Politics of disease control in Africa and the critical role of global health diplomacy: A systematic review
Bibliographic record
Abstract
Background: Africa is facing the triple burden of communicable diseases, non-communicable diseases (NCDs), and nutritional disorders. Multilateral institutions, bilateral arrangements, and philanthropies have historically privileged economic development over health concerns. That focus has resulted in weak health systems and inadequate preparedness when there are outbreaks of diseases. This review aims to understand the politics of disease control in Africa and global health diplomacy’s (GHD’s) critical role. Methods: A literature review was done in Medline/PubMed, Web of Science, Scopus, Embase, and Google scholar search engines. Keywords included MeSH and common terms related to the topics: "Politics," "disease control," "epidemics/ endemics," and "global health diplomacy" in the "African" context. The resources also included reports of World Health Organization, United Nations and resolutions of the World Health Assembly (WHA). Results: African countries continue to struggle in their attempts to build health systems for disease control that are robust enough to tackle the frequent epidemics that plague the continent. The politics of disease control requires the crafting of cooperative partnerships to accommodate the divergent interests of multiple actors. Recent outbreaks of COVID-19 and Ebola had a significant impact on African economies. It is extremely important to prioritize health in the African development agendas. The African Union (AU) should leverage the momentum of the rise of GHD to (i) navigate the politics of global health governance in an interconnected world(ii) develop robust preparedness and disease response strategies to tackle emerging and reemerging disease epidemics in the region (iii) address the linkages between health and broader human security issues driven by climate change-induced food, water, and other insecurities (iv) mobilize resources and capacities to train health officials in the craft of diplomacy. Conclusion: The AU, Regional Economic Communities (RECs), and African Centres for Disease Control should harmonize their plans and strategies and align them towards a common goal that integrates health in African development agendas. The AU must innovatively harness the practice and tools of GHD towards developing the necessary partnerships with relevant actors in the global health arena to achieve the health targets of the Sustainable Development Goals.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".