Country-level determinants of the severity of the first global wave of the COVID-19 pandemic: an ecological study
Bibliographic record
Abstract
OBJECTIVE: We aimed to identify the country-level determinants of the severity of the first wave of the COVID-19 pandemic. DESIGN: Ecological study of publicly available data. Countries reporting >25 COVID-19 related deaths until 8 June 2020 were included. The outcome was log mean mortality rate from COVID-19, an estimate of the country-level daily increase in reported deaths during the ascending phase of the epidemic curve. Potential determinants assessed were most recently published demographic parameters (population and population density, percentage population living in urban areas, population >65 years, average body mass index and smoking prevalence); economic parameters (gross domestic product per capita); environmental parameters (pollution levels and mean temperature (January-May); comorbidities (prevalence of diabetes, hypertension and cancer); health system parameters (WHO Health Index and hospital beds per 10 000 population); international arrivals; the stringency index, as a measure of country-level response to COVID-19; BCG vaccination coverage; UV radiation exposure; and testing capacity. Multivariable linear regression was used to analyse the data. PRIMARY OUTCOME: Country-level mean mortality rate: the mean slope of the COVID-19 mortality curve during its ascending phase. PARTICIPANTS: Thirty-seven countries were included: Algeria, Argentina, Austria, Belgium, Brazil, Canada, Chile, Colombia, the Dominican Republic, Ecuador, Egypt, Finland, France, Germany, Hungary, India, Indonesia, Ireland, Italy, Japan, Mexico, the Netherlands, Peru, the Philippines, Poland, Portugal, Romania, the Russian Federation, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, Ukraine, the UK and the USA. RESULTS: Of all country-level determinants included in the multivariable model, total number of international arrivals (beta 0.033 (95% CI 0.012 to 0.054)) and BCG vaccination coverage (-0.018 (95% CI -0.034 to -0.002)), were significantly associated with the natural logarithm of the mean death rate. CONCLUSIONS: International travel was directly associated with the mortality slope and thus potentially the spread of COVID-19. Very early restrictions on international travel should be considered to control COVID-19 outbreaks and prevent related deaths.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| 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".