Statistical Analyses of the Public Health and Economic Performance of Nordic Countries in Response to the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Aim To compare trends and undertake statistical analyses of differences in public health performance (confirmed cases and fatalities) of Nordic countries; Denmark, Finland, Norway and Sweden, and New Zealand, in response to the COVID-19 pandemic. Methods Per capita trends in total cases and per capita fatalities were analysed and difference-in-difference statistical tests undertaken to assess whether differences in stringency of mandated social distancing (SD) measures, testing rates and border closures explain cross-country differences. Results Sweden is a statistical outlier, relative to its Nordic neighbours, for both per capita cases and per capita fatalities associated with COVID-19 but not in terms of the reduction in economic growth. Sweden’s public health differences, compared to its Nordic neigbours, are partially explained by differences in terms of international border closures and the level of stringency of SD measures (including testing) implemented from early March to June 2020. Conclusions We find that: one, early imposition of full international travel restrictions combined with high levels of government-mandated stringency of SD reduced the per capita cases and per capita fatalities associated with COVID-19 in 2020 in the selected countries and, two, in Nordic countries, less stringent government-mandated SD is not associated with higher quarterly economic growth.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".