Analysis of Symptom Spectra and Associated Factors Among 536 Respondents During the COVID-19 Epidemic in China: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: This study aims to identify common COVID-19 symptoms and asymptomatic infection rates during the epidemic in China. We also introduce the concepts of “Time-point asymptomatic rate” and “Period asymptomatic rate”. Object and Methods: A questionnaire survey was conducted online from December 2022 to January 5, 2023, collecting demographic characteristics, laboratory results, clinical symptoms, lifestyle and vaccination history. Statistical methods were used to analyze symptom characteristics, associated factors, and patterns during an 8-day observation period. Numerical variables were described by median M (Q1-Q3) or mean and standard deviation (). Categorical variables are described by frequency (N), ratio (%) or rate (%). The influencing factors were studied by Wilcoxon or Kruskal-Willis H rank sum test or logistic regression analysis, and the trend of symptom incidence by Spearman rank correlation. P value being ≤ 0.05 was statistically significant. Results: Out of 536 participants, 493 (91.98%) were infected, with 3 asymptomatic cases and 490 symptomatic cases within 8 days. The time-point asymptomatic rate increased from 0.61% on day 1 to 15.42% on day 8. Fever, cough, and fatigue were the main symptoms, with additional symptoms such as vomiting, diarrhea, and hyposmia reported. Symptom durations varied, with cough and expectoration lasting longer and vomiting and diarrhea lasting shorter. Several symptoms showed a downward trend over time. Conclusion: Our online survey highlighted that most COVID-19 patients experienced symptoms, and the time-point asymptomatic rate showed a dynamic change among the infected population. Onset patterns and demographic factors influence symptom occurrence and duration. These findings have implications for clinical practitioners and decision-makers in public health measures and strategies. Keywords: Novel coronavirus infection, onset patterns, clinical features, associated factors, time-point asymptomatic, period asymptomatic rate
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".