A non-autonomous dynamic model-based study on influencing factors of seasonal brucellosis transmission and control strategies in Zhejiang Province, P. R. China
Bibliographic record
Abstract
Brucellosis incidence in China has risen steadily, posing severe prevention and control challenges. Given the national “north decline, south rise” epidemiological pattern, southern epidemic dynamics are increasingly critical and require in-depth study. Focusing on Zhejiang Province, this study constructed a non-autonomous periodic dynamic model of the environment–sheep–mutton–human transmission chain, aiming to clarify regional brucellosis periodic epidemic patterns, identify core risk factors and optimize control strategies. Theoretically, we explored the model’s global dynamical behaviors (disease extinction, uniform persistence, disease-free periodic solution and endemic periodic solution) and defined the effective reproduction number as a quantitative metric. Integrating 2020–2025 multi-source quarterly data, including human brucellosis cases, livestock industry statistics and meteorological data in Zhejiang, numerical analysis revealed significant periodicity in local brucellosis. Sheep increment is the core risk factor, with susceptible-infectious sheep/contaminated mutton contact as secondary drivers. Surveillance gaps in breeding, consumption and disinfection links may trigger large-scale epidemics. Lag correlation analysis confirmed statistically significant correlations between human brucellosis cases and the following variables: lag 0 (sheep slaughter, temperature, self-produced mutton, permanent population); lag −1 (sheep increment, infected mutton); lag 1 (environmental pathogen load, sheep cases). This study demonstrates that strengthening routine testing throughout the entire circulation chain of sheep and mutton, enhancing disinfection measures and improving public protection awareness are crucial for reducing the regional incidence of brucellosis. The research provides theoretical support and practical basis for brucellosis prevention and control in southern China.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".