Factors Explaining Japan’s Low COVID-19 Mortality: Comparison with Rich and Democratic Countries
Bibliographic record
Abstract
The novel coronavirus (SARS-CoV-2) caused a large number of deaths during the COVID-19 pandemic. The pandemic had a greater impact on wealthy and developed countries. Considering the per capita deaths and fatality-to-case ratio, also known as the case fatality ratio (CFR), Japan was among the least affected countries. The CFR of Japan was compared with nine other democratic and wealthy countries: the US, Italy, Spain, France, Austria, Germany, Canada, Australia, and South Korea. Japan’s CFR was the second lowest at 0.2%, only behind South Korea with 0.1%. The highest rates were recorded by the US and Canada, each at 1.1%. The per capita (per 100,000 people) fatality rate of Japan was 57.72 deaths, whereas the US had six times more deaths compared to Japan. We calculated the mortality (fatality) rates based on the cumulative deaths as of March 16, 2023, when the pandemic was mostly over. The amount of GDP spent on healthcare in Japan, mask awareness, the stringency index (SI), vaccinations, urbanization, life expectancy (LE), and the age cohorts of the population were examined to determine the factors that resulted in a low mortality rate in Japan during the pandemic.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".