Nonpharmaceutical interventions reduce the incidence and mortality of COVID‐19: A study based on the survey from the International COVID‐19 Research Network (ICRN)
Bibliographic record
Abstract
The recently emerged novel coronavirus, "severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)," caused a highly contagious disease called coronavirus disease 2019 (COVID-19). It has severely damaged the world's most developed countries and has turned into a major threat for low- and middle-income countries. Since its emergence in late 2019, medical interventions have been substantial, and most countries relied on public health measures collectively known as nonpharmaceutical interventions (NPIs). We aimed to centralize the accumulative knowledge of NPIs against COVID-19 for each country under one worldwide consortium. International COVID-19 Research Network collaborators developed a cross-sectional online survey to assess the implications of NPIs and sanitary supply on the incidence and mortality of COVID-19. The survey was conducted between January 1 and February 1, 2021, and participants from 92 countries/territories completed it. The association between NPIs, sanitation supplies, and incidence and mortality were examined by multivariate regression, with the log-transformed value of population as an offset value. The majority of countries/territories applied several preventive strategies, including social distancing (100.0%), quarantine (100.0%), isolation (98.9%), and school closure (97.8%). Individual-level preventive measures such as personal hygiene (100.0%) and wearing facial masks (94.6% at hospitals; 93.5% at mass transportation; 91.3% in mass gathering facilities) were also frequently applied. Quarantine at a designated place was negatively associated with incidence and mortality compared to home quarantine. Isolation at a designated place was also associated with reduced mortality compared to home isolation. Recommendations to use sanitizer for personal hygiene reduced incidence compared to the recommendation to use soap. Deprivation of masks was associated with increased incidence. Higher incidence and mortality were found in countries/territories with higher economic levels. Mask deprivation was pervasive regardless of economic level. NPIs against COVID-19 such as using sanitizer, quarantine, and isolation can decrease the incidence and mortality of COVID-19.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".