Transforming pandemic influenza antiviral stockpiling using a capacity-based approach: A modeling study

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). 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. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this modeling study, Nir Gavish and Amit Huppert examine whether traditional pandemic influenza antiviral stockpiles can keep hospital demand within available healthcare capacity during future pandemics. Using multiple modeling approaches and historical pandemic scenarios, they find that while stockpiles may help in moderate outbreaks, they are unlikely to prevent severe capacity pressures in high-impact scenarios, highlighting the potential value of preparedness strategies that place greater emphasis on healthcare system capacity and operational flexibility.