Assessing the Readiness of Local Vaccine Manufacturing in African Countries: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Although Africa experiences the highest burden of infectious diseases, the continent currently produces less than 1% of its vaccine needs. In 2021, the African Union set a target to locally produce at least 60% of the continent's vaccine needs by 2040. However, at the time of developing this scoping review protocol, there is no consolidated, evidence-based framework for assessing national or regional "readiness" to establish or scale vaccine production. OBJECTIVE: This protocol aims to describe a methodological approach that will be used to review existing literature to identify, map, and synthesize the existing evidence on all relevant frameworks, indicator sets, and policy documents (global or national) developed pre- and post-COVID-19 pandemic (January 1, 2010, to December 31, 2025) that addresses readiness for local human vaccine manufacturing with focus on African countries. METHODS: This scoping review will be conducted and reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, following the 9-step framework outlined in the Arksey and O'Malley methodology and further informed by guidance from the Joanna Briggs Institute. We will search MEDLINE (PubMed), Scopus, Web of Science, Africa-focused databases (eg, Africa-Wide Information, African Index Medicus, and African Journals Online), and gray literature. Eligibility criteria will follow Population, Concept, and Context guidelines (Population: 55 African Union member states; Concept: readiness frameworks, indices, indicators, and policies for human vaccine manufacturing; Context: African national or regional initiatives or global frameworks applied to Africa). Materials in English, French, Portuguese, or Arabic will be included. Publication types will be limited to frameworks, policies, guidance, and reports. Two reviewers will perform calibrated dual screening (Cohen κ) and standardized data charting. We will create an evidence map and inductive thematic synthesis using a vaccine-specific Political, Economic, Social, Technological, Legal, Environmental, plus Market taxonomy. Consistent with the guidance by the Joanna Briggs Institute, critical appraisal will not be performed. An optional expert consultation will help identify missed sources and validate domains. RESULTS: Ethics approval for the expert consultation component was obtained from the University of Geneva Research Ethics Committee (application submitted May 21, 2025; approval September 9, 2025). The initial search strategy has been finalized, and pilot searches were completed (May-August 2025). The screening calibration is planned; dual-review title and abstract screening begins in December 2025, with full-text screening and data charting scheduled for January-March 2026. Thematic synthesis and expert consultation are planned from April to May 2026. We anticipate submitting the completed scoping review paper by June or July 2026. CONCLUSIONS: This review will generate Africa's first continent-focused evidence map of vaccine-manufacturing readiness, compiling indicators by domain, comparing frameworks, identifying gaps, and informing a multidomain Country Readiness Assessment Index for policy and investment decisions. TRIAL REGISTRATION: Open Science Framework 10.17605/OSF.IO/UVSWX; https://osf.io/uvswx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/81231.
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 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 | 0.187 | 0.183 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.073 | 0.018 |
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".