Country differences in transmissibility, age distribution and case-fatality of SARS-CoV-2: a global ecological analysis
Bibliographic record
Abstract
Objectives The first COVID-19 pandemic waves in many low-income countries appeared milder than initially forecasted. We conducted a country-level ecological study to describe patterns in key SARS-CoV-2 outcomes by country and region and explore associations with potential explanatory factors, including population age structure and prior exposure to endemic parasitic infections. Methods We collected publicly available data and compared them using standardisation techniques. We then explored the association between exposures and outcomes using random forest and linear regression. We adjusted for potential confounders and plausible effect modifications. Results While mean time-varying reproduction number was highest in the European and Americas regions, median age of death was lower in the Africa region, with a broadly similar case-fatality ratio. Population age was strongly associated with mean (β=0.01, 95% CI, 0.005, 0.011) and median age of cases (β=-0.40, 95% CI, -0.53, -0.26) and deaths (β= 0.40, 95% CI, 0.17, 0.62). Conclusions Population age seems an important country-level factor explaining both transmissibility and age distribution of observed cases and deaths. Endemic infections seem unlikely, from this analysis, to be key drivers of the variation in observed epidemic trends. Our study was limited by the availability of outcome data and its causally uncertain ecological design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".