Vaccine Production in Africa: A Feasible Business Model for Capacity Building and Sustainable New Vaccine Introduction
Bibliographic record
Abstract
Africa has the highest incidence of mortality caused by infectious diseases, and remarkably does not have the capacity to manufacture vaccines that are essential to reduce mortality, improving life expectancy, and promoting economic growth. GAVI has significantly helped introduction of new vaccines in Africa but its sustainability is questionable, and new vaccines introduction post-graduation is rare. Conversely, Africa with its high population and economy growth is an increasing potential market for vaccines. This study aimed to investigate how investment for vaccine production in Africa could be triggered and in which way it could be affordable to most African governments or investors. The investigation was based on a literature review and supplemented by online questionnaires directed to global vaccine stakeholders, African governments and regulatory authorities. In-depth interviews with experts in manufacturing capacity implementation and regulatory capacity building in Africa complemented the study. We also developed business plan scenarios including facility costs calculations and a possible investment plan based on expert opinions and publicly available information from pertinent sources. We saw that, governments in Africa, show interest in vaccine production establishments but only with external support for investment. The common regulatory functionality gap was the quality control laboratories to test vaccine lots before regulatory release. The global vaccine stakeholders showed less preference in investment for vaccine production establishment in Africa. The diverse political ambitions among African governments make it difficult to predict and access the market, a prerequisite for competitive production. A feasible solution could be a small production facility that would use technologies with high yield at low costs of goods to cover the regional needs. A respective antigen production facility is estimated to cost USD 25 Million, an affordable dimension for investors or interested African governments. Attractiveness for the African market is deemed to be high when targeting diseases almost exclusively for Africa (e.g., malaria or invasive non-typhoidal salmonella). With a smart 5 years tangible implementation plan, marketing agreements within existing regional collaborations and with a strong political will, an African government alone or together with an investor could convince global vaccine stakeholders and investors to support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".