The effect of meteorological factors on severe fever with thrombocytopenia syndrome: Evidence from 34 Chinese cities
Bibliographic record
Abstract
Background: Severe fever with thrombocytopenia syndrome (SFTS) is a climate-sensitive infectious disease, and its spatial distribution has been expanding in recent years. This study aimed to investigate the influence of meteorological factors on SFTS incidence. Methods: Data on SFTS was extracted from the Infectious Disease Surveillance Report Management System from January 1, 2011 to December 31, 2023. A two-stage hierarchical analytical framework was employed in this study. First, a distributed lag nonlinear model was utilized to characterize the nonlinear exposure-response relationships between meteorological factors and the incidence of SFTS at the municipal level. Second, a multivariate meta-analysis was conducted to synthesize city-specific effect estimates, with explicit adjustment for inter-regional heterogeneity. Results: : 0.10-0.80). Conclusions: This study demonstrates that temperature and precipitation significantly influence SFTS incidence, with effects lagging consistently by 1-2 months. These findings can be integrated into China's Smart Multi-Point Surveillance System by incorporating region-specific meteorological thresholds to trigger early warnings. The system could then activate targeted interventions, such as tick control measures, accounting for the observed 1-2 month lag between climatic conditions and disease occurrence. Such climate-adaptive approaches would enhance the precision and timeliness of SFTS prevention and control efforts nationwide.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".