Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach
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Bibliographic record
Abstract
The novel coronavirus (SARS-Cov-2) has raged in mainland China for nearly three months resulting in a huge threat to people’s health and economic development. According to the cumulative numbers of confirmed cases and deathes of SARS-COV-2 infection announced by the National Health Commission of China, we divided the human population into four subgroups including the adolescents group (0–19 yr old), the youth group (20–49 yr old), the middle-aged group (50–74 yr old) and the elderly group (over 75 yr old), and proposed a discrete age-structured SEIHRQ SARS-COV-2 transmission model. We utilized contact matrixes to describe the contact heterogeneities and correlations among different age groups. Adopting the Markov chain Monte Carlo (MCMC) algorithm, we identified the parameters of the model and fitted the confirmed cases from January 24th to March 31st. Through a more in-depth study, we showed that before January 28th (95% CI [Feb. 25th, Feb. 31st]), the effective reproduction number was greater than 1 and after that day its value was less than 1. Moreover, we estimated that the peak values of infection were 66 (95% CI [65,67]) for the adolescents, 3996 (95% CI [3957,4036]) for the young group, 14714 (95% CI [14692,14735]) for middle-aged group and 297 (95% CI [295,300]) for elderly people, respectively; the proportions of the final sizes of SARS-COV-2 infection accounted for less than 90% for each group. We found that under the current restricted control strategies, the most severe and high-risk group was middle-aged people aged between 50–74 yr old; without any prevention, the most severe and high-risk group had become the young adults aged 20–49 yr old.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it