COVID-19 rapid diagnostic test could contain transmission in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has impacted heavily on global health. Although real-time polymerase chain reaction (RT-PCR) is the current diagnostic method, challenges for low- and middle-income countries (LMICs) necessitate cheaper, higher-throughput, reliable rapid diagnostic tests (RDTs). OBJECTIVE: We reviewed the documented performance characteristics of available COVID-19 RDTs to understand their public health utility in the ongoing pandemic, especially in resource-scarce LMIC settings. METHODS: Using a scoping review methodology framework, common literature databases and documentary reports were searched up to 22 April 2020, irrespective of geographical location. The search terms included 'SARS-CoV-2 AND serological testing' and 'COVID-19 AND serological testing'. RESULTS: A total of 18 RDTs produced in eight countries, namely China (6; 33.33%), the United States (4; 22.22%), Germany (2; 11.11%), Singapore (2; 11.11%), Canada, Kenya, Korea and Belgium (1 each; 5.56%), were evaluated. Reported sensitivity ranged from 18.4% to 100% (average = 84.7%), whereas specificity ranged from 90.6% to 100% (average = 95.6%). The testing time ranged from 2 min to 30 min. Of the 12 validated RDTs, the IgM/IgG duo kit with non-colloidal gold labelling system was reported to elicit the highest sensitivity (98% - 100%) and specificity (98% - 99% for IgG and 96% - 99% for IgM). CONCLUSION: We found reports of high sensitivity and specificity among the developed RDTs that could complement RT-PCR for the detection of SARS-CoV-2 antibodies, especially for screening in LMICs. However, it is necessary to validate these kits locally.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".