Impact of poor disease surveillance system on COVID-19 response in africa: Time to rethink and rebuilt
Bibliographic record
Abstract
Infectious disease outbreaks have long posed a public health threat, especially in Africa, where the incidence of infectious outbreaks has risen exponentially. Although, Africa has witnessed several outbreaks of emerging and re-emerging infectious diseases such as Ebola virus disease and other epidemic-prone diseases, little attention has been given towards strengthening the health surveillance systems. However, the recent COVID-19 pandemic has uncovered the region's already due to inefficient and ineffective health surveillance systems. However, the impact posed by the COVID-19 pandemic on health systems in the region has been catastrophic, it has also stressed the importance of rethinking and focusing on lessons learned during the COVID-19 pandemic. In this paper, we examine how Africa's poor disease surveillance systems affected the responses and strategies aimed at COVID-19 containment. To ensure early disease outbreak identification and prompt public health interventions in Africa, the current disease surveillance and response mechanisms must be strengthened.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".