Impact of climate and public health interventions on the COVID-19 pandemic: a prospective cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>BACKGROUND:</h3> It is unclear whether seasonal changes, school closures or other public health interventions will result in a slowdown of the current coronavirus disease 2019 (COVID-19) pandemic. We aimed to determine whether epidemic growth is globally associated with climate or public health interventions intended to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). <h3>METHODS:</h3> We performed a prospective cohort study of all 144 geopolitical areas worldwide (375 609 cases) with at least 10 COVID-19 cases and local transmission by Mar. 20, 2020, excluding China, South Korea, Iran and Italy. Using weighted random-effects regression, we determined the association between epidemic growth (expressed as ratios of rate ratios [RRR] comparing cumulative counts of COVID-19 cases on Mar. 27, 2020, with cumulative counts on Mar. 20, 2020) and latitude, temperature, humidity, school closures, restrictions of mass gatherings, and measures of social distancing during an exposure period 14 days previously (Mar. 7 to 13, 2020). <h3>RESULTS:</h3> In univariate analyses, there were no associations of epidemic growth with latitude and temperature, but weak negative associations with relative humidity (RRR per 10% 0.91, 95% confidence interval [CI] 0.85–0.96) and absolute humidity (RRR per 5 g/m<sup>3</sup> 0.92, 95% CI 0.85–0.99). Strong associations were found for restrictions of mass gatherings (RRR 0.65, 95% CI 0.53–0.79), school closures (RRR 0.63, 95% CI 0.52–0.78) and measures of social distancing (RRR 0.62, 95% CI 0.45–0.85). In a multivariable model, there was a strong association with the number of implemented public health interventions (<i>p</i> for trend = 0.001), whereas the association with absolute humidity was no longer significant. <h3>INTERPRETATION:</h3> Epidemic growth of COVID-19 was not associated with latitude and temperature, but may be associated weakly with relative or absolute humidity. Conversely, public health interventions were strongly associated with reduced epidemic growth.
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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 | 0.009 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it