Social Distancing Causally Impacts the Spread of SARS-CoV-2: A U.S. Nationwide Event Study
Bibliographic record
Abstract
Abstract Objectives To assess the causal impact of a spontaneous relaxation of social distancing on the spread of SARS-CoV-2 in the United States (U.S.), while controlling for social mobility and state-imposed social distancing restrictions. Design Event study. Setting Quasi-experimental setting created by the U.S. nationwide protests precipitated by George Floyd’s tragic death on May 25, 2020. Population Individuals in 3,142 U.S. counties from all 50 states and the District of Columbia. Main Outcome Measures The number of daily confirmed COVID-19 cases in all U.S. counties between the period of January 22, 2020, and June 20, 2020, and the cumulative change in COVID-19 cases in protest counties relative to non-protest counties following the onset of the protests. Results We document a country-wide increase of over 3·06 cases per day, per 100,000 population, following the onset of the protests (95%CI: 2·47–3·65), and a further increase of 1·73 cases per day, per 100,000 population, in the counties in which the protests took place (95%CI: 0·59–2·87). Relative to the week preceding the onset of the protests, this represents a 61·2% country-wide increase in COVID-19 cases, and a further 34·6% increase in the protest counties. Conclusions Our study documents a significant increase in COVID-19 case counts in counties that experienced a protest, and we conclude that social distancing practices causally impact the spread of SARS-CoV-2. The observed effect cannot be explained by changes in social distancing restrictions and social mobility, and placebo tests rule out the possibility that this finding is attributable to chance. Our research informs policy makers and provides insights regarding the usefulness of social distancing as an intervention to minimize the spread of SARS-CoV-2.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".