Quantitative analysis and mathematic modeling of the global outbreak of COVID-19
Bibliographic record
Abstract
The coronavirus pandemic is the biggest in the past 100 years, affected over 200 countries and killed over 300 thousand people. To better understand the epidemics in different areas, the progress percentage was generated in this study by dividing everyday total confirmed case number by the up-to-date total case number, so data obtained from different countries and territories can be put together and compared directly regardless of the large difference in the magnitude of numbers. The global outbreak data were analyzed and categorized into 4 groups based on different epidemic curve stages. The grouping pattern suggests that the geographical position may not play a critical role in the progress of COVID-19 epidemic. In this report, we also used a mathematic model to predict the progress of COVID-19 outbreak in UK, USA and Canada in Group 3, providing valuable information for assessing the risk in these countries and the timing of reopening business.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".