Diagnosis of COVID-19 for controlling the pandemic: A review of the state-of-the-art
Bibliographic record
Abstract
To date, health organizations and countries around the world are struggling to completely control the spread of the coronavirus disease 2019 (COVID-19). Scientists and researchers are developing tests for the rapid detection of individuals who may carry the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while striving to find a suitable vaccine to immunize healthy individuals. As there are clinically reported cases of asymptomatic carriers of SARS-CoV-2, fast and accurate diagnosis plays an important role in the control and further prevention of this disease. Herein, we present recent technologies and techniques that have been implemented for the diagnosis of COVID-19. We summarize the methods created by different research institutes as well as the commercial devices and kits developed by companies for the detection of SARS-CoV-2. The description of the existing methods is followed by highlighting their advantages and challenges. Finally, we propose some promising techniques that could potentially be applied to the detection of SARS-CoV-2, and tracing the asymptomatic carriers of COVID-19 rapidly and accurately in the early stages of infection, based on reviewing the research studies on the detection of similar infectious viruses, especially severe acute respiratory syndrome (SARS) coronavirus, and Middle East respiratory syndrome (MERS) coronavirus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".