📈 economics

Costs and impacts of vaccine preparedness investments for SARS-X pandemics

This paper demonstrates that strategic pre-pandemic investments in vaccine preparedness—particularly those accelerating vaccine availability, combining R&D with manufacturing capacity, and ensuring equitable global distribution—can significantly reduce the projected economic and human losses of future SARS-X outbreaks.

Original authors: Rob johnson, Patrick Doohan, Peter Windus, Andrew Torkelson, Damian Walker, Charles Whittaker, Gemma Nedjati-Gilani, Christian Morgenstern, Guillaume Morel, Marisa Miraldo, Giovanni Forchini, Fatima D

Published 2026-07-01

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Rob johnson, Patrick Doohan, Peter Windus, Andrew Torkelson, Damian Walker, Charles Whittaker, Gemma Nedjati-Gilani, Christian Morgenstern, Guillaume Morel, Marisa Miraldo, Giovanni Forchini, Fatima Docrat, Saul Walker, Dimitrios Gouglas, Alice Sabino, Katharina Hauck

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the world is a giant ship sailing through a foggy ocean. We know a massive storm (a new pandemic virus, which the authors call "SARS-X") could hit us at any time, but we don't know exactly when or how big it will be.

This paper is like a detailed engineering report asking: "How much money should we spend right now to build better lifeboats and storm shields, so that when the storm hits, we lose fewer lives and less cargo?"

The researchers didn't just guess; they built a computer simulation to test different "preparedness plans" and compared them to doing nothing extra (which they call "Business as Usual").

Here is a simple breakdown of their findings:

1. The "Business as Usual" Storm

If we do nothing new and wait for a virus to appear before we start making vaccines, the storm will be very expensive.

The Cost: The authors estimate that without extra preparation, a pandemic would cost the world roughly 0.4% to 1.1% of all the money the world earns every year (Global GDP).
The Delay: In this "do nothing" scenario, it takes about 365 days (one year) to develop a new vaccine after the virus is discovered. During that year, the virus spreads, people get sick, and economies shut down.

2. The Four "Lifeboat" Strategies Tested

The researchers tested ten different scenarios, but they boil down to four main ideas:

Idea A: The "Universal Shield" (Broadly Protective Vaccine)
- The Metaphor: Imagine building a shield that protects against any type of rain, not just a specific storm.
- The Plan: Develop a vaccine that works against a whole family of viruses before the new one arrives.
- The Result: This is a huge bargain. It costs very little to build the shield in advance, but if a storm hits, it saves a lot of money and lives immediately. It's like having a fire extinguisher already mounted on the wall.
Idea B: The "Reserved Seats" (Manufacturing Capacity)
- The Metaphor: Imagine a theater. Usually, when a play starts, you have to build the stage and buy tickets after the show is announced.
- The Plan: Pay a small annual fee to "reserve" the seats and the stage crew now. If a pandemic hits, you don't have to wait to build the factory; you just flip a switch and start producing immediately.
- The Result: This saves a massive amount of money during the crisis because you don't have to pay "emergency prices" to build factories from scratch. It also ensures the vaccine arrives faster.
Idea C: The "Speed Run" (Faster Vaccine Development)
- The Metaphor: Imagine a race car team. Usually, it takes a year to tune the engine for a new track.
- The Plan: Invest in research now so that when the new virus arrives, we can tune the engine in 100 days instead of 365.
- The Result: This is the most powerful single investment. The faster the vaccine arrives, the less damage the storm does. Cutting the time from a year to 100 days drastically reduces the economic and human cost.
Idea D: The "Fair Distribution" (Equal Access)
- The Metaphor: Imagine a lifeboat that only lets the people in the front row (rich countries) on first, while the people in the back (poorer countries) wait until the boat is full.
- The Plan: Change the rules so that the lifeboat fills up based on how many people are on the ship, not how rich they are.
- The Result: Surprisingly, this costs nothing extra to set up, but it saves money overall. Why? Because if rich countries get too many vaccines too fast, they can't use them all, and the vaccines sit idle while the virus keeps spreading in other parts of the world. Sharing them evenly stops the virus faster everywhere.

