SWOT Analysis of Communicable Disease Surveillance in Sudan
Bibliographic record
Abstract
Effective communicable disease surveillance is critical in Sudan to addressing the compounded health impacts of concurrent epidemics, health systems collapse and acute conflict. This article aims to map the strengths, weaknesses, opportunities and threats of Sudan's communicable disease surveillance systems before the current conflict to inform future health system rebuilding efforts. Despite existing for 50 years, little is published on Sudan's disease surveillance systems. We conducted a scoping review to map the existing evidence on Sudan's surveillance systems and utilized a strength, weakness, opportunities and threats (SWOT) analysis to identify current and future gaps and opportunities to improve the performance of these systems for communicable diseases in Sudan. Our review shows that, prior to the conflict, disease-specific surveillance and response activities were fragmented across various divisions of the Federal Ministry of Health, hindering a clear national-level hierarchy. Sudan has committed to strengthening its disease surveillance system as part of its national health sector policy. Efforts to bolster pandemic preparedness and response were and continue to be recognized as critical. Chiefly among them is the need to invest in a fit-for-purpose national surveillance system that can operate against a background of acute crisis. Greater transparency and data sharing, clear guidelines for communication and collaboration and a centralized data management system can enhance the effectiveness of Sudan's communicable disease surveillance systems. Investment in a consolidated national surveillance system can support more efficient and coordinated responses to outbreaks and other health emergencies, with a view to future health system reconstruction.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".