Additional file 1 of Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic
Bibliographic record
Abstract
Additional file 1: Table S1. Meanings and initial values of parameters in the age-structured SEIR model. Table S2. Outcomes, parameters and their values used in sensitivity analysis. Figure S1. Patterns of parameters (p_E, p_NAAT, and p_t) variation with date. A: Proportion of untraced exposed persons (p_E). B: Proportion of cases found by NAAT/RAT (p_NAAT). C: Proportion of concentrated isolation among positive NAAT cases (p_t). Figure S2. Values and regularity of p_E (A) and p_NAAT (B) used in sensitivity analysis. Figure S3. Cumulative number of cases in different districts of Shanghai. Figure S4. The potential areas of virus had been spread on different date of the epidemics in Shanghai (A) and Wuhan (B) with different mean transmission distance (namely the radius of each circle: 13.4 km in Shanghai and 4.4 km in Wuhan). The cumulative number of cases was same for each column. Figure S5. Estimated contract matrices at different settings over seven periods in Shanghai. Figure S6. Demographic comparison of population (A), cases (B) and standard incidence (C) for Shanghai and Wuhan. Figure S7. Effective reproductive number (Rt) in the epidemic of Shanghai. Rt was estimated from4-day and 7-day moving average respectively. Figure S8. Epidemic curve of cumulative predicted cases and reported cases estimate by the model with different values of parameters. Figure S9. The proportion of unascertained cases under different proportion of asymptomatic cases (A), proportion of untraced cases (B) and proportion of cases detected by NAAT/RAT (C). Figure S10. Comparation of estimated secondary attack rates (SAR) and estimated reported SAR (SARobs). Blue and red dashed line is overall SAR (24.1%) and overall SARobs (17.4%).
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.784 | 0.091 |
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".