Behavioral changes before lockdown, and decreased retail and recreation mobility during lockdown, contributed most to the successful control of the COVID-19 epidemic in 35 Western countries
Bibliographic record
Abstract
The COVID-19 pandemic has prompted a lockdown in many countries to control the exponential spread of the SARS-CoV-2 virus ( 1, 2 ). This resulted in curbing the epidemic by reducing the time-varying basic reproduction number ( R t ) to below one ( 3, 4 ). Governments are looking for evidence to balance the demand of their citizens to ease some of the restriction, against the fear of a second peak in infections. More details on the specific circumstances that promote exponential spread (i.e. R t > 1) and the measures that contributed most to a reduction in R t are needed. Here we show that in 33 of 35 Western countries (32 European, plus Israel, USA and Canada), R t fell to around or below one during lockdown (March – May 2020). One third of the effect happened already on average 6 days before the lockdown, with lockdown itself causing another major drop in transmission. Country-wide compulsory usage of masks was implemented only in Slovakia 10 days into lockdown, and on its own reduced transmission by half. During lockdown, decreased mobility in retail and recreation was an independent predictor of lower R t during lockdown, while changes in other types of mobility were not. These results are consistent with anecdotal evidence that large recreational gatherings are super-spreading events ( 5, 6 ), and may even suggest that infections during day-to-day contact at work are not sufficient to spark exponential growth. Our data suggest measures that will contribute to avoiding a second peak include a tight control on circumstances that facilitate massive spread such as large gatherings especially indoors, physical distancing, and mask use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".