EVALUATION OF THE TUBERCULOSIS TRANSMISSION OF DRUG-RESISTANT STRAINS IN MAINLAND CHINA
Bibliographic record
Abstract
In this paper, we propose a mathematical model to evaluate the transmission of Tuberculosis with drug-resistant strains and with drug-sensitive strains. Based on the epidemic data from Chinese Center for Disease Control and Prevention, we first estimate the values of parameters in the model. Then the basic reproduction number of TB infection in the mainland China is calculated to be [Formula: see text] Since the basic reproduction number is greater than [Formula: see text] Tuberculosis spreads as an endemic in mainland China. Through our investigations, the basic reproduction number associated with drug-sensitive strains is less than 1, but the number of individuals infected by drug-resistant strains will still increase quickly, thus the epidemic is not under control. Moreover, increasing the vaccination coverage rate for newborns is not always beneficial for controlling TB prevalence in China. Whether vaccination effect is positive or negative depends on the relapse rate from the recovered state to the infected state. In summary, improving sanitation conditions, introducing efficient measures to detect the disease, and keeping the public informed about how to lower the chance of being infected and the current epidemic situation are essential in slowing down or eliminating Tuberculosis transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".