or sequentially? Strategies for allocating scarce therapeutic resources during disease outbreaks

During infectious disease outbreaks, public health authorities face the challenge of fairly distributing limited medical therapeutics, such as antiviral drugs and respirators, to save lives. Such resources are not always widely available at the start of the outbreak when the progression of the outbreak is uncertain. Then, resources may be allocated from a centralized inventory—such as a federal stockpile—to regional authorities for further distribution on a periodic basis. Using mathematical optimization models for spatiotemporal resource allocations, we address a fundamental question of whether such stockpiles should be fully released at the outset or gradually distributed throughout the threat. Immediately releasing the therapeutics risks its consumption by non-symptomatic populations who are concerned about the disease; this is particularly so in the absence of a diagnostic test that distinguishes such individuals from those that are genuinely sick. We find that if the benefit a resource provides to an individual, whether genuinely sick or not, decreases as the outbreak progresses, then an immediate release is optimal. However, if some population groups are expected to benefit more than others, then sequential releases that conserve resources and match peak demand are advisable. Sequential release policies can pose challenging ethical and political dilemmas for policymakers under public pressure to provide immediate relief. To this end, using simulated scenarios sampled from six historic pandemics including the COVID-19 pandemic, we provide computational experiments for the state of Texas, with over 25 million people, that empirically demonstrate a significant number of lives can be saved by following optimal release policies.

Resource allocation, epidemiology, healthcare policy, pandemics, stochastic programs with recourse
2472-5854
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Rebennack, Steffen
d47be0b1-2fbb-4993-955c-d87e00b9266f
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Rebennack, Steffen
d47be0b1-2fbb-4993-955c-d87e00b9266f

Singh, Bismark and Rebennack, Steffen (2025) Release immediately or sequentially? Strategies for allocating scarce therapeutic resources during disease outbreaks. IISE Transactions. (doi:10.1080/24725854.2025.2525918).

Record type: Article

Abstract

During infectious disease outbreaks, public health authorities face the challenge of fairly distributing limited medical therapeutics, such as antiviral drugs and respirators, to save lives. Such resources are not always widely available at the start of the outbreak when the progression of the outbreak is uncertain. Then, resources may be allocated from a centralized inventory—such as a federal stockpile—to regional authorities for further distribution on a periodic basis. Using mathematical optimization models for spatiotemporal resource allocations, we address a fundamental question of whether such stockpiles should be fully released at the outset or gradually distributed throughout the threat. Immediately releasing the therapeutics risks its consumption by non-symptomatic populations who are concerned about the disease; this is particularly so in the absence of a diagnostic test that distinguishes such individuals from those that are genuinely sick. We find that if the benefit a resource provides to an individual, whether genuinely sick or not, decreases as the outbreak progresses, then an immediate release is optimal. However, if some population groups are expected to benefit more than others, then sequential releases that conserve resources and match peak demand are advisable. Sequential release policies can pose challenging ethical and political dilemmas for policymakers under public pressure to provide immediate relief. To this end, using simulated scenarios sampled from six historic pandemics including the COVID-19 pandemic, we provide computational experiments for the state of Texas, with over 25 million people, that empirically demonstrate a significant number of lives can be saved by following optimal release policies.

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Accepted/In Press date: 11 June 2025
Published date: 7 July 2025
Keywords: Resource allocation, epidemiology, healthcare policy, pandemics, stochastic programs with recourse

Identifiers

Local EPrints ID: 504160
URI: http://eprints.soton.ac.uk/id/eprint/504160
ISSN: 2472-5854
PURE UUID: 505e6356-6713-4656-b8b4-65afa2b0ebd3
ORCID for Bismark Singh: ORCID iD orcid.org/0000-0002-6943-657X

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Date deposited: 28 Aug 2025 16:37
Last modified: 29 Aug 2025 02:10

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Contributors

Author: Bismark Singh ORCID iD
Author: Steffen Rebennack

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