Impact of a nighttime curfew on overnight mobility
Bibliographic record
Abstract
Abstract Background Among non-pharmaceutical interventions, individual movement restrictions have been among the most impactful methods for controlling COVID-19 case growth. While nighttime curfews to control COVID-19 case growth have been implemented in certain regions and cities, few studies have examined their impacts on mobility or COVID-19 incidence. In the second wave of COVID-19, Canada’s two largest and adjacent provinces implemented lockdown restrictions with (Quebec) and without (Ontario) a nighttime curfew, providing a natural experiment to study the association between curfews and mobility. Methods This study spanned from December 1, 2020 to January 23, 2021 and included the populations of Ontario (including Toronto) and Quebec (including Montreal). The intervention of interest was a nighttime curfew implemented across Quebec on January 9, 2021. Unadjusted and adjusted difference-in-differences models (DID) were used to measure the incremental impact of the curfew on nighttime mobility in Quebec as compared to Ontario. Results The implementation of the curfew was associated with an immediate reduction in nighttime mobility. The adjusted DID analysis indicated that Quebec experienced a 31% relative reduction in nighttime mobility (95%CI: -36% to -25%) compared to Ontario, and that Montreal experienced a 39% relative reduction compared to Toronto (95%CI: -43, -34). Discussion However, this natural experiment among two neighbouring provinces provides useful evidence that curfews lead to an immediate and substantial decrease nighttime mobility, particularly in these provinces’ largest urban areas hardest hit by COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 teacher head, 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".