Arbovirus Epidemics as Global Health Imperative, Africa, 2023
Bibliographic record
Abstract
Arboviruses represent a major cause of illness in Africa and have the potential to trigger widespread epidemics. We present data on arbovirus epidemics in Africa in 2023 and demonstrate the need for global public health authorities to intensify efforts in the surveillance and control of arbovirus diseases. Data were collected from the World Health Organization Weekly Bulletin on Outbreaks and Other Emergencies, Africa Centers for Disease Control and Prevention Weekly Event Based Surveillance Report, and other online sources. In 2023, a total of 7 arboviruses were responsible for 29 outbreaks across 25 countries in Africa, 22 of which occurred in West Africa; the outbreaks resulted in 19,569 confirmed cases and 820 deaths. Arbovirus epidemics in Africa pose a threat not only to public health within the continent but also globally, underscoring the urgent need for substantial investment in arbovirus surveillance, research, and preparedness capacities in Africa to prevent and respond to health crises effectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".