3. The Big Wins (What Happens When You Mix Them)

The researchers found that the best results come from combining these strategies, like mixing ingredients in a recipe:

The "Double Whammy": If you combine "Reserved Seats" (Idea B) with "Speed Run" (Idea C), the benefit is greater than the sum of the two parts. Having the factory ready and the science ready means the vaccine arrives incredibly fast and cheaply.
The "100-Day Mission": The most expensive but most effective plan was to aim for a vaccine in 100 days plus having a massive factory reservation. This offered the biggest protection against the storm.

4. The Bottom Line

The paper concludes that spending money now is like paying a small insurance premium to avoid a catastrophic loss later.

The Cost of Doing Nothing: Losing 0.4% to 1.1% of the world's annual income every year on average due to pandemic risks.
The Cost of Investing: A few billion dollars upfront and some annual fees.
The Payoff: These investments can significantly reduce the "storm damage." Specifically, getting vaccines to people faster (whether by making them broadly protective or just faster to make) is the single biggest way to save money and lives.

In short: The paper argues that we shouldn't wait for the storm to start building the lifeboats. Investing in the "blueprints" (research), the "dry docks" (factories), and the "fair rules" (distribution) today will save the world trillions of dollars and millions of lives tomorrow.

Technical Summary: Costs and Impacts of Vaccine Preparedness Investments for SARS-X Pandemics

Problem Statement
The paper addresses the economic and health trade-offs associated with advance investments in vaccine preparedness for future SARS-X pandemics (novel sarbecoviruses). While the rapid development of COVID-19 vaccines was a scientific success, the response was characterized by significant delays in availability (approx. 365 days from sequencing), regressive distribution favoring high-income countries (HICs), and substantial global economic losses. The authors seek to quantify the costs and potential benefits of specific preparedness strategies—specifically Broadly Protective Sarbecovirus Vaccines (BPSVs), accelerated Strain-Specific Vaccine (SSV) development (the "100 Days Mission"), manufacturing capacity reservations, and equitable distribution—compared to a "Business as Usual" (BAU) counterfactual. The core challenge is to model these investments within an idealized policy framework to isolate the marginal value of vaccine-specific levers (R&D, manufacturing, delivery) without predicting chaotic real-world political failures.

Methodology
The study employs a "What-If" analytical framework combining a costing model with an economy-structured epidemiological model (Daedalus).

Scenario Design: Ten distinct investment scenarios were modeled against a BAU counterfactual (365-day SSV availability, no BPSV, HIC-first distribution, limited capacity reservations). Scenarios varied by:
- BPSV Availability: Development of a broadly protective vaccine held in an investigational reserve.
- SSV Timeline: Reducing time-to-authorization from 365 days to 200 or 100 days.
- Capacity Reservations: Pre-booking manufacturing capacity (700 million or 2 billion doses annually) to lower procurement costs and accelerate transition.
- Distribution Equality: Shifting from HIC-first to proportional global distribution and increasing administration rates in Low- and Lower-Middle-Income Countries (LLMICs).
Costing Model: A stochastic model estimated costs across three categories:
- Upfront Preparedness: One-off R&D costs (weighted by pipeline success probabilities) and investigational reserve maintenance.
- Annual Costs: Recurring fees for capacity reservations and reserve maintenance.
- Response Costs: Procurement (distinguished between reserved vs. unreserved capacity prices) and delivery costs (scaled by income group and rollout phase).
- Note: Costs were discounted to 2025 USD, and uncertainty was handled via 10,000 Monte Carlo simulations.
Epidemiological and Economic Impact Model:
- Synthetic Countries: The authors generated synthetic country profiles for LLMIC, UMIC, and HIC categories by resampling data from existing nations to address data gaps.
- Epidemic Simulation: Using the Daedalus model, they simulated 2,048 potential SARS-X pathogens with varying transmission and fatality rates. For each pathogen, four sector-closure policies were tested, and the policy minimizing societal loss was selected (an "ideal response" assumption).
- Loss Metrics: Societal loss ( $L_{IR}$ ) was calculated as the sum of Years of Life Lost (valued via Value of a Statistical Life), short-term GDP loss (from mandated closures), and long-term educational/economic loss.
- Aggregation: Epidemic outcomes were aggregated to estimate pandemic-level impacts using exceedance probabilities derived from historical outbreak data. The primary metric is $\Delta L_{IR}$ , the annualized difference in expected losses between a scenario and BAU, expressed as a percentage of global GDP.

