Lessons learned in shaping vaccine markets in low-income countries: a review of the vaccine market segment supported by the GAVI Alliance
Bibliographic record
Abstract
OBJECTIVES: The Global Alliance for Vaccines and Immunization (GAVI) anticipated that growing demand for new vaccines could sufficiently impact the vaccines market to allow low-income countries (LICs) to self-finance new vaccines. But the time required to lower vaccine prices was underestimated and the amount that prices would decline overestimated. To better understand how prices in the LIC vaccine market can be impacted, the vaccine market was retrospectively examined. DESIGN: GAVI archives and the published literature on the vaccine markets in LICs were reviewed for the purpose of identifying GAVI's early assumptions for the evolution of vaccine prices, and contrasting these retrospectively with actual outcomes. RESULTS: The prices in Phases I and II of GAVI-supported vaccines failed to decline to a desirable level within a projected 5-year timeframe. GAVI-eligible countries were unable to sustain newly introduced vaccines without prolonged donor support. Two key lessons can be applied to future vaccine market-shaping strategies: (1) accurate demand forecasting together with committed donor funding can increase supply to the LIC vaccines market, but even greater strides can be made to increase the certainty of purchase; and (2) the expected time to lower prices took much longer than 5 years; market competition is inherently linked to the development time for new vaccines--a minimum of 5-10 or more years. Other factors that can lower vaccine prices include: large-scale production or alternate financing mechanisms that can hasten vaccine price maturation. CONCLUSIONS: The impacts of competition on vaccine prices in the LIC new-vaccines market occurred after almost 10 years. The time for research and development, acquisition of technological know-how and to scale production must be accounted for to more accurately predict significant declines on vaccine prices. Alternate financing mechanisms and the use of purchase agreements should also be considered for lowering prices when planning new vaccine introductions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".