Global ecological analysis of COVID-19 mortality and comparison between “the East” and “the West”
Bibliographic record
Abstract
Although SARS-CoV-2 was first reported in China and neighbouring countries, the pandemic quickly spread around the globe. This paper explores national drivers of the pandemic and the radically different epidemiology and response in the West and in the East. We studied coronavirus disease (COVID-19) mortality until 31st December 2020, using an ecological study design, considering baseline characteristics and responses that might account for the uneven impact of the pandemic. A multivariable regression model was developed to explore key determinants. Key variables in the West were contrasted with those in the East, and speed of response was examined. Worldwide, 2.24 million COVID-19 deaths were documented in 2020. Western countries reported a median mortality 114 times that of the East (684 vs. 6.0 per million). Significant correlates of mortality in countries with at least 1 million population were median age, obesity prevalence, and democracy index; political stability and experience of SARS in 2002-2003 were protective; health system variables and income inequality were not associated. Outputs of the model were consistent when adjusted for stringency index, timeliness of stay-at-home requirements, and geographical autocorrelation. The West experiences a much higher COVID-19 mortality than the East. Despite structural advantages in the West, delays in national responses early on resulted in a loss of control over the spread of SARS-CoV-2. Although the early success of the East was sustained in the second half of 2020, the region remains extremely vulnerable to COVID-19 until enough people are immunized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".