A collection of the novel coronavirus (COVID-19) detection assays, issues, and challenges
Bibliographic record
Abstract
The global pandemic of COVID-19 has rapidly increased the number of infected cases as well as asymptomatic individuals in many, if not all the societies around the world. This issue increases the demand for accurate and rapid detection of SARS-CoV-2. While accurate and rapid detection is critical for diagnosing SARS-CoV-2, the appropriate course of treatment must be chosen to help patients and prevent its further spread. Testing platform accuracy with high sensitivity and specificity for SARS-CoV-2 is equally important for clinical, regional, and global arenas to mitigate secondary transmission rounds. The objective of this article is to compare the current detection technology and introduce the most accurate and rapid ones that are suitable for pandemic circumstances. Hence, the importance of rapid detection in societies is discussed initially. Following this, the current technology for rapid detection of SARS-CoV-2 is explained and classified into three different categories: nucleic acid-based, protein-based, and point of care (PoC) detection testing. Then, the current issues for diagnostic procedures in laboratories are discussed. Finally, the role of new technologies in countering COVID-19 is also introduced to assist researchers in the development of accurate and timely detection of coronaviruses. As coronavirus continues to affect human lives in a detrimental manner, the development of rapid and accurate virus detection methods could promote COVID-19 diagnosis accessible to both individuals and the mass population at patient care. In this regard, rRT-PCR and multiplex RT-PCR detection techniques hold promise.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".