African leaders and regional institutions need to take the bull by the horns: a perspective on the impact of the 2025 funding cuts to malaria programmes
Bibliographic record
Abstract
Despite global malaria programmes already operating within resource constraints, 2025 saw a significant decrease in funding following the US government's termination of most of its global health programmes, as well as the decline in development aid spending by the UK, France, Germany, Canada, Switzerland, and other countries. The disruption of funding was sudden, with most African countries lacking adequate contingency plans. This, despite most of the malaria burden being in Africa (94% of 263 million cases in 2023), accounting for a reduction in gross domestic product of up to 1.3% annually, and half a billion lost workdays. Key malaria control programme activities have been severely impacted, including insecticide-treated bed net distribution, seasonal malaria chemoprevention campaigns, and malaria indicator surveys. In the wake of the funding cuts, some African governments have committed to increasing efforts to raise funds for malaria programmes from the private sector. The Africa Centres for Disease Control and Prevention (CDC) has developed a strategy for governments to increase health budgets while seeking additional funding from the private sector, all while maintaining transparency and accountability. If recent malaria control gains are to be sustained and to prevent resurgence across the continent, African governments will need to increase domestic funding and build robust public-private partnerships for their malaria programmes. Lessons can be learnt from countries where these partnerships have succeeded or failed. Leadership by the African Union, the Africa CDC, the African Leaders Malaria Alliance, and other regional bodies is crucial to support countries in taking immediate, substantive steps and benchmarking progress.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".