COVID-19 Fatality Rate: A 1% Chance of Dying
Bibliographic record
Abstract
The COVID-19 pandemic, caused by SARS-CoV-2, resulted in a large number of deaths worldwide. Based on global data from Japan, France, Germany, the UK, the US, and India, we calculated the % case-to-fatality ratio (CFR). We conducted a comparative study among global, European, American, and Asian countries. The fatality rates for 2020, 2022, 2023, and 2025 were compared. The selected countries also partially illustrate the pandemic’s impact on both rich and poor nations. Japan had the lowest fatality rate (0.2%) followed by France and Germany (0.4%). The fatality rate in the UK was the same as that in the global tally (0.9%). Both the US and India have mortality ratios in the range of 1.1% to 1.2%. Excluding Japan, Germany, and France, the fatality rate is around 1.0%, reported globally, in the UK, the US, and India. For the three years of the pandemic (2020–2023), the percentage of relative risk reduction (RRR%), representing the fatality rate reduction, was in the following order: India had the lowest reduction in death risk (29.81%), whereas Japan had the highest reduction (96.80%). The other countries listed in order of relative risk reduction are France (95.93%), Global (92.88%), Germany (90.75%), the UK (86.11%), and the US (65.04%). This study examines all three major waves of the pandemic that have occurred globally through 2025. In India, three main variants appeared in sequence: Wuhan from May 2020 to January 2021, Delta from March 2021 to October 2021, and Omicron from January 2022 to March 2022. The peaks of these waves were observed in mid-September 2020, the first week in May 2021, and the last week in January 2022. The major waves in the US ended in March 2022. The study covered all major waves worldwide and in the aforementioned countries.
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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 | 0.060 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".