Key Contributions and Results
The study provides a comparative analysis of investment efficiency, revealing three consistent patterns:

Time Reduction is Paramount: Reducing the time to SSV availability yielded the largest reductions in expected pandemic losses.
- Accelerating SSV availability to 100 days (Scenario S07–S09) resulted in a $\Delta L_{IR}$ of 0.028–0.13% of global GDP per year, compared to 0.016–0.044% for 200 days.
- The upfront R&D costs for 100-day readiness were estimated at $7.6–8.6 billion.
Synergy of R&D and Capacity Reservations: Combining R&D investments with manufacturing capacity reservations produced impacts greater than the sum of their parts.
- Capacity reservations alone (S02, S03) reduced procurement costs significantly (up to $110 billion savings per pandemic for 2 billion doses) but had modest impacts on $\Delta L_{IR}$ (0.0039–0.019% of GDP).
- When combined with R&D (e.g., S09: 100-day SSV + 2 billion dose reservation), the $\Delta L_{IR}$ reached 0.046–0.13% of global GDP.
Equality Generates Benefits at No Cost: Improving global distribution equality (Scenario S10) generated measurable benefits without increasing preparedness costs.
- By distributing doses proportionally to population and increasing LLMIC administration rates, the model showed reduced "idle" doses in HICs and higher overall coverage.
- This scenario yielded a $\Delta L_{IR}$ of 0.0076–0.021% of global GDP per year solely through logistical optimization.
BPSV Impact: Developing a BPSV (Scenario S01) required a relatively low upfront investment ($210–370 million) and yielded a $\Delta L_{IR}$ of 0.038–0.11% of global GDP, driven by spillover effects that expedited SSV production by 18 weeks.

Significance and Claims
The authors position this work as a comparative analytical construct rather than a prediction of real-world outcomes. By modeling an "ideal response" where policy mistakes are minimized, the results represent the maximum potential gains from specific vaccine levers.

Scale of Impact: The study contextualizes SARS-X pandemic losses (counterfactual annualized loss of 0.39–1.1% of global GDP) against other global threats like climate change and antimicrobial resistance (AMR). While SARS-X losses are an order of magnitude smaller than projected climate damages, the study argues that preparedness investments in the order of billions of USD can avert losses in the trillions.
Policy Relevance: The findings support the "100 Days Mission" and the strategic value of capacity reservations. The authors note that while their model excludes broader preparedness systems (surveillance, regulatory), the specific levers of vaccine timing, manufacturing, and distribution offer high-value interventions.
Limitations: The authors explicitly acknowledge limitations, including the use of synthetic countries, the assumption of ideal policy responses (excluding chaotic or suboptimal real-world scenarios), and the exclusion of non-vaccine mitigation costs. They state that the model likely underestimates total pandemic losses but provides a robust framework for comparing the marginal value of different investment strategies.

In conclusion, the paper demonstrates that targeted, advance investments in vaccine technology and manufacturing capacity can significantly reduce the health and economic burden of future SARS-X pandemics, with the most effective strategy being a combination of accelerated R&D, reserved manufacturing capacity, and equitable distribution.

1. The "Business as Usual" Storm

2. The Four "Lifeboat" Strategies Tested

3. The Big Wins (What Happens When You Mix Them)

4. The Bottom Line

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