Effectiveness of SARS‐CoV‐2 testing strategies: A scoping review
Bibliographic record
Abstract
Abstract Introduction Rapid identification of severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) infections by testing potentially reduced coronavirus disease‐19 (COVID‐19) cases. Testing strategies varied across countries and during different stages of the pandemic. This scoping review aims to map the available evidence on the effectiveness of SARS‐CoV‐2 testing strategies for suspected cases and asymptomatic populations to inform the development of World Health Organization recommendations for SARS‐CoV‐2 testing strategies. Methods We followed the standard methods for scoping reviews. We searched Medline (OVID), EMBASE (Elsevier), and Europe PMC using a comprehensive search strategy. The search was conducted in January 2023 and covered the period from January 2020 to January 2023. Two review authors independently screened the titles and abstracts, and full texts. Data were extracted onto a pilot‐tested form by a review author and cross‐checked by another review author. We provided a descriptive report summarizing the extracted data around the outcomes and created an interactive map of the available evidence using the evidence for policy and practice mapper. Results We identified 34,550 citations from the databases. After the screening, we included 17 studies from 11 countries for data extraction. The study designs were randomized controlled trials ( n = 3), nonrandomized experimental studies ( n = 3), cohort studies ( n = 3), cross‐sectional studies ( n = 4), self‐controlled case series ( n = 1), and economic evaluations ( n = 3). Among the included studies, 14 used reverse transcription‐polymerase chain reaction and 10 studies used antigen‐detecting rapid diagnostic test. The settings of the studies were healthcare facilities ( n = 8), communities ( n = 4), schools, and workplaces ( n = 3). Included studies considered symptomatic and asymptomatic individuals, or both, or asymptomatic contacts. Most of the studies ( n = 14) reported the COVID‐19 positivity rate as the primary outcome. Other reported outcomes are the number of COVID‐19 cases ( n = 11), number of hospitalizations and deaths ( n = 3), and cost ( n = 3). Conclusion We identified evidence gaps in the effectiveness of SARS‐CoV‐2 testing strategies, particularly in specific settings such as schools and long‐term care facilities. This scoping review provides a foundation for further research, allowing researchers and stakeholders to focus on addressing the identified gaps.
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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 | 0.036 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".