Financing pandemic prevention, preparedness and response: lessons learned and perspectives for future
Bibliographic record
Abstract
BACKGROUND: The attainment of global health security goals and universal health coverage will remain a mirage unless African health systems are adequately funded to improve resilience to public health emergencies. The COVID-19 pandemic exposed the global inequity in accessing medical countermeasures, leaving African countries far behind. As we anticipate the next pandemic, improving investments in health systems to adequately finance pandemic prevention, preparedness, and response (PPPR) promptly, ensuring equity and access to medical countermeasures, is crucial. In this article, we analyze the African and global pandemic financing initiatives and put ways forward for policymakers and the global health community to consider. METHODS: This article is based on a rapid literature review and desk review of various PPPR financing mechanisms in Africa and globally. Consultation of leaders and experts in the area and scrutinization of various related meeting reports and decisions have been carried out. MAIN TEXT: The African Union (AU) has demonstrated various innovative financing mechanisms to mitigate the impacts of public health emergencies in the continent. To improve equal access to the COVID-19 medical countermeasures, the AU launched Africa Medical Supplies Platform (AMSP) and Africa Vaccine Acquisition Trust (AVAT). These financing initiatives were instrumental in mitigating the impacts of COVID-19 and their lessons can be capitalized as we make efforts for PPPR. The COVID-19 Response Fund, subsequently converted into the African Epidemics Fund (AEF), is another innovative financing mechanism to ensure sustainable and self-reliant PPPR efforts. The global initiatives for financing PPPR include the Pandemic Emergency Financing Facility (PEF) and the Pandemic Fund. The PEF was criticized for its inadequacy in building resilient health systems, primarily because the fund ignored the prevention and preparedness items. The Pandemic Fund is also being criticized for its suboptimal emphasis on the response aspect of the pandemic and non-inclusive governance structure. CONCLUSIONS: To ensure optimal financing for PPPR, we call upon the global health community and decision-makers to focus on the harmonization of financing efforts for PPPR, make regional financing mechanisms central to global PPPR financing efforts, and ensure the inclusivity of international finance governance systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".