Rapid epidemic expansion of Sars-Cov-2 Omicron BA.2 subvariant during China’s largest outbreaks
Bibliographic record
Abstract
Abstract A complete and accurate statistic panorama and analysis of cases contracted Sars-Cov-2 Omicron BA.2 subvariant are given under the conditions of strict mandatory quarantine and isolation and of high rate of full vaccination. Sars-Cov-2 is still new and human know little about either its direction of variation or its propagation laws. No country other than China has been able to disclose every infected case and to have the data of heavily intervened large outbreaks. Here my study reveals that the BA.2 subvariant can still spread very fast and wide in areas with strict “dynamic zero-Covid Policy” in China, that there exist in different cities as much as twenty-time big differences of morbidity rate unrelated to any of the influence factors known and that Omicron BA.2 subvariant is unpredictable of its virulence though its severe rate of confirmed cases is low. This analysis provides a first-hand solitary and valuable information for further research of similar epidemic in the future. It may bring new thoughts for correction of present epidemiologic theory and mathematic models. It may also give other countries time to be better prepared for the coming 6th wave driven by Omicron BA.2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".