Variation Among Public Health Interventions in Initial Efforts to Prevent and Control the Spread of COVID-19 in the 50 States, 29 Big Cities, and the District of Columbia
Bibliographic record
Abstract
US states and big cities acted to protect the residents of their jurisdictions from the threat of SARS-CoV-2 infection and reduce COVID-19 transmission. As there were no known pharmacologic interventions to prevent COVID-19 at the outset of the pandemic, public health and elected leaders implemented a host of nonpharmaceutical interventions (NPIs) to slow the spread of the virus. This article discusses variation among states and cities in their implementation of 3 NPIs: stay-at-home/shelter-in-place orders, gathering restrictions, and mask mandates. We illustrate how frequently each was used by states and big cities, discuss state and local authorities to implement such interventions, and consider how these NPIs and accompanying public adherence to public health orders may vary considerably in different regions of the country and by local and state laws specific to state preemption of public health authority.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".