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Record W3040563693 · doi:10.21203/rs.3.rs-1025454/v1

Social Distancing Causally Impacts the Spread of SARS-CoV-2: A U.S. Nationwide Event Study

2021· preprint· en· W3040563693 on OpenAlexaff
Louis Gagnon, Stephanie Gagnon, Jessica Lloyd

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocial distancePopulationCoronavirus disease 2019 (COVID-19)DistancingDemographic economicsDemographyDevelopment economicsPolitical scienceSocial psychologyPsychologyMedicineSociologyEconomicsDisease

Abstract

fetched live from OpenAlex

Abstract We assess the causal impact of a spontaneous relaxation of social distancing practices on the spread of SARS-CoV-2 in the U.S., while controlling for social mobility and state-imposed social distancing restrictions. Using the quasi-experimental setting created by the U.S. nationwide protests precipitated by George Floyd’s tragic death on May 25, 2020, 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 in-crease 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. Hence, 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 our 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.498
GPT teacher head0.582
Teacher spread0.084 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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