Toward a New Paradigm of North–South and South–South Partnerships for Pandemic Preparedness: Lessons Learned from COVID-19 and Other Outbreaks
Bibliographic record
Abstract
COVID-19 underscores the need to reimagine North-South partnerships and redefine best practices for building public health and research capacity to address emergent health threats and pandemic preparedness in low- and-middle income countries (LMICs). Historically, outbreak and emergency responses have failed to ensure that the Global South has the autonomy and capacity to respond to public health threats in a timely and equitable manner. The COVID-19 response, however, has demonstrated that innovations and solutions in the Global South can not only fill resource and capacity gaps in LMICs but can also provide solutions to challenges globally. These innovations offer valuable lessons about strengthening local manufacturing capacity to produce essential diagnostic, treatment, and prevention tools; implementing high-quality research studies; expanding laboratory and research capacity; and promoting effective cooperation and governance. We discuss specific examples of capacity-building from Rwanda, South Africa, and Senegal. To fulfill promises made to the Global South during the COVID-19 pandemic, restore and resume health service delivery, and effectively prevent and respond to the next health threat, we need to prioritize equitable access to local manufacturing of basic health tools while building health systems capacities in the Global South.
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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 | 0.035 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".