Multiple factors shape technology transfer for the development and manufacture of vaccines in Latin America and the Caribbean
Bibliographic record
Abstract
The COVID-19 pandemic highlighted significant inequalities in access to medicines and emergency supplies, including vaccines, that persist in Latin America and the Caribbean. From a regional perspective, it is necessary to improve the conditions to ensure more equitable and inclusive access to health technologies, both in normal scenarios and during future biological threats. Technology Transfer emerges as an effective tool to permanently avoid scarcity in global and regional vaccine supplies. Here we describe the global and regional ecosystem of Technology Transfer, its actors, roles, interactions, and evolution through research of publicly available documents and interviews with experts from the region and international institutions. Additionally, we identify and analyze vaccine projects, characterize typologies of projects in the region, suggest an evolution of three temporal phases, reveal lessons from the COVID-19 pandemic and identify four drivers that expedite vaccine Technology Transfer in Latin America and the Caribbean. These drivers include (i) strengthening of regulatory capacities for vaccines; (ii) adoption of trade standards; (iii) increasing manufacture capacity, R&D, and human resources; and (iv) consideration of aggregated demand. Finally, we present recommendations to maximize the potential of scientific-technological and vaccine production capacities in Latin American and the Caribbean. They relate to the four drivers, the promotion of complementary industries, data access and availability policies, inter-institutional dialogue and coordination, public health considerations, and future work in areas of information opacity.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".