Strengthening Preparedness for Infectious Disease Outbreaks in Sub-Saharan Africa: Lessons from Recent Outbreaks
Bibliographic record
Abstract
One of the major public health emergencies that has affected lives globally is infectious disease outbreaks. These issues are of great concern due to their potential to transcend borders. The control and management of such outbreaks even with the attention channelled towards it globally has been a difficult task in many developing and underdeveloped countries of the world of which the majority of sub-saharan african countries fall under. However, with this review, we aim to contribute to the body of knowledge dedicated towards control of infectious diseases by analyzing the preparedness of Sub-Saharan African (SSA) countries in managing infectious disease outbreaks based on lessons from recent outbreaks (with focus on COVID-19, Lassa fever and Ebola outbreaks). In carrying out this narrative review, we make use of PubMed and African Journals Online (AJOL) as the primary literature sources. To ensure we capture publications from reputable organizations that are solely involved in control of infectious diseases in the region, we carried out a grey literature search. However in this review, we synthesized challenges such as weak healthcare systems, inadequate healthcare infrastructure, inefficient surveillance systems, poor data management and reporting practices, limited laboratory capacity and reliance on external donors for supplies during emergencies. The review proposes potential interventional measures aimed at addressing these challenges aimed at enhancing the preparedness The findings from this review provide critical insights into the preparedness gaps and potential interventions, informing policy and practice to enhance the region's resilience future outbreaks.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".