Covid 19 in sub-Saharan Africa: Is it the calm before the storm?
Bibliographic record
Abstract
We proposed several hypotheses to explain the low rate of Covid19 in Sub-Sahara Africa (SSA). The small number of people tested for Covid19, a younger population, higher immunity to covid19, and seasonality emerged as potential factors influencing the Covid 19 rate in SSA. Rigorous responses to covid19 to flatten the curve are urgently needed and will include (1) a substantial increase of Covid19 testing and the use of cellphone location to trace contact, (2) complete lockdowns with social distancing measures followed by an assessment of the impact of those interventions to flatten the curve, (3) the use of prior experience with Ebola outbreak to increase awareness about the Covid-19 and its fatal complications, (4) warnings about potential side effects associated with the use of chloroquine/hydroxychloroquine with azithromycin and (5) the maintenance of essential health services
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".