The mobility gap: estimating mobility levels required to control Canada’s winter COVID-19 surge
Bibliographic record
Abstract
Abstract Background Non-pharmaceutical interventions remain a primary means of suppressing COVID-19 until vaccination coverage is sufficient to achieve herd immunity. We used anonymized smartphone mobility measures in seven Canadian provinces to quantify the mobility level needed to suppress COVID-19 (mobility threshold ), and the difference relative to current mobility levels (mobility gap ). Methods We conducted a longitudinal study of weekly COVID-19 incidence from March 15, 2020 to January 16, 2021, among provinces with 20 COVID-19 cases in at least 10 weeks. The outcome was weekly growth rate defined as the ratio of current cases compared to the previous week. We examined the effects of average time spent outside the home (non-residential mobility) in the prior three weeks using a lognormal regression model accounting for province, season, and mean temperature. We calculated the COVID-19 mobility threshold and gap. Results Across the 44-week study period, a total of 704,294 persons were infected with COVID-19. Non-residential mobility dropped rapidly in the spring and reached a median of 36% (IQR: 31,40) in April 2020. After adjustment, each 5% increase in non-residential mobility was associated with a 9% increase in the COVID-19 weekly growth rate (ratio=1.09, 95%CI: 1.07,1.12). The mobility gap increased through the fall months, which was associated with increasing case growth. Interpretation Mobility strongly and consistently predicts weekly case growth, and low levels of mobility are needed to control COVID-19 through winter 2021. Mobility measures from anonymized smartphone data can be used to guide the provincial and regional implementation and loosening of physical distancing measures.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".