How effective are "stay-at-home” (SAH) policies? To guide policy-decision making in response to the pandemic
Bibliographic record
Abstract
The COVID-19 pandemic had its genesis in Wuhan, China, and its first case outside of China in Thailand before spreading around the globe including Canada, which reported its first case on January 25, 2020. This pandemic has transformed the world and has resulted in an array of restrictions that force or encourage people to stay at home (SAH) in order to curb the growth in cases and deaths, thereby easing the burden on health systems. Many terms have been used to describe these measures, but here they all are consolidated under one term, “SAH policies”. While countries have imposed these intuitively effective measures, there is currently a paucity of evidence to support their use. Consequently, this proposal addresses this knowledge gap. The proposed study brings together a team from China, Thailand and Canada to assess in each country: (1) the regional effectiveness of SAH policies on daily cases and daily deaths; and (2) the relationship between the duration of SAH policies and their effectiveness. Publicly available data at the population-level on health effects, e.g. daily new confirmed cases and deaths, SAH policies, e.g. the timing and duration of SAH policies, and other covariates will allow for modelling at the provincial (or special administrative area/region) level for each country. Because the local context is expected to play a major role in the determination of the effectiveness of SAH policies, as each region varies in terms of its population, health system and environmental characteristics, nuanced policy recommendations will flow. Several dissemination strategies are planned to enhance evidence-based policy. Team members will champion the findings through their respective organizations and networks of influence to enhance uptake.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.356 |
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; both teacher heads agree on what is shown here.
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".