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Record W7117660016 · doi:10.1142/s1793524525501785

A non-autonomous dynamic model-based study on influencing factors of seasonal brucellosis transmission and control strategies in Zhejiang Province, P. R. China

2025· article· en· W7117660016 on OpenAlex
Juan Li, Wei Jiang, Huaiping Zhu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Biomathematics · 2025
Typearticle
Languageen
FieldVeterinary
TopicBrucella: diagnosis, epidemiology, treatment
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsBrucellosisTransmission (telecommunications)Basic reproduction numberLivestockChinaLagIncidence (geometry)Epidemiology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.349
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it