The first 100 days of the COVID-19 epidemic in Mali: a descriptive analysis
Bibliographic record
Abstract
Abstract Background Since the detection of the first cases of COVID-19 in Mali, the ministry of health provides daily released of information and situation report including information on the number of testing, confirmed cases, case-contacts, recovered patients, COVID-19 related deaths; and the geographic locations affected by the epidemic. The objective of this study was to analyze this information and to examine the relation between the number of confirmed cases and the number of testing, case-contacts, recovered patients and COVID-19 related deaths. Method From the daily released of information and situation reports, the data related to the number of testing, confirmed cases, case-contacts, recovered patients, COVID-19 related deaths; and the affected geographic locations were extracted on an Excel file before being analyzed with SPSS 25 software. The analyses were essentially descriptive including Spearman correlation test and Chi 2 test for statistical significance (p≤0, 05).Results The analyses include 14,938 testing, 2,260 PCR confirmed cases, 12, 864 case-contacts, 1,502 recovered patients and 117 deaths reported during the first 100 days of the epidemic, particularly from March 25 to July 2, 2020. The results show low level of testing and demonstrate a positive correlation between the number of confirmed cases and the number of testing, case-contacts, recovered patients and deaths. These results suggest that Mali could have more confirmed cases by increasing testing, particularly among case-contacts.Conclusion The results can help to understand the evolution of the epidemic, call for more testing and contact tracing of COVID-19 cases. They can also contribute to improving data quality and response to COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".