Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out
Bibliographic record
Abstract
BACKGROUND: Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out. METHODS: We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains. RESULTS: Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth. CONCLUSIONS: The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".