Transforming pandemic influenza antiviral stockpiling using a capacity-based approach: A modeling study
- 1Faculty of Mathematics, Israel Institute of Technology, Haifa, Israel
- 2Data & Analytics, Sheba Medical Center, Ramat Gan, Israel
- 3Department of Epidemiology and Preventive Medicine, the Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv-Yafo, Israel
Abstract
Background: Pandemic influenza preparedness in high-resource nations has traditionally relied on large antiviral stockpiles, typically targeting coverage for 25% of the population. This inventory-based benchmark focuses on cumulative disease burden but often overlooks the inelastic capacity of healthcare systems to manage acute surges. As multi-billion-dollar inventories reach expiration, we evaluated whether static stockpiles remain a viable strategy for preventing healthcare system collapse during future pandemics.
Methods and findings: We projected daily hospital bed demand using three complementary frameworks: a static lower-bound estimation, a dynamic deterministic Susceptible-Infectious-Hospitalized-Recovered transmission model, and historical reconstructions of the 1918, 1957, and 1968 pandemics using mortality data. We evaluated official planning scenarios for the United Kingdom, Japan, and the United States, comparing projected demand against national surge capacities (i.e., staffed beds). To test the theoretical limits of the stockpile strategy, we applied best-case assumptions, including perfect distribution of antivirals to 100% of symptomatic cases and high clinical effectiveness (30% to 60% reduction in hospitalization).
Our models identified a critical feasibility gap. While stockpiles successfully suppressed demand to manageable levels in moderate scenarios (analogous to 1957/1968), they were structurally insufficient for severe events. In high-severity scenarios (analogous to 1918 or CDC Very Severe parameters), the daily surge of patients exceeded available hospital capacity by a factor of 5–9. This inundation occurred even under the assumption of 100% treatment coverage, rendering antivirals incapable of preventing transition to crisis standards of care. Although this analysis is subject to the inherent uncertainties of future pandemics, our use of optimistic best-case assumptions suggests that the identified structural failures are a lower-bound estimate. Realistic operational hurdles would only widen the disparity between stockpile targets and system capacity.
The main limitations of this study’s methodology are its inherent optimistic bias, specifically the use of a conservative 4-day hospital length of stay that underestimates prolonged intensive care needs, the reliance on general hospital beds as a proxy that abstracts away from critical sub-system bottlenecks, and the assumption of guaranteed antiviral efficacy against novel strains.
Conclusions: This study demonstrates that large reserves cannot overcome the rigid physical constraints of healthcare systems, even under the optimistic assumption of perfect distribution. Consequently, the conventional 25% coverage target for antiviral stockpiles, a hallmark of legacy planning, presents a critical opportunity for re-evaluation. As global stocks near expiration, policymakers are uniquely positioned to leverage this inflection point by exploring a capacity-based framework. This adaptive paradigm suggests incorporating a synergistic mix of targeted reserves, temporary non-pharmaceutical interventions, and flexible delivery options, such as decentralized home-first care. Together, these integrated measures could serve as a vital temporal bridge, preserving system integrity until vaccine deployment.
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BibTeX
@article{gavish2026transforming,
title = {{Transforming pandemic influenza antiviral stockpiling using a capacity-based approach: A modeling study}},
author = {Nir Gavish and Amit Huppert},
journal = {PLOS Medicine},
year = {2026},
month = nov,
doi = {10.1371/journal.pmed.1004992},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004992},
note = {CC BY},
}
RIS
TY - JOUR
TI - Transforming pandemic influenza antiviral stockpiling using a capacity-based approach: A modeling study
AU - Nir Gavish
AU - Amit Huppert
PY - 2026
DA - 2026/11/01
JO - PLOS Medicine
DO - 10.1371/journal.pmed.1004992
UR - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004992
AB - Background: Pandemic influenza preparedness in high-resource nations has traditionally relied on large antiviral stockpiles, typically targeting coverage for 25% of the population. This inventory-based benchmark focuses on cumulative disease burden but often overlooks the inelastic capacity of healthcare systems to manage acute surges. As multi-billion-dollar inventories reach expiration, we evaluated whether static stockpiles remain a viable strategy for preventing healthcare system collapse during future pandemics. Methods and findings: We projected daily hospital bed demand using three complementary frameworks: a static lower-bound estimation, a dynamic deterministic Susceptible-Infectious-Hospitalized-Recovered transmission model, and historical reconstructions of the 1918, 1957, and 1968 pandemics using mortality data. We evaluated official planning scenarios for the United Kingdom, Japan, and the United States, comparing projected demand against national surge capacities (i.e., staffed beds). To test the theoretical limits of the stockpile strategy, we applied best-case assumptions, including perfect distribution of antivirals to 100% of symptomatic cases and high clinical effectiveness (30% to 60% reduction in hospitalization). Our models identified a critical feasibility gap. While stockpiles successfully suppressed demand to manageable levels in moderate scenarios (analogous to 1957/1968), they were structurally insufficient for severe events. In high-severity scenarios (analogous to 1918 or CDC Very Severe parameters), the daily surge of patients exceeded available hospital capacity by a factor of 5–9. This inundation occurred even under the assumption of 100% treatment coverage, rendering antivirals incapable of preventing transition to crisis standards of care. Although this analysis is subject to the inherent uncertainties of future pandemics, our use of optimistic best-case assumptions suggests that the identified structural failures are a lower-bound estimate. Realistic operational hurdles would only widen the disparity between stockpile targets and system capacity. The main limitations of this study’s methodology are its inherent optimistic bias, specifically the use of a conservative 4-day hospital length of stay that underestimates prolonged intensive care needs, the reliance on general hospital beds as a proxy that abstracts away from critical sub-system bottlenecks, and the assumption of guaranteed antiviral efficacy against novel strains. Conclusions: This study demonstrates that large reserves cannot overcome the rigid physical constraints of healthcare systems, even under the optimistic assumption of perfect distribution. Consequently, the conventional 25% coverage target for antiviral stockpiles, a hallmark of legacy planning, presents a critical opportunity for re-evaluation. As global stocks near expiration, policymakers are uniquely positioned to leverage this inflection point by exploring a capacity-based framework. This adaptive paradigm suggests incorporating a synergistic mix of targeted reserves, temporary non-pharmaceutical interventions, and flexible delivery options, such as decentralized home-first care. Together, these integrated measures could serve as a vital temporal bridge, preserving system integrity until vaccine deployment.
SN - 1549-1676
SN - 1549-1277
ER -