The impact of mask-wearing and shelter-in-place on COVID-19 outbreaks in the United States
Bibliographic record
Abstract
OBJECTIVES: A hasty reopening has led to a resurgence of the novel coronavirus disease 2019 (COVID-19) in the United States (US). We aimed to quantify the impact of several public health measures including non-medical mask-wearing, shelter-in-place, and detection of silent infections to help inform COVID-19 mitigation strategies. METHODS: We extended a previously established agent-based disease transmission model and parameterized it with estimates of COVID-19 characteristics and US population demographics. We implemented non-medical mask-wearing, shelter-in-place, and case isolation as control measures, and quantified their impact on reducing the attack rate and adverse clinical outcomes. RESULTS: We found that non-medical mask-wearing by 75% of the population reduced infections, hospitalizations, and deaths by 37.7% (interquartile range (IQR): 36.1-39.4%), 44.2% (IQR: 42.9-45.8%), and 47.2% (IQR: 45.5-48.7%), respectively, in the absence of a shelter-in-place strategy. Sheltering individuals aged 50 to 64 years of age was the most efficient strategy, decreasing attack rate, hospitalizations, and deaths by over 82% when combined with mask-wearing. Outbreak control was achieved in the simulated scenarios and the attack rate was reduced to below 1% when at least 33% of silent pre-symptomatic and asymptomatic infections were identified and isolated. CONCLUSIONS: Mask-wearing, even with the use of non-medical masks, has a substantial impact on outbreak control. A judicious implementation of shelter-in-place strategies remains an important public health intervention amid ongoing outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".