Comparison of Epidemiological Variations in COVID-19 Patients Inside and Outside of China—A Meta-Analysis
Bibliographic record
Abstract
The objective of this study is to compare the epidemiological variations in COVID-19 patients reported in studies from inside and outside of China. We selected COVID-19 observational studies from eight countries, including, China, Italy, Australia, Canada, Korea, Taiwan, Singapore, and the USA, comprising a total of 13 studies and performed a meta-analysis for age, gender, fatality rate, and clinical symptoms of fever, cough, shortness of breath, and diarrhea. The meta-analysis shows that there are differences in symptoms and other characteristics reported by the patients of COVID-19 inside and outside China. Patients in China have a higher proportion of fever, cough, and shortness of breath as compared to patients outside of China. However, we found the opposite results for the gastrointestinal symptoms such as Diarrhea. Patients outside of China have a significantly higher proportion of Diarrhea as compared to patients within China. We also observed gender disparity among our studies, with the male population being more susceptible than the female population. Moreover, the analysis suggests that the fatality rate in China is relatively lower as compared to the fatality rate in other countries. These findings also suggest that the clinical symptoms of COVID-19 should not be generalized to fever, shortness of breath, and cough only but other symptoms such as diarrhea are also prevalent in patients with COVID-19.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".