Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario
Bibliographic record
Abstract
This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario's largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.
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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 | 0.001 | 0.168 |
| 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".