Molecular diagnosis of COVID-19 in Burkina Faso: successful challenge
Bibliographic record
Abstract
COVID-19 has worsened the health situation in Burkina Faso. In fact, the country has known a peak of the second wave, which began in November, and ended around January 2021. Biological diagnosis has played a key role in the management of COVID-19. The aim of this review paper is to address the practical aspects that laboratories have faced in order to meet the challenge of SARS-CoV-2 diagnosis in Burkina Faso. According to international requirements, Burkina Faso has used real-time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) as the “gold standard” for the diagnosis of COVID-19. From March 9, 2020 to July 31, 2021, in Burkina Faso, laboratories involved in COVID-19 diagnosis analyzed 226,189 samples by molecular tests and 2, 352 samples by rapid antigenic tests, whose peak was in January 2021 with 35,984 samples analyzed. The daily average rate of samples analysis was 456.02 tests. The majority of the individuals requesting COVID-19 tests were travelers (62.00%), followed by contact cases (18.42%), suspected cases (7.95%), voluntary screening (7.57%), and 4.06% of other applicants consisting of health care personnel and at-risk patients. In terms of prevention, vaccines are being administered to the general population. However, some efforts must be made to provide automated sample analysis equipment and complete sequencing of SARS-CoV-2 remains among the challenges.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".