Use of Masks as a Preventative Public Health Strategy to Limit the Spread of COVID-19
Bibliographic record
Abstract
SARS-CoV-2 coronavirus (COVID-19) is a respiratory infectious disease that has caused a global pandemic of unprecedented proportions. There has been a lot of discussion and debate in social media and by public health experts about the effectiveness of masks as a preventative strategy to decrease transmission of this virus. There are two modes in which mask may be beneficial: i) To serve as a physical barrier against the virus entering or leaving the oral-nasal passages of mask wearers, and ii) to decrease the risk that the person wearing the mask might pass the virus on to someone else (e.g., via coughing). The focus of this review is on the efficacy of different masks-types, and their demonstrated effectiveness in mitigating transmission from a global perspective. Our findings reveal that the use of commercially manufactured mask greatly decreases the distribution of COVID-19, whereas single layer homemade masks also provide protection by decreasing the viral dose of exposure and limit outward aerosol particle emissions. We argue that masks are a critical component in the arsenal of public health strategies to decrease transmission of viruses, including handwashing, maintaining social distancing (2 meters), limiting large gatherings of people, isolation of suspected cases, screening, and contact tracing